DSV Report Series No. 20-008 Nina Bergdahl This thesis explores students’ academic engagement and disengagement in Blended Learning, and offers valuable insights into student Upper Secondary School Student engagement and disengagement when learning with digital technologies; for example learning designs that promote engagement, Engagement and Disengagement hindrances to engage in learning, the role of students' IT skills, and the relation beween engagement and disengagement in TEL and Upper Secondary School Student Engagement and Disengagement in Blended Learning grades. Nina Bergdahl is a certified teacher, holds a Master's degree in quality management and leadership, and is a doctoral candidate in Information Society. This thesis is a manifestation of her interest in Nina Bergdahl teaching and learning with digital technologies.
Nina Bergdahl, M.Sc., PhD candidate
ISBN 978-91-7911-146-5 ISSN 1101-8526
Department of Computer and Systems Sciences
Doctoral Thesis in Information Society at Stockholm University, Sweden 2020
Upper Secondary School Student Engagement and Disengagement in Blended Learning Nina Bergdahl Academic dissertation for the Degree of Doctor of Philosophy in Information Society at Stockholm University to be publicly defended on Monday 24 August 2020 at 13.00 in L70, NOD-huset, Borgarfjordsgatan 12.
Abstract The present research approaches Swedish upper secondary school students’ engagement and disengagement in Technology- enhanced Learning (TEL). To date, research on engagement in TEL have mainly focused on university-level students and have overlooked the dimension of disengagement. The aim of the thesis is to explore how to facilitate students’ academic engagement in TEL by considering both student engagement and disengagement when students learn with digital technologies. While a mixed-methods approach was adopted across all sub-studies, sub-studies I-III emphasised qualitative methods, and sub-studies IV-V were more quantitatively orientated. Results revealed that teachers’ orchestration of digital technologies for learning varied more between the individual teachers than the subjects taught, and that the orchestration of digital technologies and the design of learning activities covaried with the observed and self-reported levels of student engagement. A Design-Based Research intervention showed that teachers could orchestrate digital technologies and design learning activities that increase student engagement in TEL, but may find it challenging to sustain the practice without support. A student evaluation showed that only the students with the highest engagement levels reported interest as their reason to engage. Instead, the most common reasons to engage were related to the social dimension of engagement. Building on the results of this intervention, design principles that facilitate student engagement when designing for engagement in TEL were identified. After the intervention, the focus was expanded to include student disengagement along with engagement and The Learner-Engagement-Technology (LET) instrument was developed using interviews and theory. The LET instrument was tested and validated to reflect multi-dimensional aspects of upper secondary school student engagement and disengagement in TEL. The LET-instrument revealed that low-, average- and high-performance students engage and disengage differently in TEL; that students’ IT skills played a role for engagement in TEL, but are not sufficient to redeem disengagement and that a majority of students use digital technologies to escape when the lesson is perceived to be boring. The results also showed that indicators of disengagement in TEL do not have a natural opposite in the engagement scale; that is; disengagement in TEL is more than the mere absence of, or lower levels of, engagement in TEL. Overlooking disengagement, when students learn with technologies, might fail to uncover critical insights that hinder student engagement. The main contributions of this thesis are: (i) derived design principles and practical insights on conditions related to student engagement and disengagement in TEL that may inform designs of learning activities to facilitate engagement (ii) a methodological contribution that reflects an attempt to combine critical realism and Design-Based Research, and (iii) a theoretical contribution that suggests how engagement and disengagement may be understood and conceptualised in TEL. Future research should explore engagement and disengagement in TEL, relating to the uptake of digital technologies in earlier school years, and other school forms. The thesis is relevant for teachers, decision makers, researchers and others interested in understanding the challenges and possibilities that may affect students in a digitalised school.
Keywords: Upper secondary school, Student engagement, Student disengagement, Blended Learning, Technology- enhanced Learning, Mixed Methods.
Stockholm 2020 http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-180871
ISBN 978-91-7911-146-5 ISBN 978-91-7911-147-2 ISSN 1101-8526
Department of Computer and Systems Sciences
Stockholm University, 164 07 Kista
UPPER SECONDARY SCHOOL STUDENT ENGAGEMENT AND DISENGAGEMENT
Nina Bergdahl
Upper Secondary School Student Engagement and Disengagement in Blended Learning
Nina Bergdahl ©Nina Bergdahl, Stockholm University 2020
ISBN print 978-91-7911-146-5 ISBN PDF 978-91-7911-147-2 ISSN 1101-8526
Cover illustration “The learning landscape” (2018) by Elisabeth Bergdahl. Reproduced with courtesy of the artist.
Printed in Sweden by Universitetsservice US-AB, Stockholm 2020 List of Publications
Substudy I
Bergdahl, N., Fors, U., Hernwall, P., & Knutsson, O. (2018). The Use of Learning Technologies and Student Engagement in Learning Activities. Nordic Journal of Digital Literacy, 13(2), 113–130. http://doi.org/10.18261/ISSN.18919-943x-2018-02-04
Substudy II
Bergdahl, N., Knutsson, O., & Fors, U. (2018). Designing for Engagement in TEL – a Teacher Researcher Collaboration. Designs for Learning, 10(1), 100–111. DOI: http://doi.org/10.16993/dfl.113
Substudy III
Bergdahl, N., Knutsson, O., & Fors, U. (2018). ‘So, You Think It’s Good’ – Reasons Students Engage When Learning with Technologies – a Student Perspective. In 10th International Conference on Education and New Learning Technologies (pp. 9556– 9563). Palma, Spain: IATED Digital Library. http://doi.org/10.21125/edulearn.2018.2289
Substudy IV
Bergdahl, N., Nouri., J., Fors, U. (2019). Disengagement, Engagement and Digital Skills in Technology-Enhanced Learning. Education and Information Technologies, http://doi: 10.1007/s10639-019-09998-w
Substudy V
Bergdahl, N., Nouri., J., Fors, U., Knutsson, O. (2019). Engagement, Disengagement and Performance when Learning with Technologies in Upper Secondary School. Computers & Education. https://doi.org/10.1016/j.compedu.2019.103783
I Abstract
The present research approaches Swedish upper secondary school students’ engagement and disengagement in technology-enhanced learning (TEL). To date, research on engagement in TEL has mainly focused on university-level students and has overlooked the dimension of disengagement. The aim of this thesis is to explore how to facilitate students’ academic engagement in TEL by considering both student engagement and disengagement when students learn with digital technologies. While all substudies apply a mixed-methods approach, substudies I‒III emphasised qualitative methods (and include observations, interviews and Design-Based Research) and substudies IV-V which were more quantitatively orientated, approached a larger population (n=410), and used descriptive and inferential statistics to analyse engagement and dis-engagement in TEL. Results revealed that teachers’ orchestration of digital technologies for learning varied more between the teachers than the subjects taught, and that the orchestration of digital technologies and the design of learning activities co-varied with the observed and self-reported levels of student engagement. A design-based research intervention showed that teachers could orchestrate digital technologies and design learning activities that increase student engagement in TEL, but may find it challenging to sustain the practice without support. A student evaluation revealed that only students reporting the highest levels of engagement reported interest as the reason they engaged in learning. The most common reasons to engage were related to the social dimension of engagement. Building on the results of this intervention, design principles that facilitate student engagement when designing for engagement in TEL were identified. After the intervention, the focus was expanded to include student disengagement along with engagement and the Learner-Engagement-Technology (LET) instrument was developed using interviews and theory. The LET instrument was tested and validated to reflect multidimensional aspects of upper secondary school student engagement and disengagement in TEL. The instrument revealed that low-, average- and high- performance students engage and disengage differently; that a majority of students use digital technologies to escape when the lesson is perceived to be boring. The results also showed that indicators of disengagement in TEL do not have a natural opposite on the engagement scale; that is, disengagement in TEL is more than the mere absence of, or lower levels of, engagement in TEL. Overlooking disengagement when students learn with technologies might lead to a failure to uncover critical insights that hinder student engagement. The main contributions of this thesis are: (i) derived design principles and practical insights into conditions related to student engagement and disengagement in TEL ‒ these may inform orchestrations of digital technologies and designs of learning activities to support engagement; (ii) a methodological contribution in which critical realism was combined with design-based research; and (iii) a theoretical contribution that suggests how engagement and disengagement may be understood and conceptualised in TEL. Future research should explore engagement and disengagement in TEL, in earlier years and other school forms. The thesis is relevant for teachers, decision-makers, researchers and others interested in understanding challenges and possibilities that may affect students in a digitalised school.
II Sammanfattning
Avhandlingen bygger på teori och empiri relaterade till gymnasieelevers engagemang i lärande. Närmare bestämt undersöker den presenterade forskningen svenska gymnasieelevers engagemang och dis-engagemang i teknikstött lärande, (nedan TEL från engelskans Technology-enhanced Learning). Forskningen idag har främst fokuserat på universitetsstudenter, och bortsett från dis-engagemang. Syftet med denna avhandling är att undersöka hur man kan stötta elevers akademiska engagemang i TEL, genom att utforska hur både engagemang och dis-engagemang manifesteras när digitala teknologier används. Emedan alla del-studierna tillämpar mixade metoder fokuserar delstudierna I-III på kvalitativa aspekter (och inkluderar observationer, intervjuer och design-baserad forskning) medan delstudierna IV-V vänder sig emot en större population (n=410), och tillämpade deskriptiv och inferentiell statistik för att analysera engagemang och dis-engagemang i TEL. Resultaten visar att komplexiteten av lärares orkestrering av digitala teknologier varierar mycket, att variationen är större mellan individuella lärare än mellan de specifika undervisningsämnena och att orkestrering av digitala teknologier och design av lärande-aktiviteter samvarierar med elevers observerade och självskattade nivåer av engagemang. En design-baserad intervention visade att lärare kan orkestrera och designa läraktiviteter som ökar elevers engagemang i TEL, men kan uppleva det utmanande att upprätthålla praktiken utan stöd. En elevutvärdering visade att endast de elever som rapporterat de allra högsta nivåerna av engagemang, uppgav intresse som anledning till att engagera sig i lärande. Istället, var de vanligaste anledningarna att engagera sig i lärande relaterade till sociala aspekter av engagemang. Ur denna studie kunde design-principer som kan informera design av lärande-aktiviteter riktade mot att öka elevers engagemang i TEL identifieras. Efter interventionen utvecklades, testades och validerades ett instrument, LET, (efter engelskans Learner-Engagement-Technology), med syfte att reflektera multi- dimensionella aspekter av gymnasieelevers engagemang och dis-engagemang i TEL (delstudierna IV-V). Resultat från dessa studier visar att låg, medel- och högpresterande elevers engagemang och dis-engagemang skiljer sig åt i TEL, att en majoritet av eleverna idag använder digitala teknologier för att fly när en lektion upplevs som tråkig, och att de indikatorer som reflekterar dis-engagemang, tvärtom vad man kan tro, inte är motsatser till engagemang. Dis-engagemang är mer än bara låga nivåer av, eller brist på engagemang. Att bortse från disengagemang i forskning, kan innebära att man missar de faktorer som utgör hinder för elevers engagemang. De huvudsakliga bidragen i denna avhandling är (i) insikter kring villkor och designprinciper som relaterar till gymnasieelevers engagemang och disengagemang i TEL. Dessa kan informera orkestrering av digitala teknologier och design av lärande- aktiviteter med syfte att stötta elevers engagemang, (ii) ett metodologiskt bidrag i vilket kritisk realism kombinerats med design-baserad forskning och (iii) ett teoretiskt bidrag som föreslår hur engagemang och disengagemang kan förstås och konceptualiseras i TEL. Forskning som undersöker engagemang och disengagemang i TEL för yngre år och andra skolformer behövs. Avhandlingen är intressant för lärare, beslutsfattare, forskare och andra som söker förstå utmaningar och möjligheter som kan påverka elever i en digitaliserad skola.
III Acknowledgements
During the first interview, when Uno Fors sternly asked me, “What do you see yourself occupied with earning a PhD?” I remember replying with something like: “A lot of reading, writing and revising.” “Oh,” he said, “there will be a lot of that.” And that came true, as Uno took me on as his PhD candidate. Thank you for taking on main supervision of me as your PhD student despite your workload. I felt that you always made time for my queries, suggested ways forward and took a personal interest in the challenges I faced. I also thank my co-supervisor Ola Knutsson for his critical eyes and for inviting me along during countless lunches. A special thank you is directed to Jalal Nouri, who, when I needed it, tailored me an introduction course to statistics. Little did I know that there would be other collaborations, which I was to enjoy deeply. Thank you for offering your insights into statistics.
With that said, I also wish to direct my gratitude to the schools who welcomed this research project, the students and teachers who participated in the studies, and especially those students who included me in their everyday school life as I shadowed them throughout their week. Thank you.
I am grateful to my family, and especially Thea, Christoffer and Nenad, who made sure that I engaged in numerous walks, dinners, board games, weekend excursions and other social events. Without you, the thesis would have been finished sooner, but my life would have been less fulfilling. I am also grateful to my very dear friends: thank you Madelen Runsten, for taking me horseback riding, going on spa days and for your ‘can- do’ attitude; thank you Inez Hamilton and Petra Tibblin for sharing my journey and for your pearls of wisdom. Thank you Linda Söderström and Nadja Shaw for the encouraging phone calls, skiing trips and visits, which I found invigorating. I also extend deep gratitude to Hanna Zandin and Elisabeth Bergdahl, who always took the time to chat about small and large things, life in academia, and who provided much valuable feedback on my thoughts and writings.
Thank you to all colleagues at Interaction Design and Learning (IDEAL), and especially Prof. Robert Ramberg for your availability and authentic interest in my concerns of varying kinds; Tuija Darvishi for always having time, once again, to show me the administrative routines when filing for travel expenses; Elisabeth Rolf, for never missing my presentations. Thank you, Mohammed Saqr, for the blessed help with my hotel mix-up in Belgium. Thank you Annika Käck for your friendship and open- heartedness.
Last, but most importantly, I thank my late mother for her strength, love and energy. I want you all to know that your support, in various ways, has made all the difference.
Nina
IV About the author
I have had an interest in understanding and contributing to the quality of teaching, especially in combination with digital technologies, for a long time. When computers were introduced in primary schools (around the turn of the century), I was working as a teacher at a lower secondary school south of Stockholm. The school had just recruited a new principal, who came from one of the computer companies that had to file for bankruptcy during the big IT boom in the 90s (leaving CEOs and IT professionals to look for work elsewhere). With an interest in digital technologies for learning and the support of an IT-oriented principal, we worked intensely to find solutions to enable students to access digital technologies. During this time, no schools had a 1:1 arrangement, and we considered ourselves lucky to have reached a situation in which the accessibility increased so that two classes could book laptops for simultaneous use! The atmosphere was one in which we, a team of four or five people, worked intensely to use digital technologies in innovating ways, including multimodal expressions and collaborations.
Later some of these team members initiated the start-up of a larger annual conference, whose aim is to inspire teachers in their professional development in relation to the digitalisation of education. Moving on, I continued to be a part of the school’s IT development team, which was mostly organised by a few teachers with an outspoken IT interest. At that time, IT development in schools was often initiated, led and managed by individuals who had a burning desire. During those years I began giving workshops on IT use from a common didactics perspective and took part in discussions on the school’s development of digitalisation.
Against the background of these experiences, I wanted to expand my understanding of student engagement when learning with digital technologies, and thus initiated the process toward earning a doctoral degree.
V Thesis overview
This PhD thesis explores student engagement and disengagement in technology- enhanced learning and relates this understanding to relevant theories and student performance.
Overview of research questions
The aim of the research is to try and answer the overarching question: How can we facilitate students’ academic engagement in technology-enhanced learning by considering both student engagement and disengagement when students learn with digital technologies? To answer this question, I raised the following research questions:
RQ1) How can teachers facilitate upper secondary school student academic engagement when learning with digital technologies?
RQ2) How can we raise awareness of upper secondary school student disengagement when learning with technologies?
Table 1 presents an overview of the research questions as they are explored in the substudies, along with the substudies’ publication status.
Table 1. Substudies and research questions
Substudies and publication status Research Status Question (RQ)
I The Use of Learning Technologies and Student Engagement in RQ1 Published Learning Activities
II Designing for Engagement in TEL – a Teacher Researcher RQ1 Published Collaboration
III ‘So, You Think It’s Good’ – Reasons Students Engage When RQ1 Published Learning with Technologies – a Student Perspective
IV Disengagement, Engagement and Digital Skills in Technology- RQ1, RQ2 Published Enhanced Learning
V Engagement, Disengagement and Performance when Learning RQ1, RQ2 Published with Technologies in Upper Secondary School
VI Delimitation
This thesis has been conducted within the third-cycle subject area Information Society. It is from this perspective that the use of digital technologies is primarily explored. The substudies are influenced by theories from educational psychology to make meaning of engagement and disengagement in a technology-enhanced environment. Engagement may be linked to pedagogic theories, and its relation to sociocultural theory is brought up, but this is not at the forefront of the study. The present research was conducted within upper secondary schools in Stockholm, Sweden. More precisely, teachers working, and students enrolled, in programmes that prepare them for higher education were approached.
Disposition of the thesis
Chapter 1 introduces the thesis, offers a background to student engagement and disengagement in technology-enhanced learning and explicates the research problem.
Chapter 2 presents the theoretical background to the thesis and introduces engagement, disengagement theory and engagement in technology- enhanced learning.
Chapter 3 provides the philosophical and methodological underpinnings and ethical considerations.
Chapter 4 presents the main results of the substudies.
Chapter 5 discusses key findings and concludes the thesis, raises implications for practice, outlines limitations and presents the contributions.
Chapter 6 points to future research.
VII Terminology
Below is an overview and explanation of terminology used:
Cognitive enhancement
Variables of engagement include using digital technologies as ‘a cognitive enhancement’. Previous research has suggested that enhanced cognition is desirable in general (Heersmink, 2016), as “enhancement of our cognitive capacities increases their instrumental value by enabling us to solve more difficult problems” (Agar, 2013, p. 26). Here, cognitive enhancement refers, for example, to using devices for memory support when managing and working on school assignments and projects, e.g. planning, scheduling, keeping different versions available, keeping several different worksheets open, and using several devices to access numerous online resources, like programming codes, synonyms, translations, grade requirements and more (Heersmink, 2016).
Face-to-face learning, in analogue, traditional and physical classrooms
It has become common to refer to learning that takes place in physical classrooms as traditional learning, or F2F (face-to-face) learning (e.g. Agudo-Peregrina et al., 2014; Chen et al., 2010; Rubio et al., 2018). “Traditional”, here, suggests that physical classrooms has been a norm for a longer time, than for example blended learning (BL) (Nortvig et al., 2018). When learning is not supported by digital technologies, like when memorising, or writing things down using pen and paper, the term “analogue” has been used to mark an opposite to learning with digital technologies (Heersmink, 2016).
Digital technologies and learning technologies
While the substudies make use of both digital technologies and learning technologies, as terms, they essentially refer to “any digital technologies brought into school”, as one of many digital tools that a student can access in school, including devices they have brought along that they employ for learning, e.g. mobile phones. In this thesis, learning technologies refers to the technologies supplied by the school to mediate learning. This categorisation focuses on how technologies are put to use for learning (Goodyear & Retalis, 2010) learning content (i.e. repositories), and teacher (school)-student communication (e.g. email clients, chats, wikis, forums, et cetera). However, learning technologies may include both devices and software (i.e. applications, cloud services and a laptop). The teachers are free to employ other technologies for learning that originally might not have been intended for educational purposes (such as a robot or a 3D printer). When these were available in education, during observations, they were considered to be learning technologies. Depending on how they are used, mobile phones may or may not be considered a learning technology, but they remain a digital technology. Students are repeatedly seen using their mobile phones for a range of purposes. When referring to digital technologies for learning, this thesis includes the actual digital technologies used, not merely the ones provided by schools.
VIII
Gamers and gaming
The terms ‘gaming’ (verb) and ‘gamer’ (noun) refer to a person who uses a device (including mobile phones, computers, gaming consoles and the like) to access and play electronic games for entertainment, rather than educational purposes (Connolly et al., 2012).
IT (information technology) and ICT (information and communication technology)
These terms are used to describe digital technologies, hardware, software and cloud services. Though ICT is commonly seen in educational disciplines, where IT is common in the private sector, the two terms are often used interchangeably (e.g. Clayton et al., 2005; Comi et al., 2017). IT implies direct or indirect communication, and as such, the “C” in ICT provides little argument to separate the two terms. In substudies I–III, “ICT” was used, whereas in substudies IV‒V, “IT” was used.
LMS (learning management system) and VLE (virtual learning environment)
Both terms suggest a virtual space, but the focus is slightly different. An LMS (learning management system) suggests a teacher perspective, where teachers access and modify courses and materials. A VLE (virtual learning environment), on the other hand, suggests a virtual space dedicated to student access, which does not necessarily include teacher access. However, today, schools provide learning platforms which offer both possibilities (not exclusively), and in this sense, the terms are viewed as interchangeable. However, in contrast to an LMS, a VLE can also be a 3D virtual world as it is an online learning environment (Dobozy & Reynolds, 2010). In substudies I–V the terms are used interchangeably to reflect the use of a learning platform provided by the school.
Orchestration and design
Wellborn (in Reeve, 2012) defines a learning activity as a task- or context-bound event that is aimed toward learning. Teachers design learning activities (Goodyear & Retalis, 2010; Laurillard, 2012), but they do not commonly design digital technologies. Researchers have described orchestration as the process in which a teacher designs, implements, develops and evaluates learning activities, using digital technologies to maximise student learning and learning outcomes in a real-life setting (Prieto et al., 2012). Orchestration is a regulation process in which the teacher “manages, in real time, multi-layered activities in a multi-constraints context” (Dillenbourg, 2013, p. 485). A learning activity can be designed and orchestrated to try and make students more engaged (see also the introductory chapter).
IX Scaffolding and (peer) modelling
The central idea in scaffolding is that the student is supported in their knowledge acquisition (Wertsch, 1985). Scaffolding can be done using instructional prompts (but should not be mistaken for instructionism, which is decontextualised and offers segmented blocks of sequential learning) (Reiser & Tabak, 2016). When scaffolding is provided by peers, researchers refer to the mechanism as (peer) modelling (Laurillard, 2013). In this thesis, the term ‘scaffolding’ is used to address this structure and prompts that can be provided by the teacher or applications, and peer modelling, as provided by peers.
