Supprimer les obstacles à l'apprentissage dans l'enseignement asynchrone en ligne des STIM

Auteurs-es

DOI :

https://doi.org/10.21432/cjlt28264

Mots-clés :

barrière d'apprentissage, pratique adaptative, traçage des connaissances, séquencement des exercices, apprentissage par renforcement, apprentissage en ligne asynchrone (à son rythme)

Résumé

            L'apprentissage en ligne asynchrone (à son propre rythme) offre une grande souplesse d'apprentissage, mais il comporte des obstacles à l'apprentissage inhérents à la nature de ce paradigme éducatif. Cet article de revue suggère quelques stratégies pertinentes permettant de répondre à ces obstacles afin de créer un environnement d'apprentissage en ligne asynchrone plus favorable. Ces stratégies comprennent a) l'augmentation de la conscience de l'apprentissage chez les étudiants, b) l'identification des étudiants en difficulté et c) la facilitation de la pédagogie de la maîtrise. En se concentrant sur la dispensation de l'apprentissage en ligne asynchrone dans les disciplines des sciences, de la technologie, de l'ingénierie et des mathématiques (STIM), cet article examine le rôle de l'évaluation formative pour l'apprentissage. Il est proposé que la conception et l'intégration systématiques de pratiques adaptatives dans les cours STIM constituerait une solution efficace de conception de l'apprentissage pour mettre en œuvre ces stratégies. En examinant les objectifs et le contexte de la pratique adaptative demandés dans cette étude, les exigences en matière de fonctionnalités sont décrites pour un tel modèle de pratique adaptative. Les modèles et les techniques qui peuvent être utilisés pour l'évaluation adaptative ont ensuite été examinés. Sur la base des résultats de cette revue, cet article soutient qu'un modèle de pratique adaptative basé sur l'apprentissage par renforcement serait la meilleure option pour répondre à ces exigences. Enfin, nous soulignons les insuffisances de la recherche dans ce domaine et suggérons une direction de recherche future pour nous-mêmes et pour d'autres chercheurs.

Bibliographies de l'auteur-e

Hongxin Yan, University of Eastern Finland

Hongxin Yan is a doctoral student at the School of Computing, University of Eastern Finland, Finland and works as a Learning Designer in the Faculty of Science and Technology, Athabasca University, Canada. His research interests include personalized and adaptive learning in online education.

Fuhua Lin, Athabasca University

Dr. Fuhua Lin is Professor and former Chair for the School of Computing and Information Systems, Faculty of Science and Technology, Athabasca University, Canada. He is the Coordinator of Intelligent Systems and Machine Learning research cluster of Athabasca University and a senior member of IEEE and ACM. 

Kinshuk, University of North Texas

Dr. Kinshuk is the Dean of the College of Information at the University of North Texas. Prior to that, he held the NSERC/CNRL/Xerox/McGraw Hill Research Chair for Adaptivity and Personalization in Informatics, funded by the Federal government of Canada, Provincial government of Alberta, and by national and international industries.

Références

Athabasca.ca. (2020). About AU - Accreditation. (Athabasca University Student Consumer Info). https://www.athabascau.ca/aboutau/accreditation/student-consumer-info.html

Bassen, J., Balaji, B., Schaarschmidt, M., Thille, C., Painter, J., Zimmaro, D., Games, A., Fast, E., & Mitchell, J. C. (2020). Reinforcement learning for the adaptive scheduling of educational activities. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–12. https://doi.org/10.1145/3313831.3376518

Beck, J. E., & Gong, Y. (2013). Wheel-spinning: Students who fail to master a skill. International Conference On Artificial Intelligence In Education (pp. 431-440). Springer. https://doi.org/10.1007/978-3-642-39112-5_44

Berry, D. A., & Fristedt, B. (1985). Bandit problems: Sequential allocation of experiments (Monographs on statistics and applied probability) (Vol. 5). Chapman and Hall. https://doi.org/10.1007/978-94-015-3711-7

Bloom, B. S. (1973). Recent developments in mastery learning. Educational Psychologist, 10(2), 53–57. https://doi.org/10.1080/00461527309529091

Clement, B., Roy, D., Oudeyer, P.-Y., & Lopes, M. (2015). Multi-armed bandits for intelligent tutoring systems. Journal of Educational Data Mining, 7(2), 20–48. https://doi.org/10.48550/arXiv.1310.3174

Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278. https://doi.org/10.1007/BF01099821

de la Torre, J. (2009). DINA model and parameter estimation: A didactic. Journal Of Educational And Behavioral Statistics, 34(1), 115-130. https://doi.org/10.3102/1076998607309474

Doignon, J.-P., & Falmagne, J.-C. (1999). Knowledge spaces. Springer-Verlag.

