Social comparison in learning analytics dashboard supporting motivation and academic achievement

Fleur, Damien S.; Bos, Wouter van den; Bredeweg, Bert

doi:10.1016/j.caeo.2023.100130

Cited by 15 publications

(4 citation statements)

References 56 publications

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“…Schunk and Zimmerman (2012) argued that the differences in learning outcomes could be explained in terms of their capacity to self‐regulate learning, their characteristics, and their motivation. Studies (e.g., Fleur et al, 2023; Lehmann et al, 2014; Zimmerman & Schunk, 2008) examined the relationship between SRL and the motivational process. They showed that motivation affected how students selected learning strategies, the learning process, and student achievement at the end.…”

Section: Literature Reviewmentioning

confidence: 99%

Unfolding self‐regulated learning profiles of students: A longitudinal study

Esnaashari

Gardner

Arthanari

et al. 2023

Computer Assisted Learning

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Background It is vital to understand students' Self‐Regulatory Learning (SRL) processes, especially in Blended Learning (BL), when students need to be more autonomous in their learning process. In studying SRL, most researchers have followed a variable‐oriented approach. Moreover, little has been known about the unfolding process of students' SRL profiles. Objectives We present the insights derived from a study that measured motivation and the learning strategies used by 198 students of a university entry‐level, business school, BL course to develop an understanding of students' SRL processes. Methods The Strategies for Learning Questionnaire (MSLQ) was used to survey 198 students three times during a semester to investigate SRL profiles and how they unfolded as the course progressed using a person‐oriented approach. Through a clustering approach, we focus on MSLQ's motivation aspects as its importance has been emphasised by different SRL theories, and extant research into motivation in learning analytics (LA) is still lacking. Results and Conclusions Through the longitudinal clustering approach, we identified minimally, average, and highly SRL profiles. We acknowledged that students might change their SRL profiles as the course progressed as a result of feedback they received. What are the 1 or 2 Major Takeaways from the Study? This study contributes to the SRL theory by examining students' SRL profiles adaptation longitudinally (addressing the challenge identified regarding the cyclical nature of SRL). This study contributes to LA by investigating motivational constructs currently lacking in the field and bringing forward theory based empirical evidence to inform theory and practice.

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Section: Literature Reviewmentioning

confidence: 99%

Unfolding self‐regulated learning profiles of students: A longitudinal study

Esnaashari

Gardner

Arthanari

et al. 2023

Computer Assisted Learning

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“…Exceptions before analyzing and classification of data need to be considered for the assurance of the accuracy of model results. The data cleaning levels are needed to verify the validity and integrity of data while analyzing (Fleur et al, 2023). The first level is the need to take off the records with invalid video codes or screen names because invalid pre-defined names are assigned as unique IDs for the learners.…”

Section: Pre-processingmentioning

confidence: 99%

The Bayesian CNN-LSTM classification model to predict and evaluate learner’s performance

Sudhamathy,

Valliammal

2023

Int. J. Appl. Sci. Eng.

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Learning analytics (LA) is a research domain that leverages the analysis of data from the learning process to gain a deeper understanding and enhance learning outcomes. To classify learner performance, a model has been proposed that combines various deep learning techniques, including convolutional neural network (CNN), Long Short-Term Memory (LSTM), and Bayesian models. The integration of these approaches aims to improve the accuracy and effectiveness of performance classification. CNN is used for capturing the local information and LSTM neural network is used for the long-distance dependencies. The effective classification of learners' performance is achieved by combining the strengths of CNN and LSTM, along with the integration of a Bayesian deep learning model. The performance of the proposed model is estimated using the metrics like Accuracy, Precision, Recall and F1-Score. The model showed improvements in Accuracy, Precision, Recall and F1-Score are 98.18%, 97.09%, 96.38% and 95.35% respectively. The proposed model is compared with another existing model such as LSTM and collaborative machine learning (ML) models in terms of performance metrics. The proposed method attained accuracy of 98.18% which is higher than other existing models.

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“…Ela pode proporcionar melhorias, como, por exemplo, melhores desempenhos nas notas das avaliações, na capacidade de autorregulação (competência própria utilizada para modular e monitorar a cognição, a emoção e o comportamento) e maior nível de motivação para os estudos (FLEUR; BREDEWEG, 2023). Com base nisso, nós podemos refletir sobre algumas questões: i.)…”

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“…Tendo isso em mente, é interessante para o leitor perceber que tal comparação poderá ser recompensada para o desenvolvimento de ações e obtenção de melhores resultados acadêmicos. E o contrário disso poderá ser prejudicial no ambiente acadêmico (FLEUR, BREDEWEG, VAN DEN BOS, 2023). No aspecto prático, o artigo apresentou uma contribuição para a academia, a partir de um modelo experimental.…”

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Learning Analytics Aplicada Às Áreas Pedagógica E Educacional: Verificação Do Aumento Da Motivação E Do Desempenho Acadêmico Em Alunos Do Nível Superior

Martins,

Calvosa

2024

SADSJ

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A obra original foi escrita por três autores. O primeiro, Damien S. Fleur, é doutorando em Psicologia Cognitiva e Aprendizagem Aprimorada por Tecnologia, professor da Faculty of Science, Informatics Institute (Amsterdã, Holanda). O segundo autor, Wouter van den Bos, fez doutorado na área de Psicologia do Desenvolvimento. É professor associado a Faculty of Social and Behavioural Sciences (Amsterdã, Holanda) e pesquisador do Center for Adaptive Rationality, Max Planck Institute for Human Development (Berlim, Alemanha). O último autor, Bert Bredeweg, fez doutorado na área de inteligência artificial. É professor associado da Amsterdam University of Applied Sciences (Amsterdã, Holanda) e da University of Amsterdam Psychology (Amsterdã, Holanda), na qual atua por mais de 37 anos.

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Social comparison in learning analytics dashboard supporting motivation and academic achievement

Cited by 15 publications

References 56 publications

Unfolding self‐regulated learning profiles of students: A longitudinal study

Unfolding self‐regulated learning profiles of students: A longitudinal study

The Bayesian CNN-LSTM classification model to predict and evaluate learner’s performance

Learning Analytics Aplicada Às Áreas Pedagógica E Educacional: Verificação Do Aumento Da Motivação E Do Desempenho Acadêmico Em Alunos Do Nível Superior

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