Smartphones and mobile phones
When mobile phones entered the market, there were still many differences between the functionalities offered between mobile phones and computers. Today, the gap between devices is shrinking, and many of the activities that could only be performed on stationary computers can today be done on laptops, tablets, other hand-held devices and mobile phones (Ott, 2017). In this thesis, mobile phones are smartphones, as most Swedish students, from lower secondary school and upwards, have a smart mobile phone that they use (Internetstiftelsen, 2016).
Synchronous, asynchronous and polysynchronous interaction
Today, interaction and dialogue are increasingly taking place online. Such interactions can be seen between people (student, peers, teachers), between the individual and the content, and between humans and machines. This thesis follows the common view of synchronous and asynchronous interactions (e.g. Bernard et al., 2009; Yang, 2011). Synchronous interactions are seen as those that take place at the same time, like a chat dialogue, or in F2F learning. Asynchronous interactions, on the other hand, have been used to mark the opposite of F2F interactions (Northey, Bucic, Chylinski, & Govind, 2015), and are seen as those where time lapses between interactions, for example on Facebook (ibid.). Lastly, following Dalgarno (2014), polysynchronous interaction is regarded as that which happens across multiple websites, platforms and devices, involving a number of individuals and/or machine interaction.
Technology-enhanced learning and technology-mediated learning
According to Graham (2006), TEL is a subset of blended learning (BL). BL reflects learning that combines F2F instruction with digital technologies. BL can be categorised into three groups, namely 1) enabling learning, 2) enhancing learning or 3) transforming learning, depending on the extent to which the technologies are allowed to alter the conditions for learning (Graham, 2006). On the other hand, the term ‘Technology- Mediated Learning (TML) can be used to encompass any learning mediated via digital technologies, regardless of the extent to which the technologies are allowed to alter the conditions for learning. For example, Henrie used both “computer-assisted instruction” and “online learning” and selected articles exploring engagement in learning through
X the use of clickers, learning in virtual worlds, learning management systems, recorded lectures, social media and software applications, to identify articles relevant for a review in technology-mediated learning, without specifying TML further (e.g. Henrie et al., 2015). Thus, it would seem that TML is very similar to TEL but may include transformed learning, or even distance learning. TEL still refers to learning when education primarily takes place in the classroom (Graham, 2006).
Unauthorised use of digital technologies
In this thesis, unauthorised use of digital technologies refers uses of digital technologies that are unintended by the teacher, and not directed toward learning (Lindroth, 2012; Tallvid et al., 2015).
XI
CONTENT OVERVIEW
Chapter 1. Introduction ...... 5 Blended Learning and Technology-Enhanced Learning ...... 5 A Nordic perspective on IT in schools ...... 12 The Research Problem ...... 13 Chapter 2. Theoretical Foundation ...... 17 Motivation and Self-regulation ...... 17 Engagement ...... 18 Disengagement ...... 20 Engagement and Disengagement in TEL ...... 27 A Sociocultural Perspective of Engagement ...... 29 Assumptions of Engagement...... 31 Chapter 3. Philosophy and Methodology ...... 33 Philosophical Underpinnings ...... 33 Epistemology and Ontology ...... 33 Methodology ...... 35 Research Design ...... 36 Validity and Reliability ...... 44 Ethical Considerations ...... 47 Chapter 4. Results...... 49 Substudy I ...... 49 Substudy II ...... 53 Substudy III ...... 57 Substudy IV ...... 60 Substudy V ...... 62 Chapter 5. Discussion, Conclusions and Contributions ...... 65 Discussion ...... 65 Limitations ...... 72 Implications for Future Practice ...... 73 Contributions ...... 75 Chapter 6. Future Research ...... 77 REFERENCES ...... 78 Copyrighted material ...... 94 APPENDICES ...... 95 APPENDIX A. Consent form substudies I‒III ...... 95 APPENDIX B. Consent form substudies IV and V ...... 96 APPENDIX C. Overview of the LET instrument indicators ...... 97
LIST OF TABLES
Table 1. Substudies and research questions VI Table 2. Future workshop overview 41 Table 3. General criteria for evaluating research 45 Table 4. The problem–solution matrix 54 Table 5. Regression analysis of engagement and disengagement 61
LIST OF FIGURES
Figure 1. Technologies as add-ons. 5 Figure 2. Blended learning environment. 6 Figure 3. Overview of digital technologies in substudies I‒V. 37 Figure 4. Students’ perception of what digitalisation means in their school. 38 Figure 5. The DBR process. 39 Figure 6. Data collection and analysis substudy 1. 40 Figure 7. Stages in exploratory sequential design. 43 Figure 8. Learning Activity A. Presentation. 50 Figure 9. Learning Activity B. Individual work. 51 Figure 10. Learning Activity C. Robot programming. 52 Figure 11. Learning Activity D. Design using the forum function of an LMS. 55 Figure 12. Learning Activity E. An assessment application is used. 56 Figure 13. Student-reported reasons to engage in learning activities. 58 Figure 14. Reasons to complete the assignment across reported engagement levels. 59
2 LIST OF ABBREVIATIONS
BL Blended Learning CA Content Analysis CR Critical Realism CS Computer Science DBR Design-Based Research DSV Department of Computer and Systems Sciences F2F Face-to-face GPA Grade Point Average ICT Information and Communication Technology IT Information Technology LET Learner-Engagement-Technology LMS Learning Management System LT Learning technology TA Thematic Analysis TEL Technology-Enhanced Learning VLE Virtual Learning Environment
3
4 Chapter 1. Introduction
Chapter 1 introduces the thesis, offers a background to student engagement and disengagement when learning with technologies and explicates the research problem. ______
This PhD thesis explores upper secondary school student engagement and disengagement in technology-enhanced learning (TEL) in relation to relevant theories. The research was part of the larger project “I use IT”, financed by the City of Stockholm.
Blended Learning and Technology-Enhanced Learning
Today, we live in a knowledge society. Governments invest in digital education as there is a belief that IT constitutes an essential foundation for learning and will improve learning outcomes (Chen & Jang, 2010; Giesbers et al., 2013), and that owning IT skills (e.g. Håkansson-Lindqvist, 2015; Skryabin et al., 2015) and programming skills (Williamson et al., 2018) is critical in modern society and will lead to rebooting Europe’s economy (European Commission, 2014). Before digital technologies were implemented in schools, students attended their lessons in learning environments that did not employ digital technologies (henceforth referred to as analogue learning environments).
Figure 1. Technologies as add-ons.
5 With the IT expansion in society at large, distance and online learning soon became available to the masses, but the digital learning environment was still something only associated with online or distance learning (see Figure 1).
As digital technologies entered the classrooms, teachers and students soon started having access to laptops, projectors and smart boards.1 The term “add-ons” has been used to point out that digital technologies were added to teaching practices, which had remained traditional, instead of disrupting and changing delivery and instruction (Downes & Bishop, 2012; Guðmundsdóttir et al., 2014). If teaching and learning mostly took place in an analogue learning environment, the ways in which technologies were used would have little impact, since the amount of use would be relatively insignificant in comparison to the overall time spent learning in a traditional way. Today, the digital expansion has affected education in ways yet unparalleled. The digitalisation of education includes laptops, Internet access and learning management systems (LMS), which today are commonplace in most schools in the Western world (Balanskat, Bannister, Hertz, Sigillò, & Vuorikari, 2013; Duval, Sharples, & Sutherland, 2017; Lonka, 2015). This emerging learning environment is commonly referred to as a “blended learning” (BL) environment, reflecting the combination of face-to-face (F2F) and online elements. BL has been defined as “thoughtful integration of classroom face- to-face learning experiences with online learning experiences” (Garrison & Kanuka, 2004).
Figure 2. Blended learning environment.
Figure 2 reflects a hypothetical overlap of the BL environment and the purely analogue classroom, raising the question of what is and what will be the “normal” way of teaching and learning (i.e. where we should place our focus to inform ourselves about teaching and learning).
1 In the Scandinavian countries, computers were first introduced (as teachers’ tools) in education during the 1970s (The Swedish Government, 2016). While initiatives have been ongoing since, the overall European escalation of digitalisation initiatives followed the USA’s “National Infrastructure Initiative” in 1993, which sought to widely increase the accessibility of digital technologies (Söderlund, 2000).
6 Some researchers take a more systematic approach and address the system ‒ “The blended learning system is one that combines face-to-face instruction with computer- mediated instruction with the aim of complementing each other” (Graham, 2006) ‒ and include the spatiotemporal flexibility of technology-mediated learning: “Taking the best from self-paced, instructor-led, distance and classroom delivery to achieve flexible, cost-effective training that can reach the widest audience geographically and in terms of learning styles and levels” (Marsh, 2001).
Graham categorises the quality of BL as either enabling, enhancing or transforming, depending on the extent to which the technologies are allowed to alter the conditions for learning (Graham, 2006). Enabling learning is described as technologies that enable students’ access to information and convenience. Enhancing learning is that which changes the conditions for learning by, for example, facilitating multiple simultaneous conversations. Finally, transforming learning would mean radical pedagogical transformation. We might imagine these as, for instance, personalisation of education in terms of pace, place and time, for instance through flexible study hours/flexible choices of courses across schools, or reduced in-school hours through technology-mediated delivery, instruction and participation, et cetera. According to Graham, classrooms in which students utilise laptops and learning platforms then fall under the second level of technology-enhanced learning (TEL).
However, the way we interpret the difference between enhancing and transforming may be different depending on what is expected and viewed as “normal”, and what is considered to be transforming in a given culture at a given time. These levels of acceptance and expectations are likely to change with new innovative technologies, alongside the access, uptake and integration of digital technologies in everyday life. The line between enhancing and transforming is not static; rather, it is a subjective cultural interpretation. While it might be easy to become enchanted by the new potentials innovative technologies offer; BL does not suggest that digital technologies should replace the analogue (or traditional) classroom but rather that they should enhance already effective teaching strategies (e.g. Bonk & Graham, 2012).
Perspectives of implementing digital technologies
When entering a classroom, one expects to see students engaged in their learning activities (for a matter of minutes or hours), i.e. tasks or schoolwork. A learning activity in school, in this thesis, in not an activity that may lead to learning, which can be any kind of unplanned activity that may (or may not) result in acquiring new skills and experiences. Instead, a learning activity is an activity that a student is expected to participate in, such as working with connecting electrodes to make a robot move, drawing an image, writing a report or holding a presentation. A learning activity can be designed and orchestrated to try and make students more engaged. Wellborn (in Reeve, 2012) defines a learning activity as a task- or context-bound event that is aimed toward learning. There can be one longer or several shorter learning activities within one lesson (ibid.). Consideration can also be given to learning sequences (Selander, 2008) or
7 “mini”-learning activities (Conole et al., 2004) that make up smaller sections of a learning process.
A couple of decades ago, it was believed that students would not need any formal IT training as they were “born as digital natives” (Prensky, 2001). This has, however, been shown to be misleading as the “digital natives” were not more familiar than others with the use of educational technologies (Judd, 2018). Nevertheless, in the information society, we use more intelligent technologies to extend our own cognitive capacities, i.e. our biological memory (Lonka, 2015) and instruments that enable us to solve more advanced problems (Agar, 2013; Heersmink, 2016). While plenty of research suggests that BL improves results significantly (Henrie et al., 2015; US Department of Education, 2017), the relationship between IT skills and performance is likely to be related to the extent to which IT is used in schools: it is logical that the more digital technologies education implements, the more owning digital skills will be needed to engage in TEL. Moreover, it has been suggested that alongside digitalisation, there has been a shift in the norm related to schooling. Where the previous generations were more motivated to study (Lonka, 2015), there are researches who suggest that the new norm for the students of today is “indifference” (Hietajärvi et al., 2015). As engagement is critical for learning (Boekaerts, 2016), a potential shift in the norm further accentuates the need to obtain an understanding of upper secondary student engagement (and disengagement) when learning with digital technologies.
However, most studies that approach student engagement in TEL have been aimed primarily at university-level students (e.g. Groccia, 2018; O’Brien & Toms, 2008; Zhai et al., 2018). The difference between upper secondary students’ engagement and that of university students may be substantial as adolescents going through puberty are in the midst of their most turbulent maturing process, physically, cognitively and emotionally, as well as being occupied with shaping their identifies (Karpov, 2014). Moreover, research from 14 countries, including Denmark and Norway, showed that there are differences between the IT skills amongst upper secondary school students that contribute to digital exclusion (Hatlevik et al., 2015). Although male students no longer outperform female students when it comes to using digital technologies for learning (Aesaert et al., 2017), some findings indicate that they still use digital technologies and engage in programming to a greater extent than female students (European Commission, 2019). To tackle the matter, the European Commission intends to expand on possibilities to develop digital skills for girls and women, improve the image of women in media and look to increase the number of female tech entrepreneurs (ibid.).
Online forums, mobile phones and laptops
If we turn to exploring specific digital technologies such as the use of online forums, mobile phones and laptops, research shows mixed results. For example, research agrees that students commonly use different online forums, LMSs, wikis, web blogs or social media to communicate with peers and teachers. Research has identified a number of possibilities related to learning, for example that technologies may enable instant publishing, sharing and collaboration amongst multiple students in a format that can be
8 used in many settings and for a variety of subjects (Zheng, Arada, Niiya, & Warschauer, 2014), and thus that online forums can be used to enhance students’ critical thinking, literacy skills and ability to use the Internet for research purposes (ibid.), and that social media forums, such as Facebook, increase engagement for university students (Northey et al., 2018). However, the fact that possibilities are enabled does not per se mean they are realised in the classroom setting (e.g. Dalgarno, 2014; Pianta et al., 2012). Conversely, when exploring the cognitive aspects of students’ discussion in online environments it has been observed that contents that displayed “understanding” (i.e. offering examples and interpretations) and “analysis” were found, while others (critical for learning, e.g. “applying”, “evaluating” and “creating”) were not. Research has also highlighted that while it is important that student dialogues are directed toward developing their conceptualisation and reconceptualisation of a subject matter (Laurillard, 2013), it may be challenging for teachers to design learning-focused and well-organised learning activities that facilitate such polysynchronous interaction (Dalgarno, 2014). Dalgarno (2014) concludes that poorly designed learning activities in online forums can have detrimental effects on learning, “with learners becoming distracted by irrelevant dialogue within multiple communication streams, struggling to maintain concentration due to the high cognitive load in attending to multiple sources of content and discussion simultaneously, or engaging only at a shallow level due to the rapid and abbreviated responses that are the convention in mobile communication channels” (ibid., 676). This highlights possible limitations, or at least challenges, for developing the advanced cognitive aspects needed (to, for example, develop argumentation skills) in online discussion activities.
Second, the mobile phone is a material representation of the ongoing digitalisation in all grades. University students may perceive them as enablers of a more flexible and “outside the classroom” learning and collaboration (Gikas & Grant, 2013). Both upper secondary school students (Ott, 2017) and primary school children (Eliasson, 2013), however, have been seen to struggle to balance their mobile phone interaction, and are either unsure of when the technology is meant to be in the background or foreground of the learning scenario (ibid.), or are deliberately using them for unproductive or private purposes (Ott, 2017). A review on the implications of mobile phone use highlighted that mobile phone use often impairs learning due to its distractive elements (Chen & Yan, 2016) and the lack of a “continuous partial attention approach” (Firat, 2013). Heflin, Shewmaker, and Nguyen (2017) drew attention to the fact that while (university-level) students perceived using their mobile phones to collaborate, this usage was associated with increased disengagement. In fact, the students who used their mobile phones to take notes demonstrated significantly less critical thinking than those who either wrote responses by hand or took notes using their computer/laptop (ibid.).
Third, several studies have shown that students who received a laptop perceived that it helped them in school (Diemer et al., 2013; Howard et al., 2016; Rashid & Asghar, 2016) and that it helped improve their grades (Tallvid et al., 2015). At the same time, students who worked with laptops were often seen to work in isolation (ibid.). Moreover, Tallvid et al. (2015) found that both unauthorised and authorised use of the laptop
9 increased in parallel: the more time that was allocated for laptop use, the more time students spent on both unauthorised and authorised use.
Some researchers argue that having computers in school is related to lower academic achievement while using computers at home is related to improved academic achievement (Erdogdu & Erdogdu, 2015; Skryabin et al., 2015). Rashid and Asghar (2016) showed that the use of technology has a direct positive relationship with (college/university) students’ engagement but not necessarily academic performance. They suggest that students might engage with technology but not necessarily for academic purposes, and that in-class multi-tasking using technologies may be distracting and lead to a decrease in time spent on learning. Research specifically focused on adolescents’ media multi-tasking has shown that task switching is correlated with slow response time (Lin & Parsons, 2018). In their review on engagement and media multitasking, Lin and Parsons (2018) state that participants who are “less resistant to distractors might also engage more frequently in media multitasking. Further, the participants with lower attentional control might have been more drawn to multi-tasking as a working heuristic” (ibid., p. 519).
Do teachers need knowledge of engagement in relation to design and orchestration?
There are instances in which the technologies themselves can increase student engagement (e.g. Guarascio et al., 2017; Han & Finkelstein, 2013). Recently, however, there has been an increased interest in how digital technologies are used, and the potential consequences of that use. Several researchers have concluded that if the actual use of digital technologies is not guided by thought-through consideration, this may lead to a lowering of student school results (Chen & Jang, 2010; Håkansson-Lindqvist, 2015; The Swedish Government, 2016). For example, in a study on 12- to 18-year-old students, Salmela-Aro (Salmela-Aro et al., 2017) showed that excessive Internet use could cause school burnout and lead to depression. Another study (Hietajärvi et al., 2019) found that using digital technologies for communication and social networking was consistently either related to lower study engagement or to higher study burnout, and that gaming was related to either lower engagement or higher cynicism. When digital technologies were used to gain and share knowledge (i.e. taking part in knowledge creation), this was related to higher academic engagement. However, the consequences of these uses differed among age groups. For example, communicating and social networking was related to low engagement in lower education, but school burnout in higher education, and sports and action games and social games did not show significant associations with engagement until high school, and these were then negative (ibid.). Yet, another study (Salmela-Aro et al., 2016) highlighted that if educational institutions do not teach their students how to use, what they refer to as, “socio-digital technologies” for learning and in creative ways, excessive Internet use might increase.
Goodyear and Retalis (2010) talk about “teaching-as-design” to draw attention to how teachers, on a day-to-day basis, already design learning activities, and furthermore that more resources should be allocated to acknowledge the importance of informed and thought-through orchestration. Goodyear and Retalis (2010) suggested that TEL is
10 supported by digital technologies that can be categorised adopting a “user focus” that describes the intended kind of use (rather than focusing on technical aspects). While it is commonly agreed that the quality of learning is related to the role of the teacher, there are different ideas of what “good learning” is. Goodyear and Retalis (ibid.) suggest that:
[...]images of “good learning” revolve around the centrality of what learners do – on the quality of their mental activity – so, images of teaching resolve around the design of good learning tasks, and the design and management of supportive learning environments. The emphasis shifts from teaching-as-exposition and teaching-as-interaction to teaching-as-design.
(Goodyear & Retalis, 2010, p. 10)
Researchers have described orchestration as the process in which the teacher designs, implements, develops and evaluates learning activities, using digital technologies to maximise student learning and learning outcome in a real-life setting (Prieto et al., 2012). Placing strong emphasis on the teacher role, Arvola (2003) argues that it is not the tool itself but rather the interactions surrounding the tool that influence the engagement. In this sense, orchestration is a regulation process, in which the teacher “manages, in real time, multi-layered activities in a multi-constraints context” (Dillenbourg, 2013, p. 485). In line with these insights, several researchers have proposed that effective uses of digital technologies in learning activities are those that mediate learning-focused interaction (Dalgarno, 2014; Laurillard, 2013, Sharples & Ferguson, 2019). Teachers are viewed as designers who can orchestrate the uses of digital technologies to facilitate social, learning-focused interaction. For example, Laurillard developed the conversational framework, which she suggests reflects effective uses of digital technologies in a learning situation (Laurillard, 2013). This framework is student centred and focuses on helping and guiding university-level students toward deep learning by reaching a conceptual understanding and reconceptualising their understanding via dialogues mediated via technologies. This idea is similar to the idea of designing for polysynchronous interaction as proposed by Dalgarno (2014), which points to the need to design for multiple simultaneous interactions, across platforms, synchronously and asynchronously to support learning.
Building on learning centred dialogues, Sharples and Ferguson engaged in developing a LMS that enabled multiple types of interactions; supporting both student internal dialogues and interactions between student-teacher, student-maching (hardware and software) and student-student (Sharples & Ferguson, 2019). In Design-Based Research (DBR) interventions, the researcher often collaborates with teachers in situ, to develop artefacts or practices, and identify design principles that can inform future designs (Anderson & Shattuck, 2012). However, not all insights that inform designs and development of digital technologies derive from interventions. Engle and Conant, (2002) proposed a set of, what they refer to as guiding principles, to fostering “productive disciplinary engagement”; (i) Problematising: that students are encouraged to take on intellectual problems, (ii) Authority: that students are given authority to
11 address those problems, (iii) Accountability: that students' intellectual work is made accountable to others and to disciplinary norms and (iv) Resources: that students are provided with sufficient resources (Engle & Conant, 2002, p. 400 p). According to Engle and Conant, principles that inform designs of learning activities, others than those deriving from design interventions, can be referred to as “guiding principles”, as they try to inform a complex phenomena. Working to increase student active participation in synchronous video-mediated learning, Weitze-Laerke (2016) used principles from gaming theories and the flow theory to inform their design. When evaluating their design Weitze-Laerke concluded that students appreciated the relevance of learning, but did not always experience positive emotions. Instead, she suggests that learning may give rise to unsettling and disconcerting emotions, and thus challenges “a light view of learning” in which it is portrayed as superficial (ibid.). Thus, designers of learning activities (including teachers) may find it beneficial to be informed by design principles, or other guidning principles, that may positively influence engagement and learning.
A Nordic perspective on IT in schools
School leaders and teachers have pointed out that schools lag behind due to insufficient ICT equipment, which has been seen as the main hindrance to IT use (European Commission, 2013). The report concluded that only half of upper secondary school students were in highly equipped schools, that 20% of them never or almost never used a computer during lessons and that 60% of upper secondary schools in Europe use an LMS. The report (published 2013) identified some significant infrastructural differences between the Nordic countries and the EU average ‒ for example, that 90% of schools in the EU do not have high-speed Internet access. This excludes the Nordic countries, which have highly developed infrastructure in terms of high-speed broadband connection (European Commission, 2013).
