Open learner models in supporting self-regulated learning in higher education: A systematic literature review

Hooshyar, Danial; Pedaste, Margus; Saks, Kai; Leijen, Äli; Bardone, Emanuele; Wang, Minhong

doi:10.1016/j.compedu.2020.103878

Cited by 95 publications

(64 citation statements)

References 47 publications

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“…Multiple studies have reported that detection of students who are going to do poorly in courses and informing their teachers and themselves to take necessary actions is among the most crucial steps toward boosting their academic success (e.g., [5][6][7]). Student's grade-which usually indicates their ability to comprehend the conveyed knowledge-is considered as one of the main indicators of their success in the course [8].…”

Section: Introductionmentioning

confidence: 99%

Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern

Hooshyar

Yang

2021

Complexity

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While modelling students’ learning behavior or preferences has been found as a crucial indicator for their course achievement, very few studies have considered it in predicting achievement of students in online courses. This study aims to model students’ online learning behavior and accordingly predict their course achievement. First, feature vectors are developed using their aggregated action logs during a course. Second, some of these feature vectors are quantified into three numeric values that are used to model students’ learning behavior, namely, accessing learning resources (content access), engaging with peers (engagement), and taking assessment tests (assessment). Both students’ feature vectors and behavior model constitute a comprehensive students’ learning behavioral pattern which is later used for prediction of their course achievement. Lastly, using a multiple criteria decision-making method (i.e., TOPSIS), the best classification methods were identified for courses with different sizes. Our findings revealed that the proposed generalizable approach could successfully predict students’ achievement in courses with different numbers of students and features, showing the stability of the approach. Decision Tree and AdaBoost classification methods appeared to outperform other existing methods on different datasets. Moreover, our results provide evidence that it is feasible to predict students’ course achievement with a high accuracy through modelling their learning behavior during online courses.

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Section: Introductionmentioning

confidence: 99%

Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern

Hooshyar

Yang

2021

Complexity

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“…Com isso, por mais que existam trabalhos que argumentem que o acesso ao MSAE promova o desenvolvimento de aspectos meta-cognitivos (Hooshyar et al, 2020), não há relatos de avaliações que comprovem esta evolução com instrumentos como o MAI. Assim, a partir da análise estatística proposta nesta pesquisa, pode-se afirmar que o acesso ao MSAE no estudo proposto relativo à disciplina de Introdução à Programação não promoveu alterações significativas nas habilidades meta-cognitivas dos estudantes participantes.…”

Section: Resultsunclassified

“…Em uma revisão bibliográfica sobre 30 anos de publicações sobre MAE, Hooshyar et al (2020) identificaram que a maior parte das implementações do MAE dão suporte aos aspectos cognitivos e não aos meta-cognitivos. Os autores também identificaram que, embora existam possibilidades de interação maiores com o MAE, a maior parte dos trabalhos se foca apenas na inspeção do modelo do aluno e da turma.…”

Section: Modelo Social Aberto Do Estudanteunclassified

A Aprendizagem de Programação Apoiada pelo Modelo Social Aberto do Estudante

Ferreira

Reategui

2020

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This article presents the implementation of the Open Social Student Model in the virtual learning environment of an introductory computer programming course in higher education. The Open Social Student Model presents, in a visual and interactive way, a representation of the students' performance and that of their colleagues, thus allowing them to compare, evaluate and reflect about their own performance. As introductory programming courses have a high failure and dropout rate, the aim of this study has been to investigate how the metacognitive scaffolding provided by the interaction with the Open Social Student Model could contribute to the learning processes. To achieve this goal, a quasi-experiment was carried out involving 40 students during one semester, following a quali-quantitative approach. Results showed that the students used the Open Social Student Model to evaluate and monitor their own performance, to organize their studies and identify colleagues who could provide them support or who could be helped by them,. However, changes in the students' meta-cognitive awareness were not observed when comparing pre-test and post-test results. Thus, the study led to the conclusion that students who already master their metacognitive processes and know how to apply them in their learning process are the ones who can benefit the most with the use of the Open Social Student Model.

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“…However, in the past, researchers begin to feel that incorporating affective states into ITSs can lead to a significant improvement of the learner performance as well as the learning experience [8], [9]. Analogously, recent studies such as [10], [11] also found that the open learner model (OLM) (a means of visualizing current knowledge or skill level and the aim is to help learners learn more effectively by tracking, reflecting, and pacing the learning process) which are developed mainly upon ITSs ignores the emotion area mostly and support cognition the most and then a bit less metacognition and motivation. Hence, it is necessary to properly investigate affective intervention in tutoring environments to enhance and motivate the learning outcomes [12], [13].…”

Section: Introductionmentioning

confidence: 99%

The Transition From Intelligent to Affective Tutoring System: A Review and Open Issues

et al. 2020

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Open learner models in supporting self-regulated learning in higher education: A systematic literature review

Cited by 95 publications

References 47 publications

Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern

Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern

A Aprendizagem de Programação Apoiada pelo Modelo Social Aberto do Estudante

The Transition From Intelligent to Affective Tutoring System: A Review and Open Issues

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