Doroudi, S., Aleven, V., & Brunskill, E. (2019). Where’s the reward? International Journal of Artificial Intelligence in Education, 29(4), 568-620. https://doi.org/10.1007/s40593-019-00187-x

Elo, A. (1978). The rating of chessplayer. past and present. Arco.

Guskey, T. (2010). Lessons of mastery learning. Educational Leadership, 68(2), 52-57. https://uknowledge.uky.edu/edp_facpub/14

Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Sage Press.

He-Yueya, J., & Singla, A. (2021). Quizzing policy using reinforcement learning for inferring the student knowledge state. Proceedings of The 14th International Conference on Educational Data Mining (EDM 2021). https://files.eric.ed.gov/fulltext/ED615585.pdf

Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research. 4, 237–285. https://doi.org/10.1613/jair.301

Kaser, T., Klingler, S., Schwing, A., & Gross, M. (2017). Dynamic bayesian networks for student modeling. IEEE Transactions on Learning Technologies, 10(4), 450–462. https://doi.org/10.1109/TLT.2017.2689017

Kennedy, T. J., & Odell, M. R. (2014). Engaging students in STEM education. Science Education International, 25(3), 246-258. https://files.eric.ed.gov/fulltext/EJ1044508.pdf

Kingston, N., & Nash, B. (2011). Formative assessment: A meta-analysis and a call for research. Educational measurement: Issues and practice, 30(4), 28-37. https://doi.org/10.1111/j.1745-3992.2011.00220.x

Kinshuk. (2016). Designing adaptive and personalized learning environments. Routledge.

Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory into practice, 41(4), 212-218. https://doi.org/10.1207/s15430421tip4104_2

Lin, F. (2020). Adaptive quiz generation using Thompson sampling. Third Workshop Eliciting Adaptive Sequences for Learning (WASL 2020), co-located with AIED 2020. https://jiji.cat/wasl2020/adaptive-quiz-generation.pdf

Liu, Q., Shen, S., Huang, Z., Chen, E., & Zheng, Y. (2021). A survey of knowledge tracing. ArXiv Preprint ArXiv:2105.15106. https://doi.org/10.48550/arXiv.2105.15106

Menéndez, I. Y., Napa, M. A., Moreira, M. L., & Zambrano, G. G. (2019). The importance of formative assessment in the learning teaching process. International Journal Of Social Sciences And Humanities, 3(2), 238-249. https://doi.org/10.29332/ijssh.v3n2.322

Pavlik Jr, P. I., Cen, H., & Koedinger, K. R. (2009). Performance Factors Analysis—A New Alternative to Knowledge Tracing. Online Submission. https://doi.org/10.1007/s11257-017-9193-2

Pelánek, R. (2017). Bayesian knowledge tracing, logistic models, and beyond: An overview of learner modeling techniques. User Modeling and User-Adapted Interaction, 27(3), 313-350. https://doi.org/10.1007/s11257-017-9193-2

Piech, C., Spencer, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L., & Sohl-Dickstein, J. (2015). Deep knowledge tracing. arXiv Preprint ArXiv:1506.05908. https://proceedings.neurips.cc/paper/2015/file/bac9162b47c56fc8a4d2a519803d51b3-Paper.pdf

Shepard, L. A., Penuel, W. R., & Pellegrino, J. W. (2018). Using learning and motivation theories to coherently link formative assessment, grading practices, and large-scale assessment. Educational Measurement: Issues and Practice, 37(1), 21-34. https://doi.org/10.1111/emip.12189

Sorrel, M., Barrada, J. R., de la Torre, J., & Abad, F. (2020). Adapting cognitive diagnosis computerized adaptive testing item selection rules to traditional item response theory. PLoS ONE, 15(1). https://doi.org/10.1371/journal.pone.0227196

Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.

Vygotsky, L. S. (1997). The historical meaning of the crisis in psychology: A methodological investigation. Problems of the Theory and History of Psychology, 3, pp. 233-344.

Weiss, D. J., & Kingsbury, G. G. (1984). Application of computerized adaptive testing to educational problems. Journal of Educational Measurement, 21(4), 361–375. https://doi.org/10.1111/j.1745-3984.1984.tb01040.x

Yan, H. (2020). Using learning analytics and adaptive formative assessment to support at-risk students in self-paced online learning. In 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT), 396-398. https://doi.org/10.1109/ICALT49669.2020.00125

Yan, H., Ives, C., & Lin, F. (2021). Adaptive practicing design for self-paced online learning. Proceedings of the 29th International Conference on Computers in Education, 765-768. https://icce2021.apsce.net/wp-content/uploads/2021/12/ICCE2021-Vol.II-PP.-765-768.pdf

Publié-e

2022-11-29

Numéro

Rubrique

Articles