While the digitalisation of education is framed as encompassing rich potentials of new ways of teaching and learning, research has concluded that teaching practices in the Nordic countries have remained traditional (Gudmundsdóttir et al., 2014; Samuelsson, 2014). It has been suggested that the differences in IT use in classrooms may be due to wide differences between teachers’ IT didactic competence (e.g. Samuelsson, 2014). While 35‒40 % of European teachers use digital technologies for learning (OECD, 2019b), only 25‒30 % of European students are taught by teachers for whom ICT training is compulsory (European Commission, 2013). Swedish teachers rank right at the bottom in the OECD comparison in terms of the percentage of teachers for whom “the use of ICT for teaching” was included in their formal teacher training (OCDE, 2019c, p. 5). However, at the EU level, teachers often devoted their free time to developing their ICT skills, as continuous professional development is critical for enabling teachers to implement digital technologies in their teaching practices (European Commission, 2019). A report exploring Swedish adolescents’ access to, and use of, digital technologies revealed that digital technologies have permeated the lives of young people: 98% of students use the Internet daily, and 41% of adolescents own multiple devices, as they have their own tablet, computer and mobile phone
12 (Internetstiftelsen, 2016). Yet others have suggested that there is a need for more formal training for students to develop the skills needed to use technologies productively for schoolwork (Håkansson-Lindqvist, 2015; Internetstiftelsen, 2016), as almost half of the students who did not own a digital device (i.e. a laptop, tablet or mobile phone) reported that they lacked the IT skills required to succeed in school (Internetstiftelsen, 2016).
The Research Problem
This section identifies five critical gaps that have informed the research aim: (i) engagement research has often been directed toward university-level students; (ii) engagement research has regularly overlooked the dimension of disengagement; (iii) much research that measures engagement in TEL has been techno-centric; (iv) there is a tendency for both traditional engagement research and engagement in TEL research to operationalise engagement in simplified ways; and (v) there is little research on engagement and disengagement in TEL, reflecting the conditions of digitalisation in Swedish classrooms.
Research approaching university-level students
This thesis argues that it is critical to approach upper secondary school students separately for a number of reasons. First, as students go through puberty, they may experience an intense and turbulent time; thus their maturity, needs and preferences might differ from those of university-level students (Karpov, 2014). Second, students’ uses of digital technologies impact their engagement and disengagement differently at different ages (Hietajärvi et al., 2019). Third, student engagement has been found to decline as students progress from primary to secondary school (Wylies & Hodgen, 2012), and if students’ disengagement spiral into absenteeism and school dropout, they have a hard time re-entering and pursuing higher education (Department for Business Innovation and Skills, 2014). Thus, interventions should be carried out before absenteeism and school dropout are a fact. Lastly, having dropped out, these students are no longer part of the population from which research aimed at university-level students samples its respondents, and thus those individuals will not inform the research.
Overlooking the dimension of disengagement
Indeed, many online services and applications try to make their users more engaged by exploring how interface design, colours, sound and interaction can persuade them to spend more of their time with the application. However, such approach translates poorly to schools, as schools have a responsibility to engage all learners, including the disengaged ones. While online services and applications look to increase engagement with those already engaged. With much research focusing on engagement (Azevedo, 2015) researchers have suggested that engagement and disengagement are related but different constructs (Wang et al., 2017). Disengagement is not merely “less engaged”. Nor can it be described merely by the variables at the lower end of engagement.
13 However, some indicators of disengagement can be negated and thus can be said to have a natural opposite on the engagement scale (e.g. paying attention/not paying attention), but this is not true for all indicators (Skinner et al., 2009; Wang et al., 2017). For example, while absenteeism and attendance are natural opposites, absenteeism may reflect disengagement, but attending class does not guarantee engagement in learning (Finn, 1989). Thus, trying to tackle disengagement by focusing on engagement only might be naïve, as such an approach may mean that certain facets of hindrances to learning remain undisclosed.
Engagement is operationalised in simplified ways
On the one hand engagement, and disengagement has mainly been directed to include indicators that exclude contextual indicators of digital technologies (e.g. Fredricks et al, 2004; Wang et al., 2017). Thus, if a student is found to be, for example, frustrated, such information will not reveal if the frustration is related to the student’s effort to master a subject, or poorly functioning technologies. Moreover, when including indicators that reflect how engagement and disengagement facilitated, this has often been done by including one indicator in one dimension (e.g. “playing on the phone”) (Fredricks et al., 2016) this is informative but offers a simplified view on how technologies influence engagement. On the other hand, research exploring engagement and digital technologies has used measures like counting clicks, Facebook likes, time on tool and similar, often simplified operationalisations of engagement, which have been criticised (Henrie et al., 2015). The view in this thesis is that research and theory from both fields of interest can, and should, inform each other to provide essential information when striving to design engaging learning activities with digital technologies.
Techno-centric approaches
Research has highlighted that simply adding digital technologies, even when they are developed to enhance learning, will not increase student engagement (e.g. Guðmundsdóttir et al., 2014). At the same time, much research in TEL informs of engagement levels with (i) a particular tool, (ii) brought into the classroom (iii) that is novel for the students, and (iv) that is not explored in relation to student engagement disposition, the variation of engagement over time, or academic engagement with other, or no, technologies (Henrie et al., 2015).
Engagement and disengagement in TEL and Swedish conditions
Today, digital devices are commonly used across ages in Swedish society (Internetstiftelsen, 2016), and in schools in Sweden and abroad (US Department of Education, 2017; The Swedish Government, 2017), However, what is needed to engage learners in a TEL setting may not be the same as what was needed in the traditional classroom (Grissom, McCauley, & Murphy, 2017). But to date, and in contrast to, for example, Finland, there are no Swedish research departments that are specially dedicated to conduct engagement in TEL. Being a Nordic country, vis-à-vis having high level accessibility to digital technologies, Finnish studies may be highly relevant and
14 inform Swedish research and decision-making. At the same time, Finnish school students consistently outperform Swedish students in international reports (European Commission, 2019; OECD, 2006, 2019b) highlighting that Sweden as a, urgently needs to address this lack of research initiative.
A Swedish school inspection report (The Swedish School Inspectorate, 2015) revealed that as many as two-thirds of schools did not meet the educational standards set out to tackle disengagement, and that almost none of the schools investigated the causes of student absence or the need for support. The report concludes that schools, in general, have to improve their efforts in supporting at-risk students and tackle education dropout (ibid.). Thus, when we think about using technologies for learning, we have to make informed decisions that inform effective uses of digital technologies, instead of reproducing the same mistakes in a new setting (Chen & Jang, 2010; Håkansson- Lindqvist, 2015; Warschauer et al., 2014).
While most studies have been aimed at exploring university student engagement (overlooking disengagement), the thesis addresses the gaps above by examining upper secondary school students and their everyday use of technologies in schools, and the variation in their engagement throughout a school week, between school subjects or as a result of different conditions for learning. The substudies explore four dimensions of engagement and disengagement in TEL, namely the behavioural, the cognitive, the emotional and the social, and thereby seek to expand the current understanding of how engagement and disengagement in TEL are manifested, to inform current practices and contribute with knowledge to the field.
15
16 Chapter 2. Theoretical Foundation
While there are no single theories of either engagement or disengagement, this chapter describes engagement and disengagement theories related to the substudies and concludes by offering a sociocultural perspective on engagement. ______
Motivation and Self-regulation
While the main focus in this thesis is engagement and disengagement, some closely related constructs are referred to in the substudies. Below is a brief description of motivation and self-regulation that focuses on reflecting how they relate to engagement.
Motivation
Motivation research has consistently proven that motivation is related to school success (Ryan, 2012). Motivation is typically described as intrinsic or extrinsic, in which student interest (an internal phenomenon) is seen as an example of intrinsic motivation, and parental expectations or grades (external to the student) are seen as extrinsic motivation (Ryan & Deci, 2000). Motivation has also been described as a non-developmental construct (in comparison, for example, to interest, which can be triggered and promoted) (Järvelä & Renninger, 2014). Järvelä and Renninger state that “motivation is typically assessed relative to others and [that] learners are described as being more or less motivated, meaning that they may or may not self-regulate to accomplish goals and/or understand the utility of engagement” (2014, p. 672). While this might indicate that pursuing motivation would be sufficient to understand school success, researchers have suggested that motivation alone is not enough for students to persist in their learning (Boekaerts, 2016; Poskitt & Gibbs, 2010).
To date, there has been no common agreement on the exact relationship between engagement and motivation. However, it is broadly agreed that engagement and motivation are related but distinct constructs. Some propose that motivation drives engagement (e.g. Skinner et al., 2009) and others that engagement both may drive, and be driven by, motivation (e.g. Reeve, 2012). In trying to separate the two, research has proposed that engagement is “the outward manifestation of motivation” being both visible and measurable (Boekaerts, 2016; Fredricks & McColskey, 2012). Others have suggested that engagement is a meta-construct that subsumes motivation (e.g. Fredricks et al., 2004) and is not always observable (Wang et al., 2017). This thesis recognises
17 that underlying, psychological factors may drive engagement, and that engagement, in turn, may influence motivation. Indeed, a student can engage in a topic unknown to them (i.e. without interest or expectation) without sitting passively waiting to be motivated (Reeve, 2012).
Self-regulation
Some researchers focus specifically on self-regulation (e.g. Järvelä et al., 2016; Järvenoja et al., 2015; Pardo et al., 2016). Self-regulation is often used to reflect an agentic nuance of engagement and may be used to address cognitive, behavioural, emotional and/or social dimensions in which the individual is seen as an active agent (Bandura, 1991). While some view self-regulation as individuals’ ability to adjust the energy they direct towards behavioural, emotional and cognitive dimensions of engagement (Järvenoja et al., 2015), others explore self-regulation capacities in the social dimensions (Barujel & Groba, 2014). The idea in approaching self-regulation is that students with self-regulatory (mental) tools can learn and master content much better and faster than a student who lacks them (Karpov, 2014), and by applying regulation, they may resist desires or urges to (inter-)act inappropriately in relation to situational expectations or demands, and thus may pursue their goals more effectively. There are overlaps between self-regulation and engagement (Boekaerts, 2016). Boekaerts points out that while “several aspects of psychological functioning (affect, interest, motivation, volition and self-regulation) are implicated in engagement, they are not the core of student engagement” (ibid., p. 81). Indicators of self-regulation are, however, often included in conceptualisations of engagement (Fredricks et al., 2004), and may even make up the entire cognitive dimension when conceptualising engagement (e.g. Wang et al., 2019).
Engagement
Engagement research has been critiqued. This critique points out in particular that many studies lack clear definitions and conceptualisations of engagement, and that variables overlap, or are used to reflect different engagement dimensions (Alrashidi, Phan, & Ngu, 2016; Henrie et al., 2015). For example, “effort” has been included in the cognitive dimension (Fredricks et al., 2004) as well as in the behavioural dimension (Fredricks et al., 2016). It has been argued that, despite these unclarities, the multidimensional construct of student engagement has proven significant for student success in school (Alrashidi et al., 2016) and has considerable potential as it unites (at least) cognitive, behavioural and emotional dimensions in a meaningful way.
Fredricks proposed that engagement could be used as a “meta”-construct (Fredricks et al., 2004). The higher abstraction level of a meta-construct opens up for differentiated models of engagement. Researchers argue that it is an absolute necessity, as engagement is malleable and highly interactive and context-dependent, they different ways it engagement is manifested must be explored in the context of the learner (Shernoff et al.,
18 2016). Recently, Wang and Hofkens (2019) suggested that engagement both shapes and is shaped by the context, reciprocally and sequentially (e.g. Wang & Hofkens, 2019). This is in line with this thesis, as the aim is to explore how engagement and disengagement are influenced by the uptake and use of the existing digital technologies in schools, yet being informed by, but not limited to, theory.
The attribute “academic” in “academic engagement” is used to describe the students’ engagement in learning and is not related to “academia” per se (Furrer & Skinner, 2003; Skinner et al., 2009). General school engagement may include engagement in after- school activities (Finn & Zimmer, 2012). Some researchers have chosen to approach general school engagement and view students’ engagement in academic activities and social interactions outside the classroom as subordinate (e.g. Christenson & Reschly, 2012; Wang & Fredricks, 2014). Students’ academic engagement, however, relates to students’ engagement in their (formal) learning and has been described as “[t]he time and effort students devote to activities that are empirically linked to desired outcomes” (Kuh, 2009, p. 683), “work completion and accuracy, class preparation, eagerness to learn, and persistence” (Anderson et al., 2004, p. 109) or “a multi-component construct, the common denominator being that all the components (i.e. types of engagement) comprise active, energetic and approach-oriented involvement with academic tasks” (Pekrun & Linnenbrink-Garcia, 2012, p. 260). Moreover, some researchers have proposed that academic engagement subsumes other dimensions of engagement (for a review see Alrashidi et al., 2016), while yet others suggest that academic engagement is a dimension of its own, next to the emotional, behavioural and cognitive dimension (Christenson & Reschly, 2012). In this thesis, the term ‘academic’ is used to clarify that the focus is on the uses of digital technologies for learning, not in relation to after-school activities, which could be the case in general school engagement.
Conceptualising engagement
Currently, it is common to treat engagement as a multidimensional construct with two to four dimensions (Christensen & Reschly, 2012). However, these constructs, or conceptualisation vary. For example, Shernoff et al. (2014) conceptualise engagement as concentration, interest, enjoyment, Schaufeli et al. (2002) as vigour, dedication and absorption and Kahn (1990) suggested that (work) engagement consists of emotional, behavioural and cognitive energy directed toward performance at work. Interestingly, despite these disparities there is a consensus that engagement is critical for learning (Alrashidi et al., 2016).
It should also be noted that researchers do not always approach all dimensions of engagement. For example, Greene (2015) dedicated 20 years to explore the cognitive dimension of engagement. She described cognitive engagement as deep and shallow engagement, which include: “involving the active use of prior knowledge and the intentional creation of more complex knowledge structures by integrating the new information with prior knowledge. Shallow engagement involves rote processing and other intentional cognitive actions that are more mechanical than thoughtful” (ibid., p.
19 15). Green concluded, noteworthily, that focusing on the cognitive aspect alone was not enough to predict school success.
In an effort to form a consensus, Fredricks et al. brought together the most frequently used conceptualisations and operationalisations of engagement (at the time) (Fredricks et al., 2004) into a meta-construct, and suggested that:
1. The cognitive dimension relates to students’ concentration, persistence and invested effort in learning and mastering content. 2. The behavioural dimension includes activities that the student performs to learn, e.g. taking notes, rehearsing and answering questions. 3. The emotional dimension reflects the feelings the student may have, e.g. joy, interest and curiosity, and the acceptance of teacher instruction. (Fredricks et al., 2004) Several researchers have later proposed that engagement can be conceptualised using a fourth, social, dimension:
4. The social dimension encompasses a positive attitude to working and learning with other students, as well as spending time with, supporting and helping peers. (Fredricks et al., 2016; Wang & Degol, 2014; Wang et al., 2017)
Wang, Fredricks, Ye, Hofkens, and Linn (2016) explored the validity of a social dimension and found statistical support for the notion that behavioural, emotional, cognitive and social engagement are conceptually related but distinct and unique constructs. They operationalised the social dimension as: “I build on others’ ideas”, “I try to understand other people’s ideas in science/maths classes”, “I try to work with others who can help me in science/maths”, “I try to help others who are struggling in science/maths”, “I don’t care about other people’s ideas”, “When working with others, I don’t share ideas”, “I don’t like working with classmates”, et cetera (Wang et al., 2016).
Disengagement
Disengagement is commonly viewed as absenteeism and dropout (e.g. Anderson et al., 2004; Strittmatter et al., 2015; Tafelski et al., 2017), or the underlying factors driving absenteeism (e.g. emotional, behavioural, cognitive, social) (Strambler & Weinstein, 2010; Wang et al., 2017). Thus, when approaching disengagement, statistics on attendance and performance are of interest as they reflect, if not disengagement, at least the consequences thereof. A recent PISA report inquired how many students that had arrived late for school during the past two weeks and found that it was around 50%
20 across OECD countries (OECD, 2019a). Swedish students were late more often than the OECD average and expressed less positive feelings than the average OECD student (OECD, 2019a).2 In Sweden, 8.5% (11.2% in Stockholm) of the students had their student allowances withdrawn due to absenteeism in 2018 (The Swedish Board of Student Finance, 2018), which was an increase in comparison to previous years. In OECD countries (and partnership countries), more than 10% of adolescents in age ranges suitable for upper secondary school have dropped out of school (OECD, 2019c). In Sweden, about a third of upper secondary school students drop out from school (Statistics Sweden. Theme report 2017:4, 2017). Yet, Sweden invests heavily in education, with general government expenditure on education among the highest in the EU (OECD, 2019a).
However, the investments in digital technologies in Swedish schools have not led to improvements in students’ school results (Hall et al., 2019). Reports have shown that most of the students who eventually dropped out began disengaging from school long before (The Swedish School Inspectorate, 2015). At the same time, a school inspection report highlights that almost none of the schools investigate the causes of student absence or the need for support and that schools generally underperform in supporting at-risk students and taking preventive measures to tackle dropout rates (The Swedish School Inspectorate, 2015). Freeman and Simonsen (2015) have proposed that disengagement indicators can be identified as early as during the primary school years (Freeman & Simonsen, 2015), and that such approach may assist in tackling disengagement before it escalates into drop out.
Theories of disengagement
Disengagement research is disparate. While Finn (1989) and Wang and Fredricks (2014) describe disengagement as a negative process that may start with indifference and disruptive behaviour, and spiral into withdrawal, absenteeism and eventually school dropout, others have related disengagement to underperformance and social problems (Garcia & Cohen, 2013), describing disengaged students as “underperforming”, “ambivalent students” with “motivational issues” (Hunter-Jones, 2012). During the past decade, researchers have grappled with disengagement in various ways, for instance by pointing to biological factors, viewing siblings as an indicator that may correlate with disengagement (Anderson et al., 2004), or identifying socio-economic factors and family stress levels as causes (Alexander et al., 2001).
Scholars generally agree that mere attendance is not enough for students to engage in learning: It is easy to picture a disengaged student as someone who lacks interest and quickly gives up (e.g. Järvelä & Renninger, 2014). Nonetheless, there is yet no consensus on what disengagement is, and how it is related to engagement. In developing an understanding of what disengagement is, some researchers have focused on understanding disengagement in school (e.g. Järvelä & Renninger, 2014; Skinner et al., 2009), while others have focused entirely on attendance and the lack thereof. Balfanz
2 The most recent PISA report (OECD, 2019b) provided evidence on performance improvements for Sweden, but also stated that the gaps between high and low performers were wide.
21 and Byrnes (2013) view disengagement as an absence that begins with avoidance and develops into chronic, or severely chronic, absenteeism. Yet others have pointed out that some students disengage from one subject in order to engage more effectively with another (Reeve et al., 2019), and thereby imply that disengagement can be related to self-regulatory strategies.
Mostly, however, there seems to be an agreement that disengagement affects students’ learning negatively. When researches began exploring how disengagement relates to engagement, disengagement was interpreted as “less engaged”, and therefore assumed that it belonged at the lower end of the engagement continuum. A “continuum refers to a single dimension of engagement (ranging from high to low); continua refers to the separation of engagement and disengagement/disaffection into two dimensions (each ranging from high to low)” (Christenson & Reschly, 2012, p. 12). Research have also suggested that disengaged students can exhibit a range of indicators other than those opposite to engagement, such as avoidance, helplessness, going through the motions, doing the bare minimum at school, and displaying mental or emotional withdrawal (Finn et al., 1995; Skinner et al., 2009). In line with Skinner et al. (2009), this thesis argues that merely exploring engagement may result in overlooking facets of disengagement. Today, many engagement researchers have identified engagement and disengagement as separate, yet related, constructs (e.g. Balwant, 2018; Skinner et al., 2009; Skinner & Pitzer, 2012; Wang et al., 2017). Wang et al. (2017) used a bifactor model to statistically confirm that engagement and disengagement can and should be treated as separate constructs, each with its own specific factors.
Definitions of disengagement
To illustrate the challenges when defining disengagement, one could say that definitions either reflect an underlying view of an engagement/disengagement continuum or separate engagement and disengagement continua. And while definitions of engagement focus on capturing the something, i.e. what students do, current definitions of disengagement often reflect the non-existence, the nothing of the expected something, i.e. what students do not do or refrain from doing, for example: “the state of being for a group of students who do not actively pursue opportunities to engage in their learning community. For some students, the interlocking of individual and institutional interests, goals and aspirations never occurs. They do not choose or see the need to waver from their familiar path to engage with people, activities or opportunities in the learning community” (Krause, in Trowler, 2010 p. 3), or “students’ simultaneous withdrawal of themselves and defence of their preferred self in displaying low activation behaviours that are characterised by physical, cognitive and emotional absence and passivity” (Balwant, 2018, p. 398). Some conceptualisations combine capturing the something, i.e. what students do when they disengage, e.g. different disruptive behaviours, with the nothing of the expected something, i.e. passivity, avoidance, procrastination, not handing in schoolwork, et cetera (e.g. Wang et al., 2017).
22 Conceptualising disengagement Disengagement has been conceptualised as a parallel construct to engagement with the same dimensions:
1. The cognitive dimension relates to how students give up or prioritise completing objectives “fast” as supposed to “well”. 2. The behavioural dimension involves activities that hinder/enable avoidance of learning: finding excuses to opt out, not adhering to rules, zooming out during lessons. 3. The emotional dimension reflects affection and has to do with irritation, frustration, worrying, boredom and anxiety and may include rejection of the teacher’s instruction.
(Fredricks, 2011; Skinner et al., 2009; Wang et al., 2017)
4. The social dimension encompasses dissociative attitude: not caring for other students, no desire to interact with other students or not feeling noticed by people in the school.
(Fredricks, 2011; Wang et al., 2017)
Examining disengagement is a complex matter. Disengagement is not as stable as engagement has been proposed to be (Boekaerts, 2016). Students may be differently engaged over time or between tasks and subjects (Hockings, Cooke, Yamashita, McGinty, & Bowl, 2008; Reeve et al., 2019). For example, Schaufeli et al. concluded that “feeling emotionally drained from one’s work “once a week” does not mean that the individual is not engaged for the other days of the week” (Schaufeli et al., 2002, p. 294). This was also echoed by Reeve et al. (2019) who suggested that students may disengage from one subject to engage more in another.
