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Pedagogical Based Learner Model Characteristics Pedagogical based Learner Model Characteristics Nour El Mawas1, Ioana Ghergulescu2, Arghir-Nicolae Moldovan1 and Cristina Hava Muntean1 1School of Computing, National College of Ireland, Dublin, Ireland 2Adaptemy, Dublin 2, Ireland Abstract personalisation rules definition in order to provide personalisation based on the information represented The personalisation and adaptation of content in the Learner Model have been proposed [16]. creation, distribution and presentation aim to The HORIZON 2020 NEWTON1 project provides increase learner quality of experience, improve the an European learning platform (NEWTELP) that learning process and increase the learning outcomes. delivers personalised and adaptive technology- This paper introduces a novel Learner Model that is enhanced educational content and learning activities, integrated by the NEWTON project into the addressing the individual learner needs in order to NEWTELP learning platform in order to support increase the learning outcomes and learner quality of personalisation and adaptation. The NEWTON’s experience. A Learner Model implemented in the Learner Model includes a multitude of learner NEWTELP platform supports the personalisation and characteristics, including pedagogical, disability, adaptation. The NEWTON Learner Model includes affective and multi-sensorial. various demographic, pedagogical, disability, affective and multi-sensorial learner characteristics. 1. Introduction This paper introduces the NEWTON Learner Model and the learner characteristics modelled by it. Personalisation is a key factor in modern The paper is organised as follows. Section 2 education, as the differences between learners are now proposes the theoretical background of the study and widely recognised by both researchers and educators existing pedagogical and affective based alike. Personalisation is also one of the biggest current characteristics considered in the Learner Model by trends in the e-learning industry [1]. There is an different e-learning systems. Section 3 presents the increasing need for personalised and intelligent NEWTON Learner Model and its characteristics that learning environments as learners differ in levels of support personalisation and adaptation. Section 4 knowledge, motivation, and have a variety of learning concludes the paper and presents its perspectives. styles and preferences [2]. Personalised learning can support individual learning and further engage 2. Literature Review on Learner Model learners in their studies. Gaps between slow and fast Characteristics learners are consistently emerging, and teaching to cater for these differences between students is being 2.1. Pedagogical based Characteristics noted as one the most challenging aspects by science educators [3], [4]. The Public and Private Information (PAPI) for There is a call for personalisation to be Learners [7] is a standard that divides the learner implemented into modern pedagogies, in order to information into six categories: (1) contact meet the needs and interests of different types of information (e.g. name, postal address and telephone learners [5]. In the Technology Enhanced Learning number), (2) relations information (e.g. classmates, (TEL) domain, personalisation is one of the key teammates, and mentors), (3) security information features, and can assist in bringing the focus of the (e.g. public keys, private keys and credentials), (4) learning experience to the learner instead of the preference information (useful and unusable I/O teacher [6]. The personalisation is based on a Learner devices, learning styles and physical limitations), (5) Model which represents the necessary learner performance information (e.g. grades, interim reports, characteristics in the context of a learning system. and log books), and (6) portfolio information (e.g. Various authoring tools that provide support for accomplishments and works). 1 http://www.newtonproject.eu/ 1 The Chinese E-Learning Technology Standards research attempts to address these factors by (CELTS-11) [8] is a specification for Learner Model developing different Learner Models [13]. that includes seven categories: (1) individual Learner’s affective characteristics can be grouped information (e.g. name, gender, date of birth, phone into two main dimensions related to: motivation and number and e-mail), (2) academic information (e.g. emotions. These can be measured and assessed using major, grade and learning plan), (3) relationship either objective or subjective methods. Objective information (e.g. the relation of the learner with tutors metrics of motivation can be derived through analyses and other learners), (4) safety information (e.g. of data that relates to student’s navigation behaviour password), (5) predilection information (e.g. learning such as time on task, number of repeated tasks, and resource types such as picture, animation, audio, number of help requests [11]. They can also be video, text), (6) performance information (e.g. the assessed by using external equipment, such as video learner’s knowledge and mastery of the learned camera recordings, heartrate monitor bracelet, knowledge), and (7) show reel information (e.g. Electroencephalography (EEG) headset and following accomplished work and projects of the learner). eye-tracker movements. Subjective metrics include Robson and Barr[9] identified five types of learner scores of questionnaires regarding motivation and information to be included in the Learner Model: (1) engagement, and similar, questionnaires can be used educational records and high level competencies (e.g. for emotion recognition. Furthermore, an language skills and certifications), (2) competencies observational assessment can be carried out to (e.g. skills, knowledge, abilities, outcomes, evaluate the emotional state of the learner. objectives) and level of competence, (3) data in A number of adaptive learning systems have Affective, motivational, and social dimensions, (4) incorporated an Affective Learner Model [2], [17], goals (e.g. learning goals and mission/task goals), and [18]. Cocea and Weibelzahl [14] studied data from a (5) physical adaptations (e.g. location, device web-based interactive environment, HTML-Tutor, to capabilities, ambient light, and accessibility data). predict whether a learner is disengaged. They defined The Felder–Silverman learning style model interest as being a determinant of engagement, as (FSLSM) [10] characterises each learner according to people tend to be more engaged in activities they are four dimensions: (1) sensing (learn facts and concrete interested in. The study focused on two types of tasks, learning material) / intuitive (learn abstract learning reading and problem-solving activities, with results material), (2) active (learn best by working actively pointing out similarities between blind-guessing and with the learning material) / reflective (think about uninterested guessing. and reflect on the material), (3) visual (remember best Looi [15] based their study on the ARCS Model, what they have seen) / verbal (get more out of textual inferring three aspects of motivation: confidence, representation), and (4) sequential (learn in small confusion, and effort. This research focused on incremental steps) / global (learn in large leaps). identifying the learner’s focus of attention and inputs According to these main refinement works that related to learners' actions, such as time spent on task, provide a general vision of the information considered time spent reading the relevant text, the time for the for user/learner profile/model, one can notice that learner to decide how to perform the task, the time of there are four main types information considered: (1) starting/finishing the task, the number of tasks the demographic data such as learner name, his/her postal learner has finished with respect to the current plan address, his/her gender, his/her number, etc., (2) (progress), the number of unexpected tasks performed grouping data like student mentors, his/her by the learner (unrelated to the learning plan), and the teammates, his/her grades, his/her certifications, (3) number of times learners have used the help files. student preferences such as media presentation and Rogaten et al. [13] criticised the lack of affective language skills, and (4) learning activity based changes (attitude) and behaviour in research, and information including for example learner’s proposed to breach this gap by studying an Affective- knowledge level, his/her learning goals, and skills. Behaviour-Cognition model of learning gains using longitudinal multilevel modelling, which included 2.2. Affective based Characteristics data from 80,000 students. Their focus was on finding out whether any specific student or course Learner’s affective characteristics, such as characteristic can predict learning gains or highlight a motivation, engagement and emotions were also potential dropout. recognised as important aspects to be considered for a successful learning process [11]. Affective 3 NEWTELP Platform characteristics such as motivation, engagement, interest, joy, surprise, boredom and frustration have a NEWTON is a large-scale EU Horizon 2020 huge impact on decision making, managing learning project that aims to develop, integrate and disseminate activities, timing, and reflection on learning [12]. A innovative technology-enhanced learning (TEL) Learner Model should take into consideration these methods and tools, and to create new or inter-connect changing learner’s affective characteristics.
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