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Learning Analytics: Learning to Think and Make Decisions

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Learning Analytics: Learning to Think and Make Decisions LEARNING ANALYTICS: LEARNING TO THINK AND MAKE DECISIONS Airina Volungevičienė, Vytautas Magnus University Josep Maria Duart, Universitat Oberta de Catalunya Justina Naujokaitienė, Vytautas Magnus University Giedrė Tamoliūnė, Vytautas Magnus University Rita Misiulienė, Vytautas Magnus University ABSTRACT The research aims at a specific analysis of how learning analytics as a metacognitive tool can be used as a method by teachers as reflective professionals and how it can help teachers learn to think and come down to decisions about learning design and curriculum, learning and teaching process, and its success. Not only does it build on previous research results by interpreting the description of learning analytics as a metacognitive tool for teachers as reflective professionals, but also lays out new prospects for investigation into the process of learning analytics application in open and online learning and teaching. The research leads to the use of learning analytics data for the implementation of teacher inquiry cycle and reflections on open and online teaching, eventually aiming at an improvement of curriculum and learning design. The results of the research demonstrate how learning analytics method can support teachers as reflective professionals, to help understand different learning habits of their students, recognize learners’ behavior, assess their thinking capacities, willingness to engage in the course and, based on the information, make real time adjustments to their course curriculum. Key words: learning analytics, metacognition, reflective professionals INTRODUCTION education with the prospect of reconsidering how Learning analytics can be defined as the learning analytics may contribute to better teaching measurement and collection of extensive data and learning and address, in particular, issues in about learners with the aim of understanding higher education (Zilvinskis & Borden, 2017) and and optimizing the learning process and the massive open and online learning. environments in which it happens. Recently There is sufficient data available on virtual researchers have started a fundamentally new learning environments provided by learning direction of learning analytics by addressing big analytics on student and teacher behavior and data (Picciano, 2012); educational data mining performance, but there is no common practice (Siemens & Baker, 2012); academic analytics, among teachers in open and online learning in social learning, and action analytics (Ferguson, higher education of using this data to improve the 2012); as well as issues of student dropouts and learning and teaching process. Stewart’s (2017) ways to increase student success (Arnold & Pistilli, research proves that there are inhibitors, such as a 2012), with the purpose of developing a method of lack of training, a fear of exposure, too much or too how learning analytics may enhance teaching and little data, a lack of ability, cultural vs procedural learning (Gasevic, Dawson, & Siemens, 2015). This behavior of teachers, and a lack of resources shift revealed a completely new area of research in and practices among teachers, that hinder them JOURNAL OF EDUCATORS ONLINE from applying learning analytics in practice. universities, resulted in a low response rate, which, How learning analytics and data may inform and in its turn, revealed that the teachers were aware improve open and online learning from the point of of the possibilities of accessing learning analytics view of teacher and learner awareness about their data, but it was still quite challenging for them to behavior and their learning and teaching methods use it. Therefore, this empirical research focuses on has been addressed by several researchers, thus an international expert interview. putting forward the idea of describing learning LITERATURE REVIEW analytics as a metacognitive tool (Durall & Gros, We looked at research covering the process 2014), suggesting a development of metacognitive of applying learning analytics as a metacognitive decision-making skills in teacher education tool in open and online learning and teaching by (Griffith, Bauml, & Quebec-Fuentes, 2016), and focusing on learning analytics data for teachers’ focusing on learning design in higher education inquiry cycle implementation, reflecting on open by using data from learning analytics (Nguyen, and online teaching, and discussing learning Rienties, & Toetenel, 2017). analytics data analysis for curriculum improvement Metacognitive activities in open and online and learning design. higher education learning created by using learning analytics data enhance a better organized reflection Process of applying of learning analytics as a by the teacher on the process and the outcomes of metacognitive tool in open and online learning the choices of learners and lead to better decisions and teaching made by the teachers during curriculum and The ability to monitor thinking and use learning design. Learning analytics focuses on appropriate skills and strategies to achieve the measurement and collection of data about desirable outcomes is defined as metacognition. learners in order to understand and optimize their It is considered a critical component of successful learning and the environments in which it happens. learning, which involves self-regulation and self- Regardless of the advantages, teachers do not yet reflection on the learning process and guides the use learning analytics as a common practice to thinking process (Medina, Castleberry, & Persky, examine the link between learners’ expectations 2017). Brown (1987) presents it in two coherent and curriculum and learning design. components: metacognitive knowledge and This research is focused on how learning metacognitive processes. Metacognitive knowledge analytics as a metacognitive tool can be applied to refers to the perception of one’s cognition strengths, developing a learning analytics method for reflective weaknesses, learning habits, task characteristics, teacher practice. It builds on the description of and task solving strategies (when, where, and how learning analytics as a metacognitive tool for to apply them). Metacognitive processes allow teachers as reflective professionals, but it also monitoring and regulating cognition, and all the opens new prospects for investigating the process latter processes are divided into three subcategories: of applying learning analytics as a metacognitive planning, monitoring, and evaluation (Brown, method in open and online learning and teaching, for 1987). The above-mentioned processes can be using learning analytics data for the implementation used for setting goals, selecting needed cognitive of teacher inquiry cycle and reflection on open and strategies, planning decision-making or learning online teaching, and for improving curriculum and steps, monitoring cognitive activities, evaluating learning design. outcomes, and reflecting on the processes It must be noted that there is insufficient empir- (Brown, 1987). ical evidence available in the research literature on Griffith et al. (2016) state that teachers make the use of learning analytics data among teachers. instant decisions when evaluating pedagogical Thus, the authors initiated a pilot research project knowledge and pedagogical content knowledge. by addressing a number of teachers all over the As a result, they are able to refine their teaching world and asking them to provide evidence on how expertise. Teachers need opportunities to think they apply learning analytics data in teaching and systematically about the complexity of the learning. The pilot project, contacting a number classroom and its participants (Griffith et al., of academic departments at open and traditional 2016), i.e., teaching and learning behavior as well JOURNAL OF EDUCATORS ONLINE as curriculum and learning design. Teachers as (McLoughlin & Lee, 2010). The fundamental reflective practitioners and professionals do reflect question, in this case, is how teachers can help upon teaching and learning process. However, students think critically and metacognitively research shows that teachers (McKown & Weinstein, about their own choices, while understanding 2008) and students (Borghi, Maindardes, & Silva, why these different choices occur. Learning 2016; Koc, 2017) have different expectations of analytics may be one of the best metacognitive the learning and teaching processes. Teachers are tools to visualize these paths, to help teachers inclined to be more interactive with students because better prepare students for metacognitive interaction should improve student learning. From activities in learning, and to clarify the the learners’ perspective, the research reveals that reasons for different student choices. autonomous and self-directed learners and learners In both cases, learning analytics as a with high academic performance have an internal metacognitive tool can be applied and empowered locus of control, i.e., they are inclined to internally by data mining from online environments to control the situation surrounding them (Shehu & benefit both teachers and students, with the aim of Bushi, 2015). Open and online learners have the improving the teaching and learning experience. characteristics of autonomous and self-directed If we analyze scenario a) as a prescriptive learners (Bonk, Lee, Kou, Xu, & Sheu, 2015), curriculum solution, the learning analytics thus they need more flexibility and asynchronous data collected from open and online learning learning, which allows them
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