Disengagement and the individual
As previously stated, statistical reports may reflect absenteeism and dropouts, but disengagement is a wider problem than statistics can reveal. When attending school, students must also choose to participate. Finn and Cox (1992) categorised students in a class as being non-participants, passive participants or active participants, and concluded that many students, even when attending class, remain passive and that it requires more than simply attending class to be engaged in learning. The fact that student engagement declines during adolescence (e.g. Wylies & Hodgen, 2012), and that low achievers from this group will be more likely to drop out when reaching higher education (Department for Business Innovation and Skills, 2014), indicates that the age range during which students attend upper secondary schools is particularly sensitive. However, it is important to point out that while disengagement may spiral into school dropout, there can be other reasons for dropout than disengagement: for example, health
23 issues. Nevertheless, disengagement has been associated with substantial negative consequences for individuals, as these students run a much greater risk of future unemployment and future health issues, and even risk facing exclusion from society (Freeman & Simonsen, 2015; Tafelski et al., 2017) as problems tend to cluster (Alexander, et al., 2001).
In addition, some studies have proposed that there has been a generational shift in students’ engagement disposition: that the indifferent student has become the new norm (Hietajärvi et al., 2015). Others have nuanced this finding and suggested that students can be simultaneously engaged and disengaged: for example, an emotionally disengaged student might display boredom but still be behaviourally engaged and work to complete their school work (Fredricks et al., 2019). Still, Fredricks point out that students who are simultaneously engaged and disengaged tend to display work avoidance and are more likely to suffer from future depression (ibid.).
Why students disengage
A body of research conducted over the past two decades has sought to understand why students become disengaged and drop out (e.g. Alexander, et al., 2001; Andersson, 2017; Azzam, 2007; Finn, 1989; Grønborg, 2013; Hansen, 2011; Tafelski et al., 2017; Ross, 2009). A Danish study on upper secondary students concluded that disengagement is contextually dependent and socially regulated, and not a set of attributes (and thus malleable, like engagement) (Grønborg, 2013). For example, being an unsuccessful student could mean being successful in belonging to a group, and this could be communicated via emotionally negative expressions reflecting a reluctance to adhere to the norms shared amongst students who do well in school. Yearning for “belonging” (Hansen, 2011) and gaining “social acceptance” (Grønborg, 2013) was a priority amongst all students, and, for some students, disengagement was the strategy to achieve this (ibid.). Thus, behaving in hostile ways could be used to mark social belonging in one group, and non-belonging in another. Others have also pointed out that disengaged students may display maladaptive behaviours such as intensified absenteeism for one or several days and very hostile attitudes to school (e.g. Ross, 2009). While some disengaged students would complete school by simply going through the motions, others would drop out. All in all, these groups were far less likely to pursue full-time education (ibid.). Alexander et al. (2001) showed that family stress levels and personal resources increase student dropout rates all the way from primary to secondary school. In exploring reasons for students to drop out, Azzam refers to “the silent epidemic”. In which he identified five reasons for leaving school: being bored, extensive absenteeism, peers having a negative influence, too much freedom and not enough rules, and the experience of not succeeding in school (Azzam, 2007).
Exploring disengagement in adolescence is particularly important as disengagement tends to intensify when progressing through education, particularly when transferring from lower to upper secondary school (Ross, 2009). Tafelski et al. (2017) pointed out that students with previous school failures that re-engage with education face huge barriers and that these students need directed support if they are to succeed with their
24 re-entry and persist long enough to complete their studies. Although efforts have been made to tackle student dropout rates, these have not decreased notably over the years (Anderson et al., 2004; The Swedish School Inspectorate, 2016). While disengagement has been linked to school dropout, it does not escalate into absenteeism and school dropout overnight. The complexity of disengagement, and the underlying needs of the student, underlines the importance of not judging students as “problematic” when they display signs of disengagement (Hockings et al., 2008). For the majority of students who choose an early exit from their educational career, this decision comes at the end of a “long process of disengagement from school” (ibid.). Studies suggest that students need to feel supported by teachers and their families, as these relationships are essential to the students’ perception of their own school (Fredricks et al., 2019; Voelkl, 2012). There are social needs that include wanting to be seen, to belong, to feel included, appreciated, accepted and recognised, and these are critical for well-being (Andersson, 2017; Grønborg, 2013; Hansen, 2011). There is a risk that teachers do not react to support this need; for instance, Skinner and Belmont (1993) pointed out that teachers may react with either aversion or increased control toward disengaged students. They explain this by pointing to the teachers’ own emotional needs not being met, i.e. that teachers have a need to feel liked or accepted by their students, and when this is threatened, teachers may display avoidance toward those students. Thus, teachers have been seen to refrain from supporting disengaged students (ibid.). However, intervention studies often identify the lack of emotional support as a key factor in failing to reach disengaged students and emphasise that “a one-size-fits-all” approach does not fit all students (Fredricks et al., 2019).
The differences between engagement and disengagement presented above, underscores that a separation of engagement and disengagement may enable us to focus on the particularities that promote engagement and redeem disengagement.
Approaching engagement and disengagement
This section addresses aspects of the context, the indicators and the facilitators relating to engagement and disengagement research.
Context
While some studies (e.g. Wang et al., 2017) have merged the classroom and school context in an approach of general school engagement, other studies, particularly those approaching technology use do not. The classroom context in these studies often deal with how teachers orchestrate the use of different digital technologies and applications such as laptops, tablets, mobile phones and accompanying text and media editing tools, communication applications and learning platforms, et cetera, to support learning (e.g. Gudmundsdóttir et al., 2014; Lindsay, 2016; Tallvid, 2015). The ways in which students can engage in their learning with the use of digital technologies are further limited to the accessibility, quality and usability of digital technologies, how they are orchestrated with other technologies and how they function in the wider educational infrastructure, with learning platforms and educational routines and information delivery, and the
25 effectiveness and usefulness of those learning platforms and educational routines and information deliveries (e.g. Skryabin et al., 2015). On the other hand, a school context would include the after-lesson use of digital technologies, which may say much about the overall uses of digital technologies, but less about how engagement and disengagement are influenced via their use for learning activities in the classroom. Instead, approaching the activity-level of student engagement directly addresses the link between engagement and performance in a learning activity (Skinner & Pitzer, 2012).
Indicators and facilitators
When examining student engagement and disengagement, particularly at the level of engagement in learning, Skinner, Furrer, Marchand, and Kindermann (2008) argue that indicators and facilitators should be separated. They propose that indicators are “the features that belong inside the construct of engagement” and suggest items such as excitement, interest and attention, and that facilitators of engagement are “causal factors (outside of the construct) that are hypothesised to influence engagement” (p. 766) like motivation or self-efficacy. Contrary to Skinner et al. (ibid.), Wang et al. (2017) view facilitators as the underlying motivational triggers of engagement (such as interest). As shown, some definitions revolve round the psychological aspects of engagement, an do not take the external conditions, that may influence engagement, into consideration. However, engagement is less psychological than, for example, motivation, and more interested in learners’ connection to learning and the learning environment (Järvelä & Renninger, 2014). In exploring this aspect, Trowler defines engagement as “the interaction between the time, effort and other relevant resources invested by both students and their institutions intended to optimise the student experience and enhance the learning outcomes and development of students and the performance and reputation of the institution” (Trowler, 2010, p. 3). While Trowler includes “resources”, applying her definition when examining student engagement is hard, as it extends to activities aimed at enhancing the “reputation of the institution” (ibid.), which is wider than approaching a learner in the setting. Christenson and Reschly (2012) argue that the role of context should not be ignored as both facilitators and indicators need to be approached if we are to understand students’ learning environment. Thus, indicators should include contextual influences as facilitators. This line is adopted in this thesis, as, for example, “frustration” can either be directed toward overcoming a problem or be due to a lack of poorly functioning technologies, thus “frustration” without the context will reveal little about the conditions needed to enhance engagement in learning.
Engagement in learning can be understood to consist of a number of aspects, or dimensions. A multidimensional view suggests that each dimension includes a number of items. Together these items comprise the dimension, which then can be treated as a global entity (e.g. Wang et al., 2017). Engagement and disengagement can thus be investigated at different levels. Research focusing on the subset of one dimension is interested in detailing how all of the items in that particular dimension relate to one another (e.g. Greene, 2015). However, this thesis adopts a multidimensional view and uses “the user in setting” (Lave, 2009) as the unit of analysis to identify instances in which students reported feeling engaged or disengaged when learning with
26 technologies. While traditional cognitive theory divides the learning mind from the world, a situated activity does not separate action and thoughts. In situated learning, knowing and learning are seen as engagement in changing processes of human activity (Lave, 2009). The National Survey of Student Engagement (NSSE) in the USA is one example of a research body that uses this approach. The NSSE explores student engagement through examining academic challenge, learning with peers, experiences with faculty and the campus environment (Fosnacht & Gonyea, 2018). These four themes are measured by a range of questions describing students’ own actions, perceptions of coursework (academic challenge), perceptions of instructors’ actions (experiences with faculty) and perceptions of the learning environment to reflect student engagement.
Engagement and Disengagement in TEL
An emerging field
Engagement theories relevantly provide valuable insights into how to support student academic engagement (e.g. Fredricks, 2011; Gebhardt et al., 2014) and prevent student disengagement (i.e. withdrawal, absenteeism, school dropout and other risk behaviours) (e.g. Anderson et al., 2004; Balwant, 2018; Finn, 1989; Finn & Zimmer, 2012; Tafelski et al., 2017). Many engagement researchers (e.g. Eccles & Wang, 2012; Fredricks et al., 2004; Skinner et al., 2009; Wang & Eccles, 2012) have almost entirely continued to measure engagement and disengagement by using items that only reflect student engagement and disengagement in analogue learning environments. Realising the change of conditions when digital technologies are implemented in learning, some researchers have included the odd indicator reflecting technology use, e.g. “playing on the phone” (Fredricks et al., 2016), or as access to online resources, “does not access LMS” and “low or very high levels of online activity” (Chipchase et al., 2017). The problem here is that technology use impacts all dimensions of engagement and disengagement but is only reflected in one.
Little focus has been directed toward defining engagement and disengagement in TEL (Halverson & Graham, 2019; Henrie et al., 2015). On the one hand, some researchers have solved this by creatively combining “traditional” descriptions of engagement in learning with definitions of engagement with digital technologies:
Student engagement can be understood as the cognitive process, active participation and emotional involvement in a learning procedure (Pellas, 2014).
ICT engagement specifically addresses involvement and participation in using digital technologies (Christoph et al., 2015).
(The combination was used in Howard et al., 2016, p. 6.)
27 Henrie et al. pointed out that most articles exploring engagement in TEL lacked a definition (Henrie et al., 2015). Engagement in TEL is, therefore, a growing field that as yet (and understandably) lacks a shared terminology. For example, while Groccia (2018) talks about “online engagement” and “online learner engagement”, others talk about “learners’ technology engagement” (Zhai et al., 2018), while yet others use the terms “ICT engagement” (e.g. Christoph et al., 2015), “user engagement with technology” (O’Brien & Toms, 2008), “blended learning engagement” (Halverson, 2016) or “student engagement in technology-mediated learning” (Henrie et al., 2015), or use the term “learner engagement” in a general sense, without conceptualising it further (Dalgarno, 2014). Furthermore, using the term “engagement” is no guarantee of an interest in students’ academic engagement. Instead, researchers have employed varying foci. For instance, Groccia (2018) used the term “engagement” in technology- mediated learning to discuss engagement in an online environment, and Zylka et al. (2015) suggested that “ICT engagement” could be a term that reflects how digital literacy can be combined with metacognition and motivation (and explicitly stated they are not concerned with computer-related engagement in relation to learning). Yet, others refer to engagement in TEL to describe face-to-face learning that makes use of digital technologies in which engagement might be either directed at the tool or the learning, (Henrie et al., 2015).
There is not only a lack of shared terminology and what should be measured but also how engagement is be operationalised. The varied ways to measure student engagement in TEL include operationalisation of engagement as “the time spent using a tool”, “the number of postings” (Henrie, Bodily, Larsen, & Graham, 2018), “the number of podcasts used” (Henrie et al., 2015) or approaches include analysing content such as: “posts in a forum” (e.g. Hew, 2016) or “virtual world interactions” (Pellas, 2014). Henrie et al. (2018) compared system log data with self-reports of engagement and found that there were no statistically significant correlations between the log data and engagement. Thus, they concluded that this way of approaching engagement in TEL would not capture the multidimensionality (i.e. emotional, behavioural, cognitive and social aspects) of student engagement. Groccia (2019) suggested that distance and online learners must engage affectively, behaviourally and cognitively, and proposed a multidimensional model of student engagement centring around “doing, feeling and thinking” (ibid.). Although not suggesting any indicators of engagement, Groccia argues that online learners need to engage as much as students in traditional settings, in order for learning to be successful.
Currently, there are initiatives being made that aim to contribute to theory development with regard to engagement in TEL, and that also ground their research in a multidimensional conceptualisation of engagement. Two identified studies (Halverson, 2016; Ma et al., 2018) (directed at university-level students) suggest that students’ academic engagement in BL is different to student engagement in a traditional learning setting and that they have contributed with insights accordingly. Ma et al. (2018) gathered an initial pool of items reflecting the behavioural, cognitive and emotional dimensions of student engagement, e.g. “I complete my responsibilities in group work on time” and “I use a web-based system to communicate with other students”. The
28 emotional dimension includes statements like “I am interested in this blended learning course” and “I am tired of learning this blended learning course”. Finally, they propose that the cognitive dimension would be reflected by statements like “I have clear learning goals at every stage of blended learning” and “When the instructor is introducing important content, I think about other things and don’t really catch what is being said” (Ma et al., 2018, p. 236).
Also directing their attention at university-level students, Halverson and Graham (2019) proposed a conceptual framework across all BL settings, reflecting university student engagement in TEL that focuses on two engagement dimensions: the emotional and the cognitive. They suggest that the cognitive dimension includes indicators that reflect quantity and quality: for example, time on task, attention, metacognitive strategies, effort and absorption. Earlier, Halverson published her thesis reviewing emotional and cognitive aspects of engagement (Halverson, 2016) suggesting that emotional engagement should be separated into a positive and a negative construct. The positive construct of emotional engagement would, for example, reflect enjoyment, happiness and confidence, and the negative construct boredom, frustration and anxiety. Halverson concludes that the main finding is that face-to-face and online engagement are related but distinct constructs, as engagement in face-to-face settings and in technology- mediated learning differs.
A Sociocultural Perspective of Engagement Vygotsky suggest that interaction and dialogue are essential for students’ interpretation and meaning making (Rieber & Carton, 1987). Interaction, and dialog are echoed in the social, cognitive, emotional and behavioural dimensions of engagement and disengagement. As engagement is shaped by interactions between the learner and the subject and the environment, these structures become relational, personal, contextual and sociocultural factors that may foster or undermine engagement (e.g. Wang et al., 2019). Vygotsky proposed that all learning should be placed at the ceiling of a learner’s understanding, referred to as the top of a student’s zone of proximal development (ZPD). A significant (more knowledgeable) other may then decide on a goal for the learning slightly above the learner’s current understanding. The ZPD enables a child to develop self-regulation and maximise their learning (Rogoff, 1990). Vygotsky suggested that no one is born with the ability to self-regulate. Instead, a young child is “a slave of his visual field” rather than “a master of his own attention” (ibid., p. 137). However, self- control (a subset of self-regulation) can be learnt. The idea is that a more knowledgeable person can perform the required action or support learners by rephrasing a learner’s understanding. The learner eventually integrates the modelled actions or psychological tools (Karpov, 2014). Vygotsky visualised a learner who receives just enough support to expand his/her learning. This guiding, and social interaction, teaches the learner self- regulatory strategies (Wertsch, 1985). The support is adjusted and decreases as the learner acquires autonomy. Vygotsky separated lower and higher mental processes. When an adult introduces a psychological tool to a child, the child integrates this tool,
29 at which point it becomes an inner resource, a sample of higher mental processes, to that child. This process of teaching children psychological tools is referred to by Vygotsky as “mediation”, and leading the way by guiding the student and giving instruction is the main way to mediate these tools (Karpov, 2014). As the period of adolescence might be turbulent, schooling needs to be consistent. For example, adolescence may include a decline in engagement, and display an increased need to search for identity, explore sexuality, expand social interaction and even rebel against parents. At the same time, Neo-Vygotskians have suggested that adolescence is a period in which the human being develops new cognitive abilities, apt for high-order thinking (ibid.) which in turn increases the demands on the school to offer the scaffolding and support needed for students to maintain their engagement. In the information society, digital tools are sometimes used to support learners. Thus, mediation revolves around the notion that tools (external, i.e. physical and internal, i.e. intellectual, psychological) mediate the reality to individuals’ practices. “To mediate” goes beyond the terms “to support” or “scaffold”. Mediation is a link between A and B, and Säljö (2014) suggests a comparison to the German word Vermittlung. To “mediate” in this view suggests that people are not in direct contact with an uninterpreted world view. While Vygotsky focused on the “significant other” to support and guide the integration of psychological tools (Karpov, 2014; Wertsch, 2009), socioculturalists focus on learners’ experience and how this is mediated by language (e.g. Säljö, 2014). Säljö (ibid.) argues that our understanding of the world is mediated by using mental tools and that it is essential to approach people in social practices using their tools, as if their tools are removed, they will become helpless individuals robbed of their sociocultural resources. Mediating, in this context, suggests that thinking and perceptions have developed from our culture and our intellectual and physical tools. Mediation of reality through practical and intellectual tools is a reality of our society. Säljö further suggests that human practices are tightly interwoven with different tools. “If we are to understand how humans use cognitive resources, learn and master situations, we cannot ignore that we function in interaction with artefacts; that we deal with situations by using physical and intellectual tools” (p. 76). When these cognitive efforts are focused on learning, and paired with behavioural actions to learning, in an accepting manner, we talk about engagement in learning. While all students exist in a socially situated learning environment, the social dimension of engagement refers to interactions to learn (Wang et al., 2017). Teaching practices require teachers to have the ability to orchestrate digital technologies that mediate individual and collaborative learning processes (Ritella, 2018). As digitalisation spreads, teachers will have to learn to orchestrate multiple digital technology tools in complex learning situations to ensure that learning activities in classrooms run smoothly and effectively support individual needs and preferences for learning. Technologies change the conditions for teaching and learning: they both solve and add problems. They affect the distribution of tasks between individuals, collectives and artefacts, between what we do inside our head, with our own body and with technologies. Teaching is about the interplay between individuals and these resources (Säljö, 2014). Orchestrating technologies is more than using an overhead projector and a laptop; it concerns considerations of pace, place and time and the extent to which
30 content should be personalised (Ritella, 2018). Learners can be supported by scaffolding what takes places in the real or virtual environment, by framing and structuring content, or by interacting with a teacher, peer or tool. Orchestrated in ways that support these varied interactions where the use of language and culture lie at the core of its functionality, in the form of, for example, synchronous chat, asynchronous chat, forum posts or blogging, the participants are given the opportunity to engage in learning. When the individual engages with the content, uses the content to inform their output (via dialogue or written contribution) to expand their understanding, develop their conceptualisation, plan and execute their production, individually and in collaboration, the learners are essentially manifesting the behavioural, cognitive, emotional and social dimensions of engagement in learning.
Assumptions of Engagement Informed by the theoretical underpinnings of engagement and disengagement, and the complexity when learning with digital technologies, these form the basis of certain assumptions made in relation to the research questions and thesis.
1. Without engagement, there will be no learning The first assumption is that student engagement is crucial for learning (Fredricks et al., 2004; Skinner et al., 2009; Wang & Fredricks, 2014). While engagement and motivation are related constructs, motivation alone is not a sufficient condition for engagement (Blumenfeld et al., 2006). In his research, Reeve (2012) examined the relationship between motivation, engagement and learning. He argued that students do not need to sit passively in order to be motivated by teachers to engage; rather, they are active individuals who can make conscious decisions to engage. Student engagement shapes motivation and vice versa. From a sociocultural perspective, as well as in critical realism, it would be hard to see a student learning in a vacuum, without engaging with something or someone (Bhaskar, 2013; Karpov, 2014). Reeve concludes that it is “rather difficult to think of a path from motivation to achievement that does not go through student engagement” (2012, p. 149), implying that without engagement, there will be no learning.
2. Engagement can be influenced Second, it is well known that engagement is related to successful learning. Urquiza- Fuentes and Paredes-Velasco even argue that “the more engaged the student, the more effective the learning” (Urquiza-Fuentes & Paredes-Velasco, 2017, p. 693). However, student engagement is not static. Skinner et al. suggest that “[e]ngagement represents a potentially malleable proximal influence shaping children’s academic retention, achievement and resilience” (2009, p. 494). As engagement is malleable, teachers, materials and learning environments can influence student engagement, and thus affect students’ success in school. This kind of influencing is further echoed in the Vygotskian
31 ideas of scaffolding, providing models or having a more knowledgeable other support for students (Karpov, 2014).
3. Disengagement is not only lower levels of engagement
A third assumption (already described, but still significant to include here) also underpins this research. This is the notion that engagement and disengagement are related but different constructs (Wang et al., 2017). While some indicators of disengagement have a natural opposite on the engagement scale, this is not true for all indicators (Skinner et al., 2009; Wang et al., 2017). For example, some engagement indictors have a natural opposite that indicates disengagement, such as concentrated/distracted, interested/bored. However, this does not work for many indicators, e.g. raising a hand/lowering a hand, as lowering a hand is not a cultural expression of disengagement (while raising a hand to answer the teacher’s question, in the context of school culture, may be a sign of engagement). Similarly, disengagement is often operationalised as absenteeism and truancy. The opposite to these would be attending and being on time, but these do not guarantee engagement in learning (Finn, 1989).
4. Teaching and learning are complex and do not get less complex when introducing digital technologies
Lastly, teaching and learning are complex matters. The implementation of digital technologies changes the conditions for learning. Such a transformation can be seen, for example, in emerging teaching practices, and opportunities to interact synchronously, asynchronously and polysynchronously and therewith related challenges (Cerratto- Pargman et al., 2017; Dalgarno, 2014; Tallvid, 2016). Such a dramatic transformation of teaching and learning is also reflected in projects that aim to inform the field by generating new “conceptual and methodological analytical tools for examining the material conditions of tablet-mediated collaborative learning” (Cerratto-Pargman et al., 2017, p. 1) and shows that the sociocultural ideas of the necessity of dialogue, interaction and meaning making are still highly relevant to revisit (Vygotskiĭ et al., 1987; Wertsch, 1985).
