Exploring the Relationship between Students Engagement and Self-Regulated Learning: A Case Study using OULAD Dataset and Machine Learning Techniques
Resumo
Exploring the correlation among student engagement, self-regulated learning, and academic performance through analysis of the Open University Learning Analytics Dataset (OULAD). This dataset covers course details, learner information and their interactions with the VLE. It records interactions such as resource clicks, course notes, discussions, and quizzes. Online student data was analyzed using educational data mining and three clustering algorithms: K-means, EM and Agglomerative Clustering. The results show a positive correlation between student engagement and academic performance, highlighting that greater interaction with learning resources results in better academic outcomes and shows a self-regulated approach to learning.
Referências
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Panadero, E. (2017). A Review of Self-regulated Learning: Six Models and Four Directions for Research. Frontiers in Psychology, 8:422.
Pintrich, P. R. e Groot, E. V. D. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, páginas 33–40.
Puustinen, M. e Pulkkinen, L. (2001). Models of self-regulated learning: A review. Scandinavian Journal of Educational Research, 45:269–286.
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53–65.
Spearman, C. (1961). The proof and measurement of association between two things. The American Journal of Psychology, 100(3/4):441–471.
Valle, A., Núñez, J. C., Cabanach, R. G., González-Pienda, J. A., Rodríguez, S., Rosário, P., Cerezo, R., e Muñoz-Cadavid, M. A. (2008). Self-regulated profiles and academic achievement. Psicothema, 20(4):724–731.
Winne, P. H. e Hadwin, A. F. (1998). Studying as self-regulated engagement in learning. In Hacker, D., Dunlosky, J., e Graesser, A., editors, Metacognition in Educational Theory and Practice, páginas 277–304. Erlbaum, Mahwah, NJ.
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Yot, C. e Marcelo, C. (2017). University students’ self-regulated learning using digital technologies. International Journal of Educational Technology in Higher Education, 14.
Zimmerman, B. e Martinez-Pons, M. (1986). Development of a structured interview for assessing student use of self-regulated learning strategies. American Educational Research Journal, 23:614–628.
Zimmerman, B. J. (1986). Becoming a self-regulated learner: Which are the key subprocesses? Contemporary Educational Psychology, 11(4):307–313.
Aldowah, H., Al-Samarraie, H., e Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37:13–49.
Anderson, T. W. e Darling, D. A. (1952). Asymptotic theory of certain” goodness of fit” criteria based on stochastic processes. The annals of mathematical statistics, páginas 193–212.
Araka, E., Oboko, R., Maina, E., e Gitonga, R. (2022). Using educational data mining techniques to identify profiles in self-regulated learning: an empirical evaluation. The International Review of Research in Open and Distributed Learning, 23(1):131–162.
Barnard-Brak, L., Paton, V. O., e Lan, W. Y. (2020). Profiles in self-regulated learning in the online learning environment. International Review of Research in Open and Distributed Learning, 11(1):61–80.
Boekaerts, M. (1988). Motivated learning: bias in appraisals. IJER, 12(3):267–280. Broadbent, J. e Fuller-Tyszkiewicz, M. (2018). Profiles in self-regulated learning and their correlates for online and blended learning students. Educational Technology Research and Development, 66.
Cobos, R., Wilde, A., e Zaluska, E. (2017). Predicting attrition from massive open online courses in futurelearn and edx. In Workshop at the 7th International Learning Analytics and Knowledge Conference, Vancouver, Canada.
Coman, C., T, ı̂ru, L. G., Meses, an-Schmitz, L., Stanciu, C., e Bularca, M. C. (2020). Online teaching and learning in higher education during the coronavirus pandemic: Students’ perspective. Sustainability, 12(24):10367.
Costa, J., Dorça, F., e Araújo, R. (2020). Avaliação do comportamento de estudantes em um ambiente educacional ubíquo. In Anais do XXXI Simpósio Brasileiro de Informática na Educação, páginas 182–191, Porto Alegre, RS, Brasil. SBC.
Efklides, A. (2011). Interactions of Metacognition With Motivation and Affect in Self-Regulated Learning: The MASRL Model. Educational Psychologist, 46(1):6–25.
ElSayed, A. A., Caeiro-Rodríguez, M., MikicFonte, F. A., e Llamas-Nistal, M. (2019). Research in learning analytics and educational data mining to measure self-regulated learning: A systematic review. In World conference on mobile and contextual learning, páginas 46–53.
