NeoAVA: A virtual learning environment for Self-Regulated Learning to be used by students and teachers
Resumo
Many students face difficulties in self-managing their studies and making efficient choices about which resources to use, resulting in lower academic performance when using Virtual Learning Environments (VLE). The study proposed a web application integrated with Google Classroom, aimed at enhancing student performance through personalized educational recommendations based on self-regulated learning (SRL) strategies and Big Five (BF) personality traits. The research employs a Design Science Research methodology, involving problem identification, solution design, and evaluation using the Technology Acceptance Model (TAM) to assess the system’s usability and effectiveness. The methodology involved experiments with a small group of participants who provided feedback via a TAM survey. The results indicate positive acceptance of the system, with participants reporting that NeoAVA is useful, easy to use, and enhances their learning experience. The system leverages SRL and BF profiles to generate personalized recommendations that guide students toward better academic outcomes, showing promise in improving student performance through tailored interventions. The findings suggest the potential for broader application of NeoAVA across different educational platforms.
Referências
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Broadbent, J. and Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The internet and higher education, 27:1–13.
Cohen, A. and Baruth, O. (2017). Personality, learning, and satisfaction in fully online academic courses. Computers in Human Behavior, 72:1–12.
Coll, C. and Monereo, C. (2010). Psicologia da Educação Virtual: Aprender e ensinas com as tecnologias da informação e da comunicação. Artmed Editora.
Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8):982–1003.
Dwivedi, S. and Roshni, V. K. (2017). Recommender system for big data in education. In 2017 5th National Conference on E-Learning & E-Learning Technologies (ELEL-TECH), pages 1–4. IEEE.
McLellan, C. K. and Jackson, D. L. (2017). Personality, self-regulated learning, and academic entitlement. Social Psychology of Education, 20:159–178.
Neo, A. V. B. S., Moura, J. A. B., Araújo, J. M. F. R., Neo, G. S., and Freitas Júnior, O. G. (2024). The Use of Self-Regulation of Learning in Recommender Systems: State-of-the-Art and Research Opportunities. In Proceedings of the 16th International Conference on Computer Supported Education - Volume 2: CSEDU, pages 525–532. INSTICC, SciTePress.
Obeid, C., Lahoud, I., El Khoury, H., and Champin, P.-A. (2018). Ontology-based recommender system in higher education. In Companion proceedings of the the web conference 2018, pages 1031–1034.
Odilinye, L. and Popowich, F. (2020). Personalized Recommender System Using Learners’ Metacognitive Reading Activities. In Methodologies and Intelligent Systems for Technology Enhanced Learning, 10th International Conference, pages 195–205. Springer.
Pintrich, P. R. et al. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). ERIC.
Raad, B. E. and Perugini, M. E. (2002). Big five assessment. Hogrefe & Huber Publishers.
Ricci, F., Rokach, L., and Shapira, B. (2010). Introduction to recommender systems handbook. In Recommender systems handbook, pages 1–35. Springer.
Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., and Carlstrom, A. (2004). Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychological bulletin, 130(2):261.
Rogers, J. K. B., Mercado, T. C. R., and Decano, R. S. (2025). Moodle interactions and academic performance: educational data mining in a Philippine university. Journal of Education and Learning (EduLearn), 19(1):542–550.
Silva, V., Ferreira, H., Torres, A., and Rodrigues, F. (2021). Math Suggestion: Uma Ferramenta de Recomendação de Objetos de Aprendizagem Fundamentada nos Princípios das Avaliações de Autoeficácia e Análise de Desempenho. In Anais do XXXII Simpósio Brasileiro de Informática na Educação (SBIE), pages 237–248, Porto Alegre, RS, Brasil. SBC.
Syukur, Y., Afdal, A., Fikri, M., Zahri, T. N., and Anggraini, O. K. (2025). Examining the impact of learning motivation, desire to work, and curiosity of students in the post-COVID-19 pandemic era. Journal of Education and Learning (EduLearn), 19(1):202–209.
Wieringa, R. J. (2014). Design science methodology for information systems and software engineering. Springer.
Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G.-J., and Paas, F. (2019). Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human–Computer Interaction, 35(4-5):356–373.
Aguiar, J., Fechine, J., and Costa, E. (2015). Recomendação de Objetos de Aprendizagem baseada na Popularidade dos Objetos e nos Estilos de Aprendizagem dos Alunos. In Simpósio Brasileiro de Informática na Educação (SBIE), volume 26, page 1147.