32 Chapter 3. Philosophy and Methodology
Chapter 3 positions the research by addressing philosophical assumptions and considerations made in relation to methodology, research designs, methods, validity and ethical aspects. ______
Philosophical Underpinnings
Critical realism (CR) is a modern, and complex, philosophical approach that offers a solid foundation, while yet embracing dialogue with other scientific perspectives and methodologies (Bhaskar, 2013; Bhaskar et al, 2015; Danermark, 2018; Gillberg et al, 2017). With its ambition to provide a philosophical stance and structure that enable the researcher, with no limitation on methods, to explore a research query by uncovering different layers of a phenomena, in the social world and the real world, CR was found particularly suitable for these substudies that combined mixed-methods approaches to explore engagement and disengagement in the real classroom setting and as a phenomenon in the social world.
While this section introduces CR as it relates to the present research, it does not claim to exhaust the subject of CR. The philosophical assumptions in CR are related to questions like: “Is there a reality beyond our understanding?” (ontology), “Is it possible to know anything of that reality?” (epistemology) and “What methods can be used to acquire this knowledge?” (methodology). These aspects are expanded on below:
Epistemology and Ontology
Bhaskar used the term “critical realism” in A Realist Theory of Science (Bhaskar, 1975), not knowing the impact that it would have. CR was a reaction against both positivism and relativism. Positivists had proposed that science should be objective, and that data should be observable and measurable. Critical realists believed in the undertaking of the same scientific project, but did not agree with a pure positivist approach (Bhaskar, 2016; Gillberg et al., 2017). On the other hand, relativists had proposed that the world is in constant flux, and that nothing can be known (Gillberg et al., 2017). Thus, CR took a middle position, based on realist ontology: “There is an objective reality, independent of our perceptions, that we can uncover” and adding to this that the reality is both structured and changeable, and not an open system in chaos, and thus it is possible to make sense of it (Gillberg et al., 2017).
33 CR promotes both the use and development of theory (Gillberg et al., 2017), but encompasses some central ideas:
• That knowledge, experiences and observations that we can uncover are provisional and conditionalised; • That we can never uncover all of reality; only a small portion; • and that reality is stratified.
(Bhaskar, 1975; Bryman, 2016; Gillberg et al., 2017)
Bhaskar proposed that all knowledge should be viewed as provisional, and suggested that theories, and even structures, develop as they are constantly informed, and interrelated (Bhaskar et al., 2015; Bhaskar & Hartwig, 2010).
Because the world is stratified, Bashkar suggested that to uncover aspects of our (social) world, there are three layers that may be explored: (i) the empirical ‒ through experiences, measurements and observations; (ii) the actual ‒ via exploring events, things and products; and (iii) the real ‒ by identifying and analysing generative mechanisms, causes and power structures that produce events (Bhaskar, 2013). That reality is both structured and changeable implies that some of the existing structures can be changed (Gillberg et al., 2017). Changeable structures may include orchestrations of digital technologies or designs of learning activities, and these in turn may shape and influence both student engagement and disengagement. However, we can only uncover a fraction of the reality.
Using Bhaskar’s stratified model of the empirical, the actual and the real, Bhaskar suggests that the empirical reflects the individual’s experiences. In this thesis, the actual refers to, for example, phenomena (i.e. engagement and disengagement) that may be partly observed (and some indicators of engagement and disengagement are easier to observe than others) and the real, which here can be said to reflect the significance and relationships between specific indicators of engagement and disengagement and their relationship with contextual factors. Still, to uncover any part of reality, a range of methods must be used, as no method may encompass data collection and analysis of every entity in the world (Bhaskar, 2016). Moreover, critical realists separate the human being as material, from social phenomena which exist as conceptual (i.e. it exists separately from the individual; for example, engagement may exist as a phenomenon, separate from the individual) (Bhaskar & Hartwig, 2010). To understand the world, critical realists build models that are conceptual rather than material (Bhaskar, 2013) (i.e. engagement can be conceptualised as a multidimensional construct, with several variables).
Moreover, according to CR ontology, all methods are fallible in this pursuit as they may only uncover certain aspects of the individuals’ particularity, and thus CR promotes methodological pluralism (Bhaskar, 2013).
34 Methodology
As described above, Bhaskar proposed that one can rarely observe all dimensions directly. Instead, several different methods and in-depth interpretations need to be employed to uncover as much nuance, and different layers, of the phenomena studied as possible. To fulfil this need, the substudies employ mixed methods. Mixed-methods research allows the researcher to design their study to draw on strengths from both qualitative and quantitative methods (Creswell & Clark, 2011). Bhaskar clarified that CR not only promotes methodological pluralism in general, but mixed-methods approaches in particular (Bhaskar, 2016).
A common misunderstanding is that critical realists dismiss the usefulness of quantitative methods. Critical realists, such as Bhaskar, suggest that social phenomena (e.g. engagement and disengagement) can be approached in different layers, material or conceptual (Bhaskar, 2016). Bhaskar referred to Næss (2004) for insights into applying a CR perspective when using quantitative methods for prediction.
Finally, because we are materially embodied as well as conceptualising beings, the human sciences must be prepared to use quantitative as well as qualitative research, that is, to measure and count our material features, as well as interpret and record our conceptual activity – to employ, in effect, “mixed-methods” research.
(Bhaskar, 2016, p. 57)
Despite this referral (and others), quantitative approaches have at times been viewed as controversial within CR. The scepticism with regard to predictions is built on two aspects: first, that statistical methods ignore the qualitative aspects individual humans bring, thereby collapsing the empirical, the actual and the real; and second, that social systems are always open, and thus in flux, where no generalisation or prediction would be possible (Bhaskar, 2013; Næss, 2004; Ron, 2002). The first aspect assumes that the substudies overlook individuals’ expression and meaning making, which is not the case in the present research: substudies I‒III, as will be shown, employ a range of data collection techniques in the respondents’ physical location to capture mainly qualitative aspects. The second refers to the notion of open systems, which may not have inherent regularities (Karlsson, 2011; Næss, 2004). However, CR has as a central point that the reality has regularities: for instance; when we drink our tea or coffee in the morning we usually drink it around the same time, with a similar amount of milk and sugar, at mostly at our own table, not any other random time, amounts or tables. Karlsson (2011) suggest that everyday activities, similar to these, do not have an unlimited number of variations, as they are guided by structures such as geographical location, regulation, time and physical limitations. Thus, he concludes, social systems can be said to have certain regularities. Furthermore Karlsson proposes that such systems are, for example, systems that occur within a location (for example, an educational setting) that employs certain
35 spatiotemporal structures (guided by regulations) and entities within different layers within those structures (here: orchestration of digital technologies, design of learning activities and indicators reflecting engagement and disengagement in those activities) (ibid.). Thus, the second objection is not valid for this particular study. Instead, teachers and decision-makers need to base their decisions on something. Therefore, the findings aim to inform on individual students’ meaning making as a “user in setting” under specific conditions. This is in line with Bhaskar’s view that knowledge has to be situated in the social world and in the world (Bhaskar & Hartwig, 2010).
Many critical realists agree (e.g. Bhaskar, 2013; Danermark, 2018; Næss, 2004; Ron, 2002) that if researchers want to explore, assess, or confirm, the relationship between variables, or even to suggest limited generalisations of the findings, quantitative statistics are needed. For instance, inferential statistics can contribute to the observed results, which both Næss (2004) and Danermark (2018) suggest are the “unpacking of different layers of explanations” following the CR tradition (e.g. Næss, 2004, p. 142). Substudies IV and V employ a mixed-methods research design together with predictive statistical analysis. Predictions using student engagement are common and quite uncontroversial (e.g. Sawyer, 2014; Strambler & Weinstein, 2010). Substudies IV and V employ both a material and a conceptual approach (as above), seeking methodological insights from critical realists such as Næss (2004) and Ron (2002). Næss applied a CR approach in a study in which he identified indicators through qualitative interviews and then explored their relation using traditional quantitative analysis methods. A similar approach was undertaken in substudies IV and V. In line with Næss (2004), this thesis argues that for some research questions, it is meaningful to search for insights that may inform the relation between specific indicators (i.e. striving to uncover insights in students’ reality) but for a larger population. In another study, Ron (2002) combined CR and regression analysis and concluded that it was both “useful and necessary”. Ron suggested that a regression analysis, from a CR perspective, can be said to reflect entities that make up the structure of our understanding, rather than a universal law that would be possible to generalise across all humans, all times and all geographical locations.
Research Design
The thesis combines qualitative and quantitative approaches by emphasising different aspects in the substudies to uncover structures informed by theory and empirical data. These values look to understand individuals’ meaning making, their experiences, their engagement and their disengagement in the context of TEL (Braun & Clarke, 2013; Willig, 2013). This is done by approaching the world as being built up by separate entities with social systems that exist, as concepts, independently of the individual. This section describes the context of digital technologies and then moves on to describe the mixed methods that have been applied across the research.
36 The context of digital technologies Through their use of laptops and the schools’ LMS, students can utilise the LMS not only as a repository for learning materials (e.g. lecture notes, presentations, links to additional online resources including streaming media, previous or online tests), but also to submit assignments, interact with teachers and peers to give and receive feedback on their learning outcomes. According to Zacharis (2015), both social and collaborative aspects of BL can be facilitated by an LMS ‒ asynchronously (through, for example, emails, forum discussions, calendars, course notifications and file sharing) or synchronously (through, for example, chat functions and online forums). Moreover, learning in an LMS facilitates polysynchronous dialogues as all students in a class can interact simultaneously with peers, teacher, content and machines across multiple platforms (Dalgarno, 2014). Dalgarno defines polysynchronous learning as “the integration of learner-learner, learner-content and learner-teacher interaction through a blending of multiple channels of face-to-face, asynchronous online and synchronous online communication” (ibid., p. 676). An overview of digital technologies used is found in Figure 3. Substudy I includes A‒ F, while substudies II‒V include A‒E.
Figure 3. Overview of digital technologies in substudies I‒V.
*D includes any applications used for text and media editing, programming, calculating, planning, collaborating, communicating or browsing intended to support learning on the students’ devices.
Using devices like mobile phones, tablets and laptops, and online resources like an LMS and online websites, the teachers may design a range of BL scenarios: students may work individually on their laptop, or be arranged in pairs, smaller groups or a combination, for varying durations. Instructions may be combined with peer/teacher or machine interaction, individually or collaboratively, using online content or classroom
37 interaction, pre-, post- and during class. Swedish upper secondary schools often apply a bring-your-own-device (BYOD) routine, allowing students to bring their devices, such as mobile phones, to their classes. Thus, even though teachers have the right to ban the use of mobile phones, they often allow them as they are integrated in the uses for learning (Ott, 2017).
"In my school, digitalisation translates into having a laptop and access to an LMS" 350 300 250 200 150 100
Number of students 50 0 I mainly disagree I agree to some I agree fully extent extent
Figure 4. Students’ perception of what digitalisation means in their school (substudies IV‒V).
When asking students (in substudies IV and V) what digitalisation actually entails in their school, the majority of them reported that digitalisation was equivalent to having a laptop and access to an LMS (see Figure 4). During observations made in substudies I‒ III, it became apparent that mobile phones were used as an additional device to support learning. Students would for example use them to look up words while reading off the laptop screen, so as not to have to leave the active (laptop) window. On other occasions, mobile phones or another tablet/laptop were used for calculations, calendar functions, references, note taking, and so on. Therefore, students’ private mobile phones are included as digital technologies used for learning (see Figure 3).
Section one ‒ design-based research using mixed methods
Section one describes how substudies I‒III approached engagement with a smaller number of participants (substudy I: n = 10) (four students, six teachers), with repeated interactions spanning four months (substudy II: n = 2) (two teachers who in turn implemented the intervention in two classes) and evaluation of the intervention (substudy III: n = 33). Selection process: the research was conducted with a school with which the institution had an established connection. The principal asked which teachers would be interested in participating. Six teachers agreed to have their classes observed, among whom one lead teacher of ICT and one teacher of Swedish agreed to participate throughout substudies I‒III. These two teachers suggested students with similar schedules to enable observations and shadowing (see Ethical considerations for details).
The intention of DBR is to learn more about “the nature of learning in a complex system and to refine generative or predictive theories of learning” (Design-Based Research Collective, 2003, p. 6). DBR has great potential for: “(a) exploring possibilities for
38 creating novel learning and teaching environments, (b) developing theories of learning and instruction that are contextually based, (c) advancing and consolidating design knowledge, and (d) increasing our capacity for educational innovation” (2003, p. 8). Being designed by and for educators, although not being attached to any particular method, Anderson and Shattuck (2012) define DBR as:
• Being situated in real educational contexts • Focusing on the design and testing of a significant intervention • Using mixed methods • Involving multiple iterations • Involving a collaborative partnership between researchers and practitioners • Evolution of design principles • Practical impact on practice (Anderson & Shattuck, 2012, p. 16)
Implementing DBR can be seen as adding to the practice on the grass-roots level (Collins, 1992), affecting both teachers’ practice (Cobb et al., 2003) and the programme level (Bannan-Ritland, 2003). This sets the stage for the educational arena, as DBR offers a way to test, in practice, theories that have previously not been understood or spread and, according to Wang and Hannafin (2005), enhance the teachers’ chances of understanding the implications of digital technologies brought into the classroom for learning.
While some DBR interventions have revolved around iterating the development of prototypes, Kress and Selander (2012) point out that design does not have to be aimed at a tangible artefact, but can revolve around shaping learning situations and social interactions. Following Kress and Selander (2012), substudy II focused on designing for increased engagement, identifying researcher and teachers’ meaning of engagement, hindrances thereof and ways to remedy them in the learning situation. Reeves (2006) described the DBR approach as research in four phases: analysis, design, iterations of design and outcome (see Figure 5).
Figure 5. The DBR process as inspired by Reeves (2006).
With the aim of designing TEL activities to increase student engagement, the research in substudies I‒III was guided by traditional DBR approaches (Anderson & Shattuck, 2012; Wang & Hannafin, 2005). Combining Anderson and Shattuck’s (2012) definition of DBR, the values of what constitutes best practices for DBR (Design-Based Research
39 Collective, 2003) and the intentions outlined above, the research approach for substudies I‒III was designed following the four stages shown above.
Research design substudies I‒III: stage 1
In substudy I, data from observations and diaries were combined (see Figure 6). The participants were four upper secondary school students in their second year (and six teachers). The students were enrolled with a Media & Information Technology program.
Figure 6. Data collection and analysis substudy 1.
Data were collected over five school days during which students filled in diaries reflecting their engagement in each lesson (see Figure 6). Dedicated classifying schemes were used to observe the orchestration of digital technologies in their learning activities as well as observable engagement. The visualisations in dedicated classifying schemes allow the observer to “eyeball” the findings as a part of the analysis, as graphics are descriptive reflections adapted for the mind with a continuum reflecting the lower and higher end of categories (Kerlinger & Lee, 2000; Miles & Huberman, 1994). Student diaries were analysed using thematic analysis (TA). Data were gathered using the two dedicated schemes and student self-reports of engagement were triangulated.
40 Research design substudies I‒III: stages 2‒4
The second substudy was focused on design and intervention (see Figure 5). A future workshop inspired by Kensing and Madsen (1991) (see Table 2) was held with the two upper secondary school teachers.
Table 2. Future workshop overview Phases First action Second action The Critique Phase The problems seen today with A clustering of ideas into regard to student engagement themes (in relation to IT use)
The Fantasy Phase A free creative session A clustering of ideas into envisioning what could be themes
The Implementation Phase Ideas on possible solutions Choice of ideas
The Action Phase Planning in regard to who, Emerging ideas and rough with which class, what and drafts on lesson design why and when
Photos 1 and 2 provide an overview of themes derived from teachers’ free association when identifying problem areas, envisioning what could be and planning ahead. Ideas were jotted down on post-it notes, which were later shared, and clustered in themes.
Photo 1. Clustering in the Critique Phase Photo 1. Outcome of the Fantasy Phase Photo 1 shows clustering in the Critique Phase, with the following themes: relevance, teacher-student contract, self-regulation, content focus and feedback. Photo 2 shows the following two themes: (1) “Visions of what a TEL intervention can bring”: assembled submissions, resource repository, possibility to reflect, less costly for students to fail, goals to meet, polysynchronous answers, timely feedback, quick and easy feedback,
41 scaffolding structure, skills matrix, progress bar; (2) “Tools needed to realise the fantasy”: LMS, online tests, progress bar, badges. Within a future workshop, there is an emphasis on mutual learning and equality that matches the built-in values of empowerment and inclusion in DBR (Anderson & Shattuck, 2012). A jigsaw technique was applied: the teachers’ views on student engagement were gathered during the workshop and evaluation; teachers were also informed about engagement theory. Following the clustering, and the discussions, we collaboratively decided to add an extra teacher tablet, with which the teacher could oversee student contribution and activity in the LMS forum (see Photo 3). This way prompts would be instant ‒ encouraging students to think deeply, as well as confirming high-quality contributions. This overview also gave the teacher an opportunity to direct support to pairs or groups of students working together, as well as those needing one-to-one support. When students had posted their contribution, they could access each other’s contributions, which triggered them to want to contribute. Students practised formulating ethos-, pathos- and logos- supported arguments (within the subject of Swedish). The students gave examples that they thought were ethos (involving emotional aspects to support their argumentation), and a peer gave the feedback that they had actually provided logos (relying on logical reasoning to support their argumentation).
While there were outcomes from the DBR intervention in the form of design principles, further evaluation was sought from students (substudy III). While the teacher had been supported when implementing the intervention in one of the classes, it was implemented in yet another one without support. Thirty-three out of 55 students gave their consent to participate in the study. The evaluation questionnaire combined multiple types of questions (e.g. tick boxes, multiple-choice questions, open questions and a Likert scale to report on Photo 2. A teacher viewing students’ online their level of engagement). Descriptive contributions statistics, Spearman’s rank correlation and (published under licence of Creative Commons) content analysis (CA) were used to analyse the data.
42 Section two – exploratory sequential design
Section two describes how substudies IV and V build on the insights gained in substudies I‒III, and apply that to a larger population (n = 410). The selection process had two stages: Stage 1 ‒ two schools that had established connections with the institution were contacted. Two teachers, one from each school, asked students in their classes if they wanted to participate in the study. Eight students agreed to be interviewed. A questionnaire was informed by these interviews. Stage 2: A purposive sampling technique was employed (Bryman, 2016) in which the five largest national programmes leading to a qualification for higher education (The Swedish National Agency for Education, 2018) were selected. From these, schools governed by the City of Stockholm were selected, as they had used an LMS for at least ten years and had distributed laptops (or similar devices) to each student. The technology programme was then omitted to avoid potential bias. Acknowledging the diversity within schools, the aim was to collect data from different programmes in the same school, thus only schools that offered at least two of the programmes were approached. Eleven of the 15 contacted schools agreed to participate: seven inner-city schools and four suburban schools.
Substudy IV adopted an exploratory sequential design (Bryman, 2016). According to Bryman, exploratory sequential design has three phases: 1) an initial qualitative phase of data collection and analysis, 2) a phase of quantitative data collection, and 3) a phase in which separate strands of data are analysed (see Figure 7).
Figure 7. Stages in exploratory sequential design. Substudies IV and V use exploratory sequential design to explore how students perceive that their engagement is related to technologies.
Research design substudies IV and V: stage 1-2
With the insights gained from substudies I‒III, eight students were interviewed with regard to their experiences of emotional, behavioural, cognitive and social engagement and disengagement (Fredricks et al., 2004; Wang et al., 2017). The interview questions were formulated to capture engagement and disengagement when digital technologies were used, as well as the educational context (e.g. “Can you describe your engagement and disengagement when learning with digital technologies?” and “What was a learning situation with digital technologies that you felt increased your engagement?”). The interview was inductively analysed using a thematic analysis approach (Braun & Clarke,
43 2013), while remaining sensitive to theory (Patton, 1990). “The user in setting” (Lave, 2009) was used as a unit of analysis to identify instances in which students reported being engaged or disengaged when learning with technologies, considering specifically the behavioural, cognitive, emotional and social dimensions of engagement and disengagement (Wang et al., 2017). Forty-nine questions formed the basis of the questionnaire. An equal number of questions reflected behavioural, cognitive, emotional and social aspects of engagement and disengagement.
Research design substudies IV and V: stages 3‒4
In substudies IV and V (as in substudy III), descriptive statistics and Spearman’s rank correlation were used to analyse the data. Substudy IV also used independent t-tests and regression analysis to analyse the data. In substudy V, factor analyses were conducted. Factor analysis differs from other multivariate procedures in that there is no separate identification of dependent or independent variables. The relationships between variables are examined without specification of one variable’s influence on another (Beavers et al., 2013). As unobservable aspects of engagement typically cannot be directly measured, the Learner-Engagement-Technology (LET) instrument was developed using students’ expression of conceptualisation of engagement and strong theoretical foundations. Factor analysis uses the items to model the relationships between the measured variables and latent factors (Field, 2018). It is assumed that these latent factors lie behind the unobservable variables in engagement and disengagement. Factor analysis is concerned with exploring the underlying factors that are present in the data set, and the extent to which the (theoretically and inductively) suggested items relate to these underlying factors. If there is no relation to a factor in the data set, items are removed.
Validity and Reliability
Both qualitative and quantitative approaches are interested in reflecting the quality of the data and results, and the interpretations made (see Creswell & Clark, 2011). The most prominent criteria for evaluating social research are reliability, replication and validity (Bryman, 2016). Table 3 provides an overview of these evaluation criteria, and summaries these for both quantitative and qualitative research (Bryman, 2016; Miles & Huberman, 1994). This section offers insights into the considerations made to ensure validity and reliability, and thus reflect the trustworthiness of the present research.