Furlanetto, G., Carvalho, V., Baldassin, A., e Manacero, A. (2022). Algoritmos de agrupamento aplicados à detecção de fraudes. In Anais da XIII Escola Regional de Alto Desempenho de São Paulo, páginas 29–32, Porto Alegre, RS, Brasil. SBC.
Hadwin, A. F., Järvelä, S., e Miller, M. (2011). Self-regulated, co-regulated, and socially shared regulation of learning. In Zimmerman, B. J. e Schunk, D. H., editors, Handbook of self-regulation of learning and performance, Educational psychology handbook series, páginas 65–84. Routledge/Taylor & Francis Group.
Hastie, T., Tibshirani, R., Friedman, J. H., e Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer.
Jain, A. K. e Dubes, R. C. (1988). Algorithms for clustering data. Prentice-Hall, Inc. Kitsantas, A. (2013). Fostering college students’ self-regulated learning with learning technologies. Hellenic Journal of Psychology, 10(3):235–252. Kuzilek, J., Hlosta, M., e Zdráhal, Z. (2017). Open university learning analytics dataset.
Scientific Data, 4:170171. Li, H., Flanagan, B., Konomi, S., e Ogata, H. (2018). Measuring behaviors and identifying indicators of self-regulation in computer-assisted language learning courses. Research and Practice in Technology Enhanced Learning, 13:1–12.
Lima, G., Araújo, R., e Dorça, F. (2020). Uma análise dos recursos tecnológicos utilizados na estimulação da aprendizagem autorregulada em ambientes educacionais na Última década. In Anais do XXXI Simpósio Brasileiro de Informática na Educação, páginas 732–741, Porto Alegre, RS, Brasil. SBC.
Lima, M., Carvalho, L., Oliveira, E., Oliveira, D., e Pereira, F. (2021). Uso de atributos de código para classificação da facilidade de questões de codificação. In Anais do Simpósio Brasileiro de Educação em Computação, páginas 113–122, Porto Alegre, RS, Brasil. SBC.
McKinney, W. et al. (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference, volume 445, páginas 51–56. Austin, TX.
Moon, T. (1996). The expectation-maximization algorithm. IEEE Signal Processing Magazine, 13(6):47–60.
Panadero, E. (2017). A Review of Self-regulated Learning: Six Models and Four Directions for Research. Frontiers in Psychology, 8:422.
Pintrich, P. R. e Groot, E. V. D. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, páginas 33–40.
Puustinen, M. e Pulkkinen, L. (2001). Models of self-regulated learning: A review. Scandinavian Journal of Educational Research, 45:269–286.
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53–65.
Spearman, C. (1961). The proof and measurement of association between two things. The American Journal of Psychology, 100(3/4):441–471.
Valle, A., Núñez, J. C., Cabanach, R. G., González-Pienda, J. A., Rodríguez, S., Rosário, P., Cerezo, R., e Muñoz-Cadavid, M. A. (2008). Self-regulated profiles and academic achievement. Psicothema, 20(4):724–731.
Winne, P. H. e Hadwin, A. F. (1998). Studying as self-regulated engagement in learning. In Hacker, D., Dunlosky, J., e Graesser, A., editors, Metacognition in Educational Theory and Practice, páginas 277–304. Erlbaum, Mahwah, NJ.
World Health Organization (2020). WHO Director-General’s opening remarks at the media briefing on COVID-19 11 March 2020. World Health Organization. Available at [link].
Yot, C. e Marcelo, C. (2017). University students’ self-regulated learning using digital technologies. International Journal of Educational Technology in Higher Education, 14.
Zimmerman, B. e Martinez-Pons, M. (1986). Development of a structured interview for assessing student use of self-regulated learning strategies. American Educational Research Journal, 23:614–628.
Zimmerman, B. J. (1986). Becoming a self-regulated learner: Which are the key subprocesses? Contemporary Educational Psychology, 11(4):307–313.
Publicado
06/11/2023
Como Citar
LIMA, Geycy D. O.; COSTA, Juliete A. R.; ARAÚJO, Rafael D.; DORÇA, Fabiano A..
Exploring the Relationship between Students Engagement and Self-Regulated Learning: A Case Study using OULAD Dataset and Machine Learning Techniques. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 34. , 2023, Passo Fundo/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 1154-1165.
DOI: https://doi.org/10.5753/sbie.2023.234344.