Baptista, A. (2023). Modelo Pedagógico LearnT para o desenvolvimento da Autorregulação da Aprendizagem e do Pensamento Computacional em Cursos de Licenciatura. In Anais do XXXIV Simpósio Brasileiro de Informática na Educação (SBIE), pages 1886–1895, Porto Alegre, RS, Brasil. SBC.
Brito, P. H., Bittencourt, I. I., Machado, A. P., Costa, E., Holanda, O., Ferreira, R., and Ribeiro, T. (2014). A systematic approach for designing educational recommender systems. In Software Design and Development: Concepts, Methodologies, Tools, and Applications, pages 1264–1288. IGI Global.
Broadbent, J. and Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The internet and higher education, 27:1–13.
Cohen, A. and Baruth, O. (2017). Personality, learning, and satisfaction in fully online academic courses. Computers in Human Behavior, 72:1–12.
Coll, C. and Monereo, C. (2010). Psicologia da Educação Virtual: Aprender e ensinas com as tecnologias da informação e da comunicação. Artmed Editora.
Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8):982–1003.
Dwivedi, S. and Roshni, V. K. (2017). Recommender system for big data in education. In 2017 5th National Conference on E-Learning & E-Learning Technologies (ELEL-TECH), pages 1–4. IEEE.
McLellan, C. K. and Jackson, D. L. (2017). Personality, self-regulated learning, and academic entitlement. Social Psychology of Education, 20:159–178.
Neo, A. V. B. S., Moura, J. A. B., Araújo, J. M. F. R., Neo, G. S., and Freitas Júnior, O. G. (2024). The Use of Self-Regulation of Learning in Recommender Systems: State-of-the-Art and Research Opportunities. In Proceedings of the 16th International Conference on Computer Supported Education - Volume 2: CSEDU, pages 525–532. INSTICC, SciTePress.
Obeid, C., Lahoud, I., El Khoury, H., and Champin, P.-A. (2018). Ontology-based recommender system in higher education. In Companion proceedings of the the web conference 2018, pages 1031–1034.
Odilinye, L. and Popowich, F. (2020). Personalized Recommender System Using Learners’ Metacognitive Reading Activities. In Methodologies and Intelligent Systems for Technology Enhanced Learning, 10th International Conference, pages 195–205. Springer.
Pintrich, P. R. et al. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). ERIC.
Raad, B. E. and Perugini, M. E. (2002). Big five assessment. Hogrefe & Huber Publishers.
Ricci, F., Rokach, L., and Shapira, B. (2010). Introduction to recommender systems handbook. In Recommender systems handbook, pages 1–35. Springer.
Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., and Carlstrom, A. (2004). Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychological bulletin, 130(2):261.
Rogers, J. K. B., Mercado, T. C. R., and Decano, R. S. (2025). Moodle interactions and academic performance: educational data mining in a Philippine university. Journal of Education and Learning (EduLearn), 19(1):542–550.
Silva, V., Ferreira, H., Torres, A., and Rodrigues, F. (2021). Math Suggestion: Uma Ferramenta de Recomendação de Objetos de Aprendizagem Fundamentada nos Princípios das Avaliações de Autoeficácia e Análise de Desempenho. In Anais do XXXII Simpósio Brasileiro de Informática na Educação (SBIE), pages 237–248, Porto Alegre, RS, Brasil. SBC.
Syukur, Y., Afdal, A., Fikri, M., Zahri, T. N., and Anggraini, O. K. (2025). Examining the impact of learning motivation, desire to work, and curiosity of students in the post-COVID-19 pandemic era. Journal of Education and Learning (EduLearn), 19(1):202–209.
Wieringa, R. J. (2014). Design science methodology for information systems and software engineering. Springer.
Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G.-J., and Paas, F. (2019). Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human–Computer Interaction, 35(4-5):356–373.
Publicado
04/11/2024
Como Citar
NEO, Alana Viana Borges S.; MOURA, José Antão Beltrão; ARAÚJO, Joseana Macêdo Fechine Régis de; NEO, Giseldo S.; FREITAS JÚNIOR, Olival de Gusmão.
NeoAVA: A virtual learning environment for Self-Regulated Learning to be used by students and teachers. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 35. , 2024, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 1890-1903.
DOI: https://doi.org/10.5753/sbie.2024.242649.