Ecological validity
In substudies IV and V, the LET instrument was developed. Ecological validity was sought through two separate pretesting occasions each with five students, with one group of students being younger and one older to ensure questions would be relevant and understandable to the target group. After the pretesting some clarifications and changes were made. The questionnaire items were also discussed amongst the authors, who hold expert knowledge in the field of TEL. While all substudies build on Fredricks
44 et al.’s (2004) conceptualisation of engagement, and related engagement research from an educational psychological perspective and research on TEL, substudies I‒III, and IV and V, build specifically on each other and share the ecological validity. Substudies I‒ III are conducted in one school. Ecological validity can be confirmed as students’ and teachers’ perceptions were sought, data were triangulated and established engagement theories were used.
Table 3. General criteria for evaluating research Criteria for evaluating qualitative and quantitative studies
Reliability/Dependability: refers to the stability and replicability of research findings by probing aspects like: Is the instrument validated? Are the measures used repeatable? Are the processes clearly described?
Internal validity/Credibility: refers to causal relations and if we are measuring what we claim we are measuring. Questions raised to probe this aspect can be: Can the independent variable, at least partially, explain the variation that has been identified in the dependent variable? Do the findings make sense?
External validity/Transferability: explores generalisability at different levels, i.e. to the population (exploring sample size and sampling), analysis to theory (the extent to which the findings inform theory), or case to case, or if guiding principles can be used in other contexts.
Ecological validity: Deals with making sure that the findings are applicable to people’s everyday life, in their natural social settings.
(Adapted from Bryman, 2016; Miles & Huberman, 1994)
Internal validity and reliability
While the questionnaire was pretested for ecological validity, further validations were made. The section below describes internal validity and reliability in relation to content analysis (CA) and thematic analysis (TA) and statistical tests performed.
According to Braun and Clarke (2012), the difference between TA and CA is that where TA relies on transparency to justify reliability, CA relies on interrater reliability. However, as TA promotes the researcher’s subjective interpretation, it may raise questions on reliability. It has been suggested (Braun & Clarke, 2013, 2006, 2012) that a quality aspect of TA is reflected when the researcher’s subjective interpretation is valued. For each iteration of analysis, the researcher sees more, understands more and discovers more of the data, and together with knowledge and experience, an increased understanding is developed (Miles & Huberman, 1994). Such in-depth understanding adds significant value to the analysis. Thus, reliability, in qualitative approaches of TA is not sought in replication, but in the transparency of rigorous application of scientific
45 methods (Braun & Clarke, 2013, 2006, 2012). In substudy III, data were analysed using CA (Gilbert, Jackson, & di Gregorio, 2014), and calculated Cohen’s kappa to support reliability (Cohen, 1960). Cohen proposes that kappa values indicate agreement, and moderate agreement is reflected in values between 0.41 and 0.60, substantial agreement is reflected in values between 0.61 and 0.80, and an almost perfect agreement is reflected in values between 0.81 and 1.00. In substudy III, interrater reliability for students’ reported reasons to engage in learning activities was calculated as κ = 0.6, and students’ reported reasons to complete the assignments as κ = 0.7.
In substudies I and III‒V, Likert scales were used to collect data. There is a 50-year- long ongoing debate on requirements when using Likert scale data as interval instead of ordinal (Carifio & Perla, 2008). Briefly, the debate revolves around the notion of whether the distances between the measured positions can be considered equal or not (Carifio & Perla, 2008; Kerlinger & Lee, 2000). Providing guidelines on social and behavioural research, Kerlinger and Lee state that social and behavioural sciences that make use of scales can “obtain measurements that follow a normal distribution”, and if the measuring instrument does this, “it is considered a good one from a measurement (scaling) point of view”, i.e. being used as interval data (Kerlinger & Lee, 2000, p. 634). Likert scales are heavily employed within the field of social sciences (Bryman, 2016), and the numerous publications using Likert scales as interval data have provided empirically valid results, which supports the position that Likert scales produce interval data (Carifio & Perla, 2008). In engagement and disengagement studies, self-reports (building on Likert scales) are widely used as they allow insights into unobservable data, and enable discussion and comparison of engagement and disengagement items, instruments, scales, models, theories and findings (for reviews on engagement, see, for example, Alrashidi et al., 2016; Christenson & Reschly, 2012; Fredricks et al., 2004; Henrie et al., 2015). This thesis follows these established traditions within engagement research. In substudies IV and V, adequacy in terms of sample size and requirements for normal distribution followed directions proposed by Bartlett, Kotrlik, and Higgins (2001). In substudy IV, traditional statistical methods were employed to test hypotheses (independent t-tests and correlation tests), along with a regression model, which further reflects the accountability of the study (Kerlinger & Lee, 2000). Substudy V details the development of the LET instrument that measures engagement and disengagement in TEL. While most instruments that are used to measure engagement are not validated (Fredricks & McColskey, 2012), the present instrument was validated using principal component factor analysis and confirmatory factor analysis. The Kaiser–Meyer–Olkin test and Bartlett’s test of sphericity were used to confirm the appropriateness of factor analysis (Field, 2018).
External validity
The substudies reflect the everyday use of digital technologies in upper secondary school, teacher orchestration and design of learning activities and observed and reported engagement and disengagement. While no educational situation is ever an exact replica of another, transferability may be limited, but guiding principles may be informative. The findings and guiding principles, derived from this research are likely to be identified
46 in other upper secondary schools in countries that utilise the aforementioned digital technologies. (Limitations are brought up in Chapter 5.)
Ethical Considerations
The research follows recommendations made by the Swedish Research Council (Hermerén, 2011). All studies adhered to the Swedish Data Protection Act, which, for example, regulates the individual’s rights to anonymity, to withdraw data, and share and store data (The Swedish Data Protection Authority, n.d.). In all studies, written informed consent was gathered from all respondents, and no incentive was given (Hermerén, 2011; Oliver, 2003). Having informed the respondents that all participation in the studies are based on the principle of free will; the respondents received informed consent in writing. The informed consent always stated the purpose of the study, forms of data collection, process and use of data, respect of anonymity and the right to withdraw at any time without questions asked, alongside contact details (see Appendix A and B). For example, it was clarified that grades would be collected and grouped 1, 2, 3 on the basis of an average value, and then survey responses would be linked to different groups, to avoid identification. Data were treated in pseudonymised form at all times to enable participants to ask to have their data removed. To enable identification of potential requests for data removal, separate physical codebooks were used to replace actual names with pseudonyms. The codebooks were locked in a cabinet at the department. To date, no respondent has asked to be removed. Consideration in relation to the sharing and storing of digital data followed recommendations made by Ess (2016). The digital data were only used in the necessary applications and not spread in digital form. Once the data analysis was done, the data were transferred to a (separate) hard drive and backup files were deleted.
The European data protection law, GDPR, was not active at the time of the initiated research. However, the research design and consent form for studies III and IV were discussed in depth with the institution’s legal advisor David Palmqvist. While ethical considerations underpinned all substudies, certain situations were identified as potential risk areas where a respondent’s dignity or integrity needed special attention, such as ethical considerations related to an individual’s human rights. A researcher must ensure the respondents’ dignity, integrity and social position are respected and protected under for example interviews and observations (Miles & Huberman, 1994). Such considerations continued when writing up the manuscript: for instance, by ensuring that pseudonyms that may be associated with positive or negative (cultural) connotations were not used.
47
48 Chapter 4. Results
This chapter presents and elaborates on the findings and ends with a summary of the results. ______
Substudy I
Aim To explore how digital technologies are used in a classroom over five school days Method/ Mixed methods: observations, student diaries Data collection Analysis Thematic analysis and triangulation Results Engagement in TEL is hard to observe. Student engagement varies and seems related to the orchestration of digital technologies. There are factors that may promote or hinder engagement. Conclusion It seemed technologies were mostly not orchestrated with student engagement or disengagement in mind.
Substudy I: The use of learning technologies and student engagement in learning activities (Bergdahl et al., 2018a) aimed to explore engagement in learning through observations and student self-reports. Initially, this study set out to understand how engagement in TEL could be manifested cognitively, emotionally and behaviourally. However, observing engagement was hard when students sat behind their laptop screens. Looking over the students’ shoulders in order to view their screens would be intrusive and unmanageable. Instead of searching for individual indicators of engagement, a dedicated schema was developed to observe how many of the respondents were engaged and the duration of their engagement and another to observe how digital technologies were orchestrated and learning activities designed for learning. Such information is pragmatic, as a teacher’s mission is first and foremost to engage the whole class (engagement research generally addresses indicators of high-quality engagement, not the duration or the fluctuation of engagement in a group of students). Results revealed that student engagement varied throughout the school day. It typically dipped after lunch, but no other time-related pattern was seen. Instead, the main findings indicate that engagement was related to the orchestration of digital technologies. On several occasions the design and orchestration promoted disengagement ‒ when, for
49 example, the device that the teacher instructed students to use (for groupwork) could only be used by one student at a time, leaving little room for other students in the group to engage, regardless of their engagement aptitude, or when there were plenty of devices, but instructions demanded that a representative of the group did the work on behalf of the other group members (post your PowerPoint, edit a movie et cetera), making the rest of the students in the group passive and leaving their devices unused. Third, it was observed that students would use headphones with music, but potentially with different implications. Student A reported that he wanted to direct his attention toward music during classes, and Student B used his headphones as a way to signal that he did not want to be disturbed while working. While the former was seen to hinder the student from disturbing others, it was also seen to lead to mindless and mechanical production (shallow engagement), and repeated interaction with the mobile phone to change music. While this requires more research, it indicates that listening to music during lessons might not be beneficial for all students’ engagement in learning. In order not to misinterpret observations, all students were approached between each lesson, when their diaries were compared with the observations. If the data did not match, we reflected together. A combination of student diaries, observations and mutual reflections informed these insights.
Observation of learning activities
Learning activities A‒C below were observed. The green boxes represent data sources, the red boxes reflect the objects that make up the BL experience. The yellow boxes represent students, and the arrows students’ ongoing interaction.
Learning activity A: Teacher presents instructions Figure 8 illustrates learning activity A3, in which the teachers give instructions and use resources and criteria that students can access online or on the learning platform. The learning sequence is as follows: the teacher gives instruction in class. Instructions was delivered using technologies (laptop/tablet and projector) using an application for text or slides. The teacher showed where to find learning criteria, instructions and resources (used Figure 8. Learning Activity A. Presentation. presentations, online sites, video instructions).
3 The dotted lines reflect the different resources in the LMS that are pointed to.
50 Learning activity B: Individual work
Figure 9 demonstrates the case of a student who receives and executes an instruction. The focus is on the outcome, not on the learning process. Learning activity B shows a learning activity in which the students, initiated by an instruction, “read a book”/ “research this topic”. Each student used a laptop (and sometimes their private mobile phone) to assist their learning. The devices were used as reference applications, i.e. to access forums on the Internet to read up on the topic, or for translation or dictionaries for individual language support. The laptop was used to write a report, which was saved locally or using cloud services, and later, often, present F2F, using PowerPoint in the classroom.
Figure 9. Learning Activity B. Individual work.
The design of TEL activities as reflected in figure 9 enabled a focus on shallow engagement. Despite promoting individual work, it did not foster higher-order thinking. This type of learning activity was the most common.
Learning activity C: Group collaboration
In learning activity C, the class was instructed to assemble a robot and join a robot racing competition. The idea was to trigger interest by using elements of competition, novelty and creation, the teacher explained. Interestingly, the behavioural engagement in groups 1 and 2 was similar for students A and D; that is, both groups had one student who actively took responsibility for executing the task (group 1 student A, group 2 student D). These students demonstrated that they had the knowledge to use multiple devices proactively for learning. Whether the other groups had these skills remained unknown, as the other students were more passive. Even though they had access to their laptop, they did not use it. In group 1, student B watched students A’s efforts, and tried to search for solutions on the Internet to provide input (1), but did not physically interact with the robot (neither assembling nor programming), but took on the role of assisting student A. Student C initially sat with the group without taking part in the discussions or reflections and shortly withdrew from the group physically.
51 Figure 104 visualises two group collaborations ‒ how group 1 and group 2 interpret and carry out teacher instruction.
Group 1 Group 2
Figure 10. Learning Activity C. Robot programming.
Notably, in group 2, students E‒H did not engage in the learning activity; instead they shared their mobile phone screens, folded paper planes, chatted, laughed loudly and walked around in the classroom. While stimulating interest, the actual opportunities for four or five students to simultaneously assemble, and program, one robot turned out to be limited. In the above learning activity, the robot can be regarded as a novel tool, which supposedly may trigger high intrinsic motivation (interest), but the way it was used in this situation did not facilitate engagement for more than one student at a time, leaving most other students in the group disengaged.
Engagement increased as a variation of learning activities and interactions in combination with the teacher’s social presence online was offered. More specifically, when the teacher paid attention to the students and their productions and used the digital technologies to gain insight into the students’ learning processes, the students responded by caring about themselves and their contributions. The orchestrations found to promote student engagement were: creating a visualisation of students’ learning process, in which all students’ participation is requested and noted, and the learning process is scaffolded. Interestingly, the ways in which digital technologies were orchestrated for learning varied more among the teachers than the subjects taught. Only one teacher was able to design TEL activities that required all students’ active participation as well as
4 The dotted lines reflect disengagement at the group level; four students gave up trying to engage, and spent their time doing non-school-related activities.
52 allowing the teacher to be socially present online. This finding suggests that many teachers do not always know how to orchestrate digital technologies to facilitate, even less increase, student engagement in TEL. In combination with being positioned behind the screen, in a traditionally set-up classroom, it was observed that, without the teacher being present online (where the students were), the teacher would not know if the students had initiated a learning process at all, or if they were merely rewriting Internet findings, or were using their devices in unauthorised ways.
Substudy II
Aim To collaboratively design with teachers to increase student engagement in technology-enhanced learning Method/ Design-based research-inspired intervention: workshops, dialogues Data collection and iteration process Analysis Design process analysis and thematic analysis Results Design principles that influenced engagement: • Ensuring all students have access to the device in question • Ensuring all students are active and cognitively challenged • Providing timely feedback and scaffolding to students • Ensuring there is a variation of learning activities and interactions • Directing subject-related online and/or F2F interaction • Enabling teachers to gain real-time insight into students’ learning process
Conclusion Using DBR and visualisation (problem‒solution matrix) it is possible to design for non-tangible artefacts such as engagement. But without support, teachers find it hard to keep up the practice. Results identify principles can support the design, and the teacher – researcher collaboration.
The DBR intervention: Designing for Engagement in TEL – a Teacher Researcher Collaboration, (Bergdahl et al., 2018b) aimed to increase student engagement in TEL. The collaboration with teachers was informed by substudy I, teachers’ understanding of student engagement and engagement theory (Fredricks et al., 2004). During a workshop, the teachers and researcher elaborated on engagement, and verbalised student hindrances to engagement, identified as entities reflecting problems in (what is here to refer to as) a “problem‒solution matrix” (Table 4). The problem‒solution matrix was used to systematically evaluate the iterations throughout the intervention. The way to increase engagement was to identify what hindered students from actively participating in the learning activities. In-class observations were made in each phase the implementation of the learning design, so as to enable a teacher-researcher dialogue on
53 each entry in the problem‒solution matrix and to evaluate these throughout all iterations. If the perceived level of engagement was not regarded as adequate in the evaluation, adjustments were made to the learning design during the evaluation meeting or ad hoc in the classroom (for example, the teacher adjusted the duration of mini-learning activities, as the working pace was found to differ between the two classes).
Table 4. The problem–solution matrix Hindering engagement Suggested solution Thought to be addressed by
Students struggle with Limit the time of the learning – Learner assessment application concentration and easily become activity. Make the learning using mini-test: SocrativeTM. distracted by friends and mobile activity structured. The relevance – Using the forum function in a phones. of learning could be met if VLE: Google ClassroomTM, students could read peer for polysynchronous interaction. postings.
Students have no goal in their Engagement of their peers is VLE – as above studies. made visible. Having peers who engage can inspire others to engage.
Learning activity is not See what peers contribute that VLE – as above considered fun. can be fun/relevant/meaningful
Relevance of knowledge: Relevant terms are tested in a Assessment application – as students do not agree that the mini-test. Students are both above, VLE – as above knowledge is relevant, relates to contributors and readers of real- curricula, goals or everyday life. time postings in the VLE, which make the contributions relevant
Students are primarily motivated Seeing how peers and high- VLE – as above by grades. achieving students think and plan their studies can inspire students who yet do not set long-term goals
(adapted from Bergdahl et al., 2018b, p. 104)
One of the main results in the DBR process concerned certain design principles, seen to influence the level of student engagement:
• Ensuring all students have access to the device in question • Ensuring all students are active and cognitively challenged • Providing timely feedback and scaffolding to students • Ensuring there is a variation of learning activities and interactions • Directing subject-related online and/or F2F interaction • Enabling teachers to gain real-time insight into students’ learning process
The interactions enabled the teacher to gain insight into students’ knowledge and learning process in two main ways: first, via a digital pre-test ‒ the test gave immediate feedback on students’ level of knowledge to both teacher and students and
54 allowed the teacher to set the level for presenting the content; second, the teacher used an additional tablet, in which insights into students’ contribution were enabled.
Visualisations of learning activities D‒E are present below. The green boxes represent data sources/repositories, the red boxes identify the objects that enable the BL experience. The yellow boxes represent the student, and the lines from the student, students’ active participation.
Learning activity D: Networking and publishing
Students used the discussion forum to publish and edit their contributions, receive feedback from peers, read what other students had written, provide feedback and engage in discussions (Figure 11). In Learning activity D, a forum is used (could be any learning platform or VLE that enables communication). Combined with a sequence of instructions provided by the teacher 1), the students 2) use digital technologies and online resources to gather facts for their argumentation, post their draft argument in the forum, see a peer contribution and comment on it. At the same time another student comments on it, and a thread develops in which there is a learning-centred dialogue.
Asynchronous dialogues, like commenting on posts after the lesson is over, or from another location, were not utilised at this point. However, some students brought the interaction into the F2F classroom. Thus, the students interact in several ways: as publishers, as peers providing and receiving responses, and as classmates informing dialogue in 1 the physical classroom. This 2 signalled that an intellectual contribution was regarded as relevant to address in the F2F classroom. It was seen to function as a means to receive praise and social recognition. Simultaneously, the students who received no peer feedback (negative feedback was not shared during these observations) also consolidated Figure 11. Learning Activity D. Design using the forum their social position (and function of an LMS. exclusion).
Learning activity E: Instant assessment Figure 12 reflects interactions when students used an assessment application to conduct a mini-test and received an automatically generated response. In learning activity E, Socrative was used to pre-test students’ general knowledge before initiating a new subject. The teacher had prepared the questions (1). After answering the questions, automatic feedback was given to the students and the teacher (2). This increased the teacher’s insight into students’ understanding pre-introduction and prepared the students
55 (3). Assessments would also allow include 21 additional (anonymous) questions, lesson comments and feedback from students, thereby potentially providing teachers with 1 more answers than that of general knowledge. Critical findings were identified. First, it was revealed that, although initially sceptical, the teacher worked to bridge the gap between the 31 online and analogue learning settings, when realising that immediate access to student contribution enabled timely feedback and scaffolding, directed to individuals, pairs or groups of students. Second, the students seemed to turn automatically to the Internet to find solutions. When the instructions Figure 12. Learning Activity E. An assessment changed, asking students for their own application is used. contributions, reading and posting and commenting on their peers, some students were hesitant about revealing their level of knowledge to both peers and the teacher. This showed that they had adapted to working in isolation. The teachers agreed that students’ self-assessment and other types of expressions could also lessen the (emotional) cost of failure for some students. The “new” ways of working allowed students to follow and view their peers’ contributions, and decide on a goal that would satisfy their own ambition in relation to their own (and others’) production. After having directed the students to read and comment on each other’s contributions, the teacher perceived the students to be highly engaged, and also more eager to conceptualise and reconceptualise their understanding (as suggested in Laurillard’s conversational framework; see Laurillard 2013). Thus, asking all students to contribute was a functioning way to engage all students, automatically relevant (as the information and thoughts came from their peers) and matched the class overall level of understanding (which cannot be guaranteed when online materials, not developed for learning, are used). It initiated content-focused dialogues that benefited all students’ understanding of the genre and working process. Afterwards, some of the students (those who were known to need support the most) told the teacher that working this way enabled them to understand and follow instructions, and engage in depth in learning. Conversely, some of the highest performers expressed that “it was ok”, but they felt hindered by working in the same pace as the others in the class. The dialogues, which replaced the previous isolated work where students were instructed to look things up online, could have been further developed to reach higher levels of critical thinking, and a higher quality of feedback. (Even though the teacher showed some examples of students’ high-quality feedback to peers, many students did not indicate that they knew how to provide productive feedback to peers.) In addition, more thoughts on personalisation could have been included to cater for the high performers.
56 The teacher had not received any professional training with regard to IT didactics prior to, or during, the intervention. Nevertheless, there was an expectation (inherent in the digitalisation of education) that the teachers should design and implement TEL activities. From time to time, the teacher was overwhelmed when facing the complexity of digital technologies. Problem areas included time constraints for lesson planning, managing settings in the applications, ensuring technologies would work smoothly together (tablet, laptop, projector, LMS, assessment applications), insecurities on how to bridge the gap between technologies and the F2F setting, not knowing how to support students with technology problems and limited ideas on what to do when a student finished unexpectedly fast. In the end, design principles, which could be used when transferring the intervention between schools, or implementing another TEL intervention, were derived. These included: “exploring theories of engagement in combination with teacher experience”, “preparing the teacher to embrace the different affordances of the learning technologies at hand” and addressing the possibility that “not negotiated actions undertaken spontaneously by teachers may cause the intervention to drift off course”. In collaboration, it was also found that revisiting the problem–solution matrix was helpful, as it visualised the collaboratively created design ideas and ways forward.
Substudy III
Aim To explore students’ reasons to engage during the intervention
Method/ Design-based research: evaluation questionnaire Data collection Analysis Descriptive and inferential statistics, content analysis Results Social dimensions of engagement were the most common reasons to engage in TEL, and was the only dimension represent across all engagement dispositions. ‘Interest’ was only reported by students reporting being highly engaged. Conclusion ”One size”-solutions - do not fit all. TEL. Increased awareness of individual students’ needs and preferences can inform personalisation. An early indicator that “social interactions” may drive engagement more than “interest”.
‘So, You Think It’s Good’ – Reasons Students Engage When Learning with Technologies – a Student Perspective (Bergdahl et al., 2018c) reports on students’ perceptions of the DBR intervention, more specifically their level of engagement and their reasons to engage in learning. Results revealed that students engage for a number of different reasons (see Figure 13). Figure 13 provides an overview of reasons students engaged in short-term learning activities. The main findings show that all students, regardless of engagement disposition, pointed to social aspects as reasons to engage. When
57 interacting, students became seen, could confirm the quality of their work and feel content with their contribution, as well as acknowledge the contribution of others.
Noteworthily, interest is often viewed as essential for in intrinsic motivation, thought to drive engagement in learning (e.g. Skinner et al., 2009). In this study, reflecting student self-reported reasons to engage, interest only came in shared fourth place when students work with a limited learning activity (see Figure 13), but came in last when reporting on reasons to complete their whole assignment (see Figure 14), even though students were free to choose topics reflecting their personal interest.
In a wider perspective, the finding may be an early indicator of consequences in a digitalised society, which offer an abundance of online resources, but might foster fewer social interactions F2F. Findings also suggests that when human-to-human interaction and social needs are not met (e.g. Maslow’s’ hierarchy of needs; Maslow, 1943), these might be more essential to meet than “interest”. It was also noted that many students did not have the skills to provide high quality feedback. Moreover, it was observed that the teacher’s feedback instruction did not refer to the quality of feedback given. Potentially both teachers and students need to develop their skills in providing effective feedback. Such a requirement could have improved the quality of the feedback needed for critical thinking, and signalled that the teacher expects higher-order and have high expectations on students’ contributions. Without doubt, such signals should not be underestimated, as they foster the unspoken student-teacher contract in important ways.
Students reported reasons to engage in learning activities
Feeling content with ones outcomes so far Using the VLE for dialogues Grades Interest It was compulsory The purpose Element of competition Getting into doing the work needed New and exciting I want to be right It was easy Wanting to be satisfied with the end product Reported reasons to engage I have a disposition to complete all tasks I got to use my creativity Stubbornness Wanting to complete the task
0 2 4 6 8 10 12 14 Number of responses (more than one was possible)
Figure 13. Student-reported reasons to engage in learning activities.
(Bergdahl et al., 2018c)
58 The study also approached the difference of applied engagement in short- and long-term goals. Results revealed that highly engaged students will work toward both short- and long-term goals, but all students, regardless of engagement disposition, will engage in learning activities with short-term goals (see Figure 14). Figure 14 shows that the reasons students engaged, when working toward short- and long-term goals, were different. Importantly, one reason seemed to trigger engagement for all students, regardless of engagement disposition. This was when students had the opportunity to break up long-term assignments in tasks. This finding suggests that helping students to break up long-term goals in sections may support students’ engagement at the class level.
Students reported reasons to complete the assignment and levels of engagement
Task-oriented Process-oriented Grade Feeling included Wanting to Refer to personality Needing to Interest Reported reasons to engage 0 2 4 6 8 10 12 Number of students and their reported level of engagement
1 2 3 4 5
Figure 14. Reasons to complete the assignment across reported engagement levels.
(Bergdahl et al., 2018c)
The different colours in figure 14 reflect students reported level of engagement. The figure thus reflects the number of students within each level of engagement that reported a specific reason to engage in completing the assignment as a whole.
While results also confirmed that high levels of engagement were related to the effort students directed toward learning, they also showed that the extent to which students would “do more than required” had to do with the complexity of the task. If the task was not challenging, students with medium or low engagement levels would engage in “doing more”. The TEL activities were designed to become progressively more challenging. When tasks became more challenging, the number of students who did more than required across all engagement dispositions decreased. “Doing more than required” is elaborated on in more detail in the discussion (see Chapter 5).
59 Substudy IV
Aim To explore the role of digital skills for engagement and disengagement in TEL Method/ Mixed methods: interviews, questionnaire and grades Data collection Analysis Descriptive statistics, Spearman’s rank correlation and regression analysis Results The levels of general engagement were related to similar levels of engagement in TEL. Students’ digital skills were related to how they engage in, but not how they disengage from, TEL. Specific indicators could explain 17% of the variance in the students’ final grades. Conclusion Engagement in TEL differs from engagement in analogue settings, and it differs between student groups. IT skills are important for engagement in TEL, but cannot redeem disengagement.
The study ‘Disengagement, Engagement and Digital Skills in Technology-Enhanced Learning’ (Bergdahl et al. 2019a) explored how students’ high and low digital skills were related to their engagement and disengagement in TEL, and if some of these indicators could be used to predict performance. The main results revealed that students’ IT skills were related to how students engage in learning when using digital technologies, but that owning IT skills was not sufficient to redeem student disengagement. The ways of engaging with digital technologies productively for learning were significantly more common among students who felt they had the skills needed. For example, students with “high levels of digital skills can and do take the initiative to use digital technologies for learning, can and do concentrate easily when working with technologies, and can and do switch between digital technologies to increase their engagement” and so on (Bergdahl et al., 2019a). Importantly, the results show that there was no significant difference between students’ high and low levels of digital skills and their disengagement. Thus, schools can take measures to provide sufficient IT skills as this supports proactive use of digital technologies for learning, but still be aware that IT skills might not be enough to redeem disengagement.
The second aim of the study was to explore the relation between engagement and disengagement in TEL and school performance (as measured by grades). In this regression analysis, grades were used as the dependent variable, and indicators reflecting behavioural, cognitive, emotional and social dimensions of engagement and disengagement in TEL were used as independent variables (Wang et al., 2017). By offering insights into the relationship between indicators, a regression analysis reflects underlying structures. As hypothesised, the results revealed that specific indicators could explain 17% of the variance in the students’ final grades. Variance is measured to investigate the extent to which variation of one variable (grades) can be explained by
60 other variables (engagement and disengagement indicators). Ten significant indicators were identified (see Table 5).
Table 5. Regression analysis of engagement and disengagement in relation to school grades
Unstandardised Standardised Coefficients Coefficients Beta Std.Err. Beta t p (Constant) 2.03 0.43 na 4.69 0.000 My absenteeism would decrease if I could attend class from elsewhere -0.10 0.02 −0.19 -4.12 0.000 Working alone decreases my engagement -0.10 0.03 −0.15 -3.05 0.002 It upsets me that one student often does more than 0.07 0.03 0.10 2.10 0.036 the rest in the group The most capable student does the work on behalf of 0.07 0.03 0.11 2.24 0.025 the group I routinely search the Internet 0.09 0.04 0.10 2.15 0.032 I don’t want to read everything from a laptop 0.08 0.03 0.14 2.89 0.004 My teacher takes part within applications that I use 0.10 0.04 0.12 2.60 0.010 I use IT to support my learning 0.19 0.06 0.19 3.34 0.001 I often switch between playing games/browsing/social media/streaming YouTube -0.07 0.03 −0.10 -2.04 0.045 (or similar) and learning in class Autonomously switching between different technologies when doing schoolwork increases my -0.09 0.04 −0.11 -2.01 0.042 engagement
(Bergdahl et al., 2019b)
Analysing the results, five factors that predict grades were subsequently grouped into three themes for engagement and disengagement, respectively: engagement: (i) learning-oriented technology switching, (ii) technologies to support learning, and (iii) social presence. And when exploring disengagement: (i) non-learning-oriented technology switching, (ii) lack of a digital balance, and (iii) unsatisfactory learning interactions. Noteworthily, it was not the actual “switching between digital technologies” that was problematic but the underlying purpose: students who displayed learning-oriented technology switching had higher grades, while students who used technologies to escape from learning had lower grades. Hence it is neither the digital technologies (mobile phones, for example) nor the switching itself that account for good learning outcomes, but the focus of the attention (e.g. Firat, 2013). Another particularly interesting finding was that disengaged students are not immune to engagement triggers (and vice versa). With these insights, the LET instrument was further developed and tested in substudy V.
61 Substudy V
Aim To develop and test an instrument to explore engagement and disengagement in TEL for low-average and high-performance groups Method/ Mixed methods: interviews, questionnaire and grades Data collection Analysis Thematic analysis, descriptive and inferential statistics Results Using IT to support learning, finding it easy to concentrate was significantly higher for the high performers than other students. Average and low performers used digital technologies to escape significantly more than high performers when the lesson was perceived as boring. The higher the student’s grade, the more likely they were to be upset or frustrated when engaging in collaborative TEL. Conclusion The use of technology, particularly for low-performance students, is not unproblematic. Students’ needs and preferences differ.
In Engagement and Performance when Learning with Technologies in Upper Secondary School (Bergdahl et al., 2019b), the Learner-Technology-Engagement instrument (LET) was further tested and validated. Statistical measures were used to explore low-, average and high-performance students (as measured by year-end grades) and students’ academic engagement and disengagement in TEL. This study combined relevant theories and students’ experiences of being engaged and disengaged, and subsequently analysed results. The main findings reveal that high, average and low-performing students engage and disengage differently in TEL.
The substudy also approached the time student spent gaming, using social media and watching YouTube in relation to their school performance. While there were no significant correlations between student performance and the time students reported gaming, the location mattered: playing games during class (e.g. switching between playing games and focusing on learning) was significantly correlated with low grades. Social media use and time spent on YouTube were also related to grades: the more time that students reported having spent on these, the lower their grades were. Thus, it was found that both average-and low- performers tended to turn to digital technologies to escape learning (the lower the grades, the more frequently students reported using digital technologies to escape when lessons were boring). Low-performers were significantly more likely than students in the high-performance group to report that they handed in their school work late due to their uses of digital technologies.
The LET instrument was validated using factor analysis to confirm structure validity. It revealed a number of significant indicators of engagement and disengagement in TEL: for example, using digital technologies to support learning, working on school assignments, feeling satisfied with their social interaction, perceiving digital
62 technologies as useful and necessary to maximise learning and wanting to use them more. Indicators like “finding it easy to concentrate with digital technologies”, and using these as “a cognitive enhancement” was identified to reflect the cognitive dimension. The engagement variables “finding it easy to concentrate with digital technologies” and using these as “a cognitive enhancement”5 relate to previous research that has shown that enhanced cognition is desirable in general (Heersmink, 2016), as “enhancement of our cognitive capacities increases their instrumental value by enabling us to solve more difficult problems”6 (Agar, 2013, p. 26).
On the other hand, the LET instrument also included disengagement in TEL, and revealed significant indicators. For example, student disengagement was seen to range from actual physical limitation in using digital technologies to distractions, being left alone to manage digital technologies or repeatedly being directed to look thing up on the Internet to learn, unauthorised uses, using digital technologies to escape class, not being able to resist the pull from the digital technologies even when being late with schoolwork, and TEL designs that do not engage all students in groupwork. As has been pointed out, schools have underperformed in supporting at-risk students and taking preventive measures to tackle dropout rates (The Swedish School Inspectorate, 2015): almost none of the schools met standards when it came to tackling disengagement. While more research is needed, it is believed that the LET instrument could support schools in investigating causes of student absence and/or the need for support (The Swedish School Inspectorate, 2015), and thereby influence student engagement. While some indicators may be generic, certain indicators also reflect a specific context, e.g. the maturity of the implementation and uses of digital technologies. While proven to be relatively stable constructs (Alrashidi et al., 2016; Boekaerts, 2016), engagement and disengagement are shaped by context (Wang & Hofkens, 2019).
For an overview of the indicators of engagement and disengagement used in the LET instrument, and how they were informed by theory and empiric findings, see Appendix C.
5 Here, cognitive enhancement refers to, for example, using devices for memory support when managing and working on school assignments and projects, i.e. planning, scheduling, keeping different versions available (e.g. Richard, 2016).
6 Keeping several different worksheets open, using several devices to access several online resources, like programming codes, synonyms, translations, grade requirements, and more. Memorising, or writing things down using pen and paper, could be regarded as opposites (Heersmink, 2016).
63
64 Chapter 5. Discussion, Conclusions and Contributions
This chapter discusses key results and concludes the thesis. The chapter ends by suggesting some implications for the future and then summarises the contributions of the present research. ______
Discussion
Two overarching questions guided this thesis: “How can teachers facilitate upper secondary school student academic engagement when learning with digital technologies?” and “How can we raise awareness of upper secondary school student disengagement when learning with technologies?“. This thesis argues that in order for teachers to be able to facilitate upper secondary school student academic engagement when learning with digital technologies, teachers need to be aware of manifestations of both engagement and disengagement in TEL and design principles and guiding principles that may inform orchestrations of digital technologies and designs of learning activities. However, to be able to raise awareness of upper secondary school student disengagement in TEL, both engagement and disengagement in TEL must first be explored. This thesis has approached upper secondary school student engagement using a multi-dimensional conceptualisation consisting of a behavioural, a cognitive, an emotional and a social dimension. Central results reflecting these are discussed below, after which disengagement and design principles are discussed.
Engagement and disengagement in TEL
The behavioural dimension
One approach (in substudy III) was to gather students’ self-reported engagement and use multiple questions to survey the students’ effort in three progressively more challenging learning activities. More precisely, sub-study III used direct and indirect questions to survey whether students had done more than required or not. Unsurprisingly, the results revealed that students with low levels of engagement chose to opt-out from “doing more than required”. Noteworthily, otherwise highly engaged students also chose to not “do more than required” as the learning activities became more challenging. “Doing more than required” was proposed as a variable to reflect engagement in Finn’s “participation identification model” (Finn, 1989). In this model,
65 Finn describes how specific pro-learning behaviours impact the probability of academic success reflected as: “doing more than the minimally required work”. “Doing more than required” has continued to be used as a variable to reflect engagement since then (e.g. Skinner et al., 2009, Fredricks et al., 2004). More recently, when prompting students with this as a direct question, researchers have found that students might perceive it to reflect strategies used when balancing multiple demands instead of engagement, why researchers have excluded the variable from their questionnaire (Wang et al., 2017). It seems that the variable derives from a time when engagement was commonly explored on one single continuum, in which high and low levels of engagement were approached. In research viewing engagement and disengagement on separate continua, it is therefore critical to consider if ”doing the bare minimum”, or ”doing more than what is required in school”, really should be translated into disengagement and engagement (e.g. Fredricks et al., 2016) or excluded (e.g. Wang et al., 2017). An analysis of the results (in substudy III) revealed that ”doing more than required in school work” was associated only with students who had reported high or very high levels of engagement. Thus, this variable seems to demand very high levels of engagement, which in this study, only a few students would display. Critically considering the use of this particular variable may be particularly important as researches have proposed that many students today display “indifference” to learning (Hietajärvi et al., 2015). Indeed, questions have been raised with regards to when the strive to increase engagement pushes students toward burn out: that the level of engagement is no longer adaptive but maladaptive (Christenson & Reschly, 2012). Against this background, the thesis raises the questions: why completing the expectations within the allocated time frame should not be considered to reflect engagement in learning; at what stage a satisfactory level of engagement has been reached, and; what level of engagement that is actually needed in order for students to succeed. These questions may be explored by future research. Thus, informed by interviews, in substudy V engagement indicators include “use digital technologies to support learning” and for “working on school assignments”, indicators which were also tested and validated (substudy V).
However, students’ effort in learning is not only linked with the level of difficulty in a task. Results also revealed that students’ IT skills also were related to how students engage in learning when using digital technologies. The ways of engaging with digital technologies productively for learning were significantly more common among students who reported that they had the IT skills needed. For example, students with “high levels of digital skills can and do take the initiative to use digital technologies for learning” (substudy IV). Expanding on reports on students IT skills, which have revealed that there are differences between the IT skills amongst upper secondary school students that contribute to digital exclusion (Hatlevik et al., 2015), and that almost half of the students (who do not own any devices) report not having the IT skills needed to succeed in school (Internetstiftelsen, 2016), these findings suggest that engagement disposition may not be sufficient if students do not own the IT skills needed to proactively engage in learning. Interestingly, there was no significant correlation between disengagement and student IT skills. Thus, owning IT skills was not sufficient to redeem student disengagement. Disengagement is discussed in more detail below.
66 The cognitive dimension
Nevertheless, the results show that high-performers could use a range of digital technologies to meet one educational purpose. Substudy IV refers to this as learning- oriented technology switching, and expands on the findings of Firat, (2013), who proposed that students display either fragmented and distributed attention (which meant that students could not remain focused, ibid., 269) and other research, (Lin & Parsons, 2018; Ophir, Nass, & Wagner, 2009), which identified that media multi-tasking might be associated with low-performance. While continuous partial attention and multi- tasking are separate, they can be viewed as a students’ either fragmented and distributed attention (“staying focused on nothing”, ibid., 269), or intentional engagement in several simultaneous activities, (ibid.).
Cognitive disengagement has been related to how students give up or prioritise completing objectives “fast” as supposed to “well” (Fredricks, 2011; Skinner et al., 2009; Wang et al., 2017), which by Greene (2015) and Dalgarno (2014) has been referred to as shallow levels of engagement. Indeed, in substudy II, a student who switched between selecting music on his mobile phone and working on the school assignment was identified to be shifting his focus so many times that there was little room for deep engagement (Greene, 2015). On the other hand, another student, who also listened to music in his headphones, displayed a higher level of engagement. When approached the student explained that the headphones shielded him from the peers, and that this was needed to be able to concentrate in the classroom. Students were also observed to switch between using the laptop to write up a document and look up words on another device, to have multiple tabs open at the same time, and switch between these (for example, instructions on the LMS, cloud services, email and a web browser). Thus, the presented research proposes that students can focus on one single goal and use several digital technologies to achieve this goal. The difference between switching between external devices and within devices thus becomes a grey zone, if the switching itself is considered to be the problem. It is proposed here that the switching between digital technologies (separate devices), can be compared to knitting using several differently coloured yarn balls. The brief distraction needed to locate and change into another colour of yarn, to then return to the task at hand, does not impact on reaching the goal, instead, it serves the purpose to fulfil the target of the main focus. This thesis proposes that it may be important to separate non-learning-oriented technology switching from learning-oriented technology switching, and learning-oriented technology switching from multi-tasking and continuous partial attention when considering how to influence engagement.
The emotional dimension
During the observations, it was seen that teachers try to motivate students by for example offering fun and interesting learning activities, but that orchestrations of digital technologies and designs of learning activities that co-occurred with the highest levels of engagement were rare (substudy I). Research has forwarded that engagement is needed as motivation alone is not sufficient for students to persist in learning (Boekaerts, 2016; Poskitt & Gibbs, 2010) that learning often involves disconcerting feelings and
67 may be troublesome (Weitze-Laerke, 2016) and that students do not need to sit passively and wait in order to be motivated to work. Instead, they are active agents who can make a decision to engage, even without being motivated, and that this engagement, in turn, can influence their motivation (Reeve, 2012). Expanding on previous research (Poskitt & Gibbs, 2010; Reeve, 2012, Weitze-Laerke, 2016) the results in substudy I, showed that a learning activity, that was considered fun by students, (in which they also reported being motivated and engaged in the idea) did not guarantee students’ engagement in learning. Instead, students who did not engage was observed to be insecure about how to participate, which in turn led to passivity and withdrawal. Moreover, it was observed that when technologies were fit for one person at a time, and the task only required one person to work, the other students in the group would disengage (substudy I). Substudy IV revealed that using digital technologies, particularly for low performing students, may be problematic, as they may find it harder to resist the pull of digital technologies. As have been pointed out by previous research (Salmela-Aro et al., 2016) educational institutions can and should teach their students how to use digital technologies for learning, as unguided, students may use them to direct their focus away from learning. Results (in substudies I-II) indicate that thought-through orchestrations of digital technologies and designs of learning activities that ensured active participation and a variety of learning activities were seen to co-occur with all (of the observed) students’ engagement levels, leaving no student inactive.
The social dimension
All substudies indicated that social aspects of engagement are important for engagement learning and thus echo previous research (Skinner et al., 2009; Voelkl, 2012; Wang & Eccles, 2011, 2012; Wang et al., 2017). Nevertheless, social aspects have been slow to develop, partly as they are often taken for granted (Miyake & Kirschner, 2014). Social aspects of academic engagement are not merely “socialising”, nor does it promote continous collaborations. (Collaboration per se does not produce learning outcomes; it depends on the extent to which groups actually engage in productive interactions, e.g. Dillenbourg et al., 2009.) Instead, social aspects of academic engagement are concerned with the interactions aimed to support learning. The majority of the design principles proposed here (below) refer to the social dimension of engagement. As have been referred to, Laurillard proposes that in order for learning to be effective, there should be a focus on student interaction and learning-focused dialogues, as such designs would promote higher-order thinking in terms of promoting dialogue that enable students to conceptualise and reconceptualise their understanding(s) (as that described in the conversational framework; Laurillard, 2013). On the one hand, this is in line with the findings in this thesis, as well as the theories on engagement in which researchers propose that social engagement is critical for learning (Wang et al., 2017). Moreover, the social dimension of disengagement may encompass indicators such as dissociative attitudes; not caring for other students, no desire to interact with other students or not feeling noticed by people in the school, (Fredricks, 2011; Wang et al., 2017). However, contrary to Wang (ibid.). The findings in substudy I, IV and V suggest that some students prefer individual work to group work which may trigger feelings of frustration, that one student does the work on behalf of others. However, low performers are more
68 likely than high performers to experience decreased engagement when working alone (substudy IV).
While “interest” is critical for motivation in learning (e.g. Skinner et al., 2009; Wang & Eccles, 2011), and considered to be subsumed in the engagement as a meta-construct (Fredricks et al., 2004), it was not included in the LET instrument (substudies IV and V). This was because the respondents did not refer to “interest” when sharing their experiences of engagement and disengagement in TEL. Moreover, in substudy III, only two students (who also reported the highest level of engagement) stated that “interest” was their main reason to engage; instead, the majority of respondents (represented across all engagement dispositions) said that social interactions were their main reason to engage in learning. An initial explanation may be that social interactions are becoming more important for students than interest, (when exploring students’ reasons to engage in learning). If so, then this finding may, in turn, be viewed in the light of existing explanations which, for instance, have proposed that: (i) many students today are indifferent to learning (Hietajärvi et al., 2015), and/or (ii) reflect a more digitalised society in which students desire to have their (physical) social needs met during school hours (Maslow, 1943), and/or (iii) family structures that do not support the student’s schooling sufficiently (Alexander et al., 2001). However, the results in substudy IV revealed that as many as 60% of the students report that they use digital technologies to disengage in class when a lesson is perceived to be boring. Against this background, the results suggest that factors that point to biological or socio-economic reasons may not be sufficient to explain student disengagement in TEL, nor is a one-sided focus on making learning fun or interesting. Instead, there seems to be a need to combine these understandings with insights in student engagement and disengagement when designing TEL activities.
Raising awareness of disengagement
Turning to disengagement, researchers have forwarded that it is related with, yet separate from, engagement as these are different constructs (Wang et al., 2017). Disengagement is not merely “less engaged”, nor can it only be described by variables at the lower end of engagement. Previous research has often sought to explain disengagement by pointing to biological factors (Anderson et al., 2004), socio-economic factors of the family (Alexander et al., 2001), underperformance and social problems (Garcia & Cohen, 2013; Hunter-Jones, 2012), or absenteeism (Balfanz & Byrnes, 2013). Disengagement has often been explained by what is not happening, with indicators like passivity, withdrawal, truancy which may escalate into school drop-out (e.g. Balwant, 2018; Finn 1989), even though engagement researchers have begun to explore that which is happening. While this thesis makes no attempt to replace existing theories or exhaust or indicators of engagement and disengagement, it proposes that conceptualisations of engagement and disengagement may combine capturing the something, i.e. what students do when they disengage, e.g. different disruptive behaviours, with the nothing of the expected something, i.e. passivity, avoidance, procrastination, not handing in schoolwork, et cetera as reflected in TEL. As has been highlighted earlier, some expressions of disengagement may be deep-rooted and
69 manifested in maladaptive or aggressive behaviour or other manifestations of low self- esteem (Grønborg, 2013). It is naïve to think that these will be dissolved if a TEL activity is well-designed. On the other hand, if a majority of the student display disengagement, then some of the explanations for the high number of disengaged students may be due to ineffective orchestrations of digital technologies or learning designs. This thesis argues that when this is the case, some of those manifestations of disengagement may be redeemed by considering student engagement and disengagement in TEL.
The results (in substudy V) showed that high- average- and low performers interact with the digital technologies differently. Expanding on the results of Hietajärvi et al. (2019) who stated that social networking and gaming was consistently related to lower levels of engagement (but that sports and action games, and social games did not show significant associations with engagement until high school when they were negative), the findings in substudy V show that the use of social media is related to lower grades, and, as can be expected, playing games during class was significantly correlated with low grades. However, gaming outside school was not found to correlate negatively with grades. Results revealed that low performers tended to display less resistance to digital technologies; while high- and average performers would prefer either gaming, social media or YouTube; low performers were more likely to engage in all. While a majority of students (both average- and low performers) reported that they use digital technologies to escape when a lesson was perceived to be boring, results revealed that the lower the grades were, the more frequently students reported using digital technologies to escape when lessons were boring. Student disengagement was seen to range from actual physical limitations of digital technologies to distractions, being left alone to manage digital technologies or repeatedly being directed to look things up online, using digital technologies to escape class, and not being able to resist the pull from the digital technologies even when becoming late with schoolwork (substudy IV and V).
Design principles and guiding principles
This thesis proposes design principles, and the results of a large-scale study that identified entities of engagement and disengagement in TEL (see Appendix C), to inform teachers on how to facilitate upper secondary school student academic engagement when learning with digital technologies and raise awareness on disengagement. Together these may inform orchestrations of digital technologies and designs of TEL. Researchers have suggested that data, other than design principles (that emerge from an intervention), can be viewed as “guiding principles” in that “they abstract from the surface features of learning environments to help explain how they work” (Engle & Conant, 2002, p 401). In accordance with this view, guiding principles can be identified as entities when that aim to explore complex phenomenon such as those of engagement and disengagement in TEL. Together the design principles and the guiding principles are aimed to inform designs of TEL that facilitate engagement and redeem or circumvent disengagement in TEL.
70 The LET instrument, developed as a part of this thesis, suggest that upper secondary school student engagement in TEL may be reflected by, for example when students “take the initiative to use digital technologies”, “use IT to support learning”, “research what others have written” and “rehearse and master content”. Students also reported that they experienced being able to “concentrate easily when using digital technologies” that they used “IT is a cognitive enhancement”, needed IT to “maximise learning”, and that they “relied on digital technologies to manage education”. Disengagement, on the other hand, was reflected by, for example, students being “prevented to work due to undeveloped technologies and system breakdowns”, “feeling frustrated over poor communication over the learning platform” or feeling “overwhelmed by information overflow”. Disengagement in TEL also reflects negative reactions toward uses of digital technologies, for example, students who “resist using” IT, and who are “unhappy with repeatedly being directed to the Internet” [by the teacher], or experience that they are left alone to “manage digital technologies for learning” (for an overview of all indicators see Appendix C).
The results also revealed that students who are generally engaged might display disengagement indicators and vice versa; that is, students are not immune to either engagement or disengagement. This proposes that identifying variables that trigger low- performing students to engage, and vice versa, needs more research. The design principles that derived from the intervention suggested that engagement may be facilitated by:
• Ensuring all students have access to the device in question • Ensuring all students are active and cognitively challenged • Providing timely feedback and scaffolding to students • Ensuring there is a variation of learning activities and interactions • Directing subject-related online and/or F2F interaction • Enabling teachers to gain real-time insight into students’ learning process
On the other hand, certain orchestrations of digital technologies and designs of learning activities were seen to hinder engagement and/or promote disengagement. For example, when:
• Technologies were suitable for only one user at a time • There was a focus on the outcome, rather than the individual learning process • One student could do the task on behalf of other students
With regards to the focus on outcome above, it was observed that recurring practices like editing film, creating PowerPoints and programming robots often excluded most students in a group, that the active and passive students were the same, and the passive ones were not perceived as disengaged, but rather unsure of their right to express that they had a right to learn (substudies I-II). On several occasions, as long as the group handed in their schoolwork (an outcome), the teacher was unaware to what extent the individual students had engaged in learning.
71 However, the intervention revealed that not all manifestations of disengagement relate to students’ engagement dispositions or engagement profiles (cf. Salmela-Aro et al., 2016). Instead, disengagement may be consequences of the orchestrations of digital technologies and designs of learning activities. The design principles that highlight a need for variation, dialogues on learning and scaffolding are not in any way radical. Indeed, the need for digital technologies to be orchestrated in ways that promote interaction and dialogue has been well documented (Dalgarno, 2014; Laurillard, 2013). Yet, we know that teacher’s uptake, use of digital technologies, and IT didactic competence vary (Howard et al., 2016; Håkansson-Lindqvist, 2015; Samuelsson, 2014). These findings are in line with the results presented here; the complexity of orchestrations and designs of learning activities in TEL, varied more between the teachers than the specific subjects. Even though Engle and Conant’s guiding principle that “students should be provided with sufficient resources” (2002, p, 400), may not have been derived from a TEL intervention, this principle is still valid and critical when teaching and learning with digital technologies. Yet, the results presented here indicate that student engagement and disengagement might be overlooked when designing for TEL in school. Digital technologies may facilitate engagement in various ways, depending on how their different modalities are put to use. For example, in the instances when teachers did not orchestrate the learning technologies in ways that made learning process visible, it was hard to know if the student had engaged in learning at all, even when they hand in their work. However, realising all desired design principles may be hard. For example, results of this thesis suggest that teachers’ social presence online as well as in the F2F setting is desirable to influence interactions; similarly to Lindroth (2012), it was found that students could sit behind their computer screen and it was not certain that they had engaged in learning at all (substudy I). While this may be due to the teachers’ design of a learning activity, it may also be due to the digital technologies not enabling a teachers’ social presence. Teachers who want to work around this limitation are then required to find other solutions to become socially present when learning takes place online.
Limitations
In substudy I the student diaries contained a single-item measure (combined with questions) to capture engagement. The decision was made as the students filled in their diaries between each class to capture the variation of engagement across subjects and schooldays. Too many questions would have taken them too long to answer (given that the students only had five-minute breaks between some of their classes). While single- item questionnaires can never capture nuances of engagement, these have been found to be useful and consistent in engagement research (Kim et al., 2018), moreover, these were combined with dialogue and one open ended question (e.g. What made you engage to the extent that you did?”). Self-reports with multiple items to measure engagement and disengagement were used in substudies IV and V. The indicators that reflect students’ perceived engagement and disengagement in the context of TEL might not be able to capture all engagement and disengagement components. Nevertheless, self- report data have continuously proven to yield substantial valid data and the method is
72 commonly accepted within engagement research. Self-reports are practical for large- scale data gathering (Berman, 2017), as they do not interrupt the student learning process (Schellings & van Hout-Wolters, 2011) and may provide insights into students’ perception of their engagement and disengagement when learning with digital technologies. Yet, considering the complexity of engagement, no single measurement will exhaust the indicators that may be manifested across educational settings. Another limitation is the use of grades to reflect school performance (substudies IV and V). Grades are a reduction of a student’s abilities; they do not capture the student’s actual skills, knowledge or progress in mastering a subject. Nevertheless, grades are commonly used in research on engagement, which warrants the use of GPA as a shared point of reference between research, taking into consideration its limitations. Moreover, the use of similar approaches allows researchers to more easily compare findings.
In DBR, one aim is to identify design principles that can be transferred into other educational settings (Design-based Research Collective, 2003). It should however be noted that for design principles to be generalisable, they need first to be tested again (Yin, 2003). As the same culture, context and conditions vary between schools, transferability may be limited. However it remains likely that future research may reveal similar findings, with same-age students and in similar settings. Generalisations were thus made toward theory, and conclusions were drawn with regard to the orchestration of digital technologies and principles that support teacher-researcher collaboration in designing for engagement, as well as principles derived from that design that also build further on existing.
Conclusions
The thesis concludes that student engagement and disengagement may be described in ways that reflect the uses of digital technologies, to better inform practitioners and researchers how digital technologies shape or influence engagement and disengagement in learning: for example, how engagement and disengagement may be manifested by different groups of students, and how orchestrations and designs of learning activities influence engagement and disengagement. The thesis suggests that overlooking disengagement might lead to a risk of failing to uncover problematic areas that are critical for learning and without being properly informed, teachers may unwilfully design for disengagement. Finally, the thesis concludes that teachers’ social presence online, as well as in the classroom, is equally important, as by being aware of specific indicators of disengagement, teachers can design thought-through learning activities that facilitate engagement, avoid pitfalls that trigger disengagement and support the development of more productive uses of digital technologies for learning.
Implications for Future Practice
As has been pointed out, BL includes enabling, enhancing and transforming learning as mediated through the uses of digital technologies (Graham, 2006). While teachers’ IT
73 skills include having the competence to decide (i) how digital technologies are used, (ii) their social impact, and (iii) the ethics surrounding them (Law et al., 2018), without professional training, support and information, enabled by school, realising such expectations is hard, and risk pushing the vision of “transforming learning” further away. As digitalisation is spreading across educational institutions worldwide (Redecker & Punie, 2017), and research has shown that students’ engagement and disengagement in TEL are critical, yet complex matters, the thesis urges educational stakeholders to ensure teachers are inform of student engagement and disengagement in TEL. Schools should survey student engagement and disengagement, to identify effective interventions, good examples, annual progress and early indicators of disengagement. Such approach is thought to lead and benchmark best practices in TEL.
74 Contributions
A critical realism and DBR approach
Both DBR and CR promotes the adoption of mixed methods, as well as “research in the wild”, (Bhaskar, 2016; The Design-Based Research Collective, 2003). Yet, CR has not been seen to be applied in combination with DBR. In substudies I‒III a critical realist perspective was combined with the DBR approach. From a CR perspective, social sciences can be approached from a material and a conceptual level (Bhaskar, 2013, 2016). This is reflected in substudies I‒III as follows: At the material level, students approached in their setting, their reasons to engage, and experiences of their engagement and disengagement are approached. At the conceptual level: engagement and disengagement are discussed and explored as concepts that exist independently of the human, by for example exploring the phenomena as such in teacher-researcher workshops. Moreover, Bashkar suggested that to uncover aspects of our (social) world, there are three layers that may be explored: (i) the empirical ‒ through experiences, measurements and observations; (ii) the actual ‒ via exploring events, things and products; and (iii) the real ‒ by identifying and analysing generative mechanisms, causes and power structures that produce events (Bhaskar, 2013).
In line with DBR, interventions have as their goal both to develop and improve a learning activity through iterations, and to contribute to knowledge and theory development (Anderson & Shattuck, 2012). Such approach resonates well with the CR perspectives:
At the material level:
Exploring the empirical: Insights were gathered using observations and interviews.
Exploring the actual: Dedicated classifying schemes events and devices were approached. During the DBR intervention, a problem‒solution matrix was used to identify hindrances to engagement, and causes and power structures that produce engagement.
At the conceptual level:
Exploring the actual: Further critical discussion was undertaken to critically examine approaches such as student effort and “doing more than required”.
Exploring the real: Design principles thought to guide teacher to inform designs of learning activities that may increase engagement (generative mechanisms )were identified from the intervention and analysed. Then statistical analyses were conducted to further explore relations, causes and power structures (for example how stress and teacher control correlated with student engagement) (Bergdahl, 2018c).
75 Design principles Design principles identified in the design intervention:
• Ensuring all students have access to the device in question • Ensuring all students are active and cognitively challenged • Providing timely feedback and scaffolding to students • Ensuring there is a variation of learning activities and interactions • Directing subject-related online and/or F2F interaction • Enabling teachers to gain real-time insight into students’ learning process
Engagement and disengagement in TEL
The LET instrument reflects four aspects of engagement and disengagement, namely behavioural, cognitive, emotional and social engagement, which may be reflected as follows:
Behavioural engagement can be observed in the use of digital technologies to support and manage learning, and behavioural disengagement as handing assignments in late due to unauthorised use of technologies, or being prevented to engage due to limited access to (functional) technologies.
Cognitive engagement may be noted in students concentrating easily when working with technologies, and taking the initiative to use technologies, and cognitive disengagement in becoming distracted by notifications and overwhelmed by information overflow.
Emotional engagement is reflected by students relying on technologies for schoolwork, as well as having an emotional desire and eagerness to use digital technologies, and being satisfied with such use, and emotional disengagement by using technologies to escape feelings of boredom, having difficulties resisting the pull from digital technologies, feeling frustration and resisting the use of digital technologies.
Social engagement includes students’ preference for, and satisfaction with, technology-mediated participation and communication with teachers and peers, and social disengagement refers to experiencing groupwork with technologies as upsetting or dispiriting, and working alone with digital technologies as irrelevant and being left to manage the tools without support.
(Bergdahl et al., 2019b)
76 Chapter 6. Future Research
This section highlights the need for future research in the field of student engagement and disengagement. ______
Future research could explore if there is a point at which greater engagement no longer brings additional benefits or (as suggested by Christenson and Reschly, 2012) the level at which students are so highly engaged that it is no longer adaptive but maladaptive. Approaching disengagement, research on how Swedish students’ disengagement profiles vary and how the dimensions of engagement and disengagement relate to each other is needed.
Observing the current state of international research on engagement and disengagement, it is worth noting that Sweden currently seems to lack established ongoing engagement research, while other countries have had well-established foundations since the 1990s. In America, for example, the NSSE has been gathering national data on student engagement since 1998, and in Finland, there are several departments that continuously publish research on student engagement in learning and in relation to technologies (e.g. the University of Jyväskylä, the University of Helsinki and the University of Oulu).
At the time of printing, the Swedish government has proposed, and are working on further proposals, to extend the possibilities students who have become school refusers, to attend class from elsewhere. Future research may be conducted to review not only successful practices, with regards to what digitally mediated support that is effective to scaffold learning for disengaged students, but also the quality and results of these interventions at scale.
Against this background, research initiatives and information on student engagement and disengagement in Sweden are critical. As indicated, disengagement in TEL is not manifested as the mere opposite of engagement. Yet, it is decisive to understand disengagement, particularly for students at risk. This thesis argues that there is little use in striving to redeem disengagement by focusing on engagement only, as the exclusion of disengagement might mean failing to uncover critical aspects that hinder learning. Thus, future research that explores engagement will benefit from including disengagement.
Depending on the level of (dis-)engagement data gathered, it would be possible to track the quality and effect of TEL activities, disengagement interventions as well as to identify early warning indicators, to promote and sustain educational quality, for local schools and regions as well as nation-wide.
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94 APPENDICES
APPENDIX A. Consent form substudies I‒III
Dear students,
My name is [researcher’s name] and I am a PhD student at [institution]. I’m about to conduct a study on ICT and student engagement.
As all research must adhere to ethical guidelines (see ‘good research’ at codex.vr.se for more information), I describe the research, its purpose and the type of data collected here:
Data are collected through observations, video recordings and interviews at [school name] during the time of data collection (spring 2017). The method includes observation (recording and note taking) and interviewing the students. The data are handled in accordance with the guidelines of PUL (Swedish law for data protection) and the Ethics Council. The data analysed are those that reflect meaning for learning. Your personal details are not the focus of the study. Those giving consent to participate will remain anonymous. That means your real name will not be used, nor will your identity be revealed in other ways: information, or other details, that might lead to the person being interviewed or observed being identified are altered to protect anonymity. The personal details are saved until the thesis is defended.
Participation is entirely based on free will. Participants have the right to withdraw their consent at any time.
Here are my contact details should you have questions: [contact details] Only students who have given written consent may be considered for participation in the study. (If you are under 18, this must be signed by your legal guardian as well.)
I hereby sign and agree to participate in the study,
95 APPENDIX B. Consent form substudies IV and V
The project "I use IT" - Information to you as a student
This study aims to explore students' experiences regarding digital technologies and engagement in their learning. In this study, we turn to a selection of upper secondary school students enrolled with programmes that prepare them for higher education in Stockholm.
Your answers are important. It is important for students to share their experiences and views on the use of IT in school in ongoing research, as school developers use research as support for making good decisions.
How is the study conducted? We gather two kinds of data: your questionnaire answers and school results. The questionnaire is online and takes about 15-20 minutes to answer. It contains questions about you and your experience of IT use in school. Your grades are then obtained from the school and grouped on the basis of value. We then link the survey responses to different groups to get a more nuanced understanding of different needs that students may have.
Are there risks in participating in the study? We have not identified any risks in participating.
Ethics and legal requirements: We follow good research practice, the Swedish Research Council's rules (see codex.vr.se) and the Swedish law (https://www.datainspektionen.se/lagar- -regler/dataskyddsforordin/)7. All surveys and other data are handled in pseudonymised form and stored at the institution to prevent unauthorised persons from accessing them8.
Your participation is voluntary and you can withdraw your consent at any time, without stating why. Use the contact details below:
Nina Bergdahl, PhD student [contact details] Uno Fors, professor, supervisor [contact details]
Data Protection Officer: Stockholm University, IT Department, 106 91 Stockholm Benita Falenius, Information Security Manager contact details
Consent to participate in the study: when you click on the survey you agree to be part of the study in the manner described in the information above
I agree to participate in the study "I use IT" I agree that information about me will be processed: - For research purposes - In accordance with applicable laws - In pseudonymised form
7 The person responsible for personal data is Stockholm University. According to the EU Data Protection Regulation, you have the right to read, correct, or request deletion, of the information about you handled in the study. Contact us then. If you have any complaints about how we handle your data, please contact the Data Inspectorate.
8 Data archiving follows archival legislation (currently stored for 10 years). Then the data is stored according to SU's application decision of RA-FS 1999: 1 (SU FV 2.6.2-3131-14). However, data that is considered to have value can be preserved and shared only in pseudonymised form.
96 APPENDIX C. Overview of the LET instrument indicators
Engagement Disengagement Beh1 Uses digital technologies as a Henrie et Dbeh1 Hands in assignments late due Fredricks et al., support for learning al., 2015 to unauthorised use of 2016* technologies Beh2 Uses the Internet to research Ott, 2017** Dbeh2 Delegates groupwork using Empiric finding what others have written and technologies to a peer, as no find facts more than one is required to complete the task Beh3 Uses asynchronous media to Ritella, Dbeh3 Is prevented from working due Empiric finding rehearse and master content 2018** to undeveloped technologies and system breakdowns Beh4 Uses technologies to work on Halverson, Dcog1 Chooses to use digital Fredricks et al., school assignments 2016; Ma et technologies in unauthorised 2016; Tallvid et al., 2018 ways al., 2015** Cog1 Concentrates well when using Reigeluth, Dcog2 Notifications (i.e. from a Ott, 2017; digital technologies 2016** mobile phone) easily cause Selwyn, 2016** distraction Cog2 Takes own initiative and Ott, 2017** Dcog3 Is overwhelmed by information Wang et al., decides on what digital 2017** technologies to use Cog3 Needs digital technologies to Empiric Demo1 Is emotionally drawn to an Eliasson, maximise learning finding application or digital 2013** technology Cog 4 Uses IT as a cognitive Heersmink, Demo2 Uses digital technologies to Selwyn, 2016** enhancement 2016** escape feelings of boredom Emo1 Wants to use more digital Ott, 2017** Demo3 Feels frustration over poor Selwyn, technologies for learning than communication over the 2016**; Wang what is used today learning platform et al., 2017* Emo2 Perceives using digital Järvelä & Demo4 Believes that teachers lack the Wang et al., technologies for learning as Renninger, IT skills needed to support 2017** engaging 2014 individual learning effectively
Emo3 Relies on technologies to Ott, 2017** Demo5 Resists using the laptop for all McCarthy & manage education reading Wright, 2005
Emo4 Finds creating with Holm- Demo6 Resists using the laptop for all McCarthy & technologies satisfying Sørensen & writing Wright, 2005 Tweddell- Levinsen, 2014** Emo5 Desires spatiotemporal Empiric Dsoc1 Is left to manage digital Jang et al., solutions and personalisation finding technologies for learning 2010*; Wang et that technologies may offer themselves al., 2017 Soc1 Is satisfied with using digital Empiric Dsoc2 Coming to school is perceived Empiric finding technologies that mediate finding as meaningless when there are teachers’ insight into students’ no interpersonal activities learning process Soc2 Is satisfied that teachers use Empiric Dsoc3 Is unhappy with repeatedly Empiric finding digital technologies to provide finding being directed to learn by feedback looking things up online Soc3 Feels that digital technologies Järvelä & Dsoc4 Feels upset/dispirited that Empiric finding are used in ways that enable Renninger, groupwork does not involve all participation, inclusion and 2014; students belonging Voelkl, 2012* Soc4 Experiences teachers’ social Empiric Dsoc5 Experiences decreasing Jang et al., presence with, as well as inside, finding engagement due to feeling 2010*; Tallvid applications isolated while using technology et al., 2015**
* The research explores the context of digital technologies per se, not (dis-)engagement ** The research explores (dis-)engagement per se, not uses of digital technologies
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