Abstract
In recent years, there has been an increasing trend in the use of student-centred approaches within educational systems that engage students in various higher-order learning activities such as creating resources, creating solutions, rating the quality of resources, and giving feedback. In response to this trend, this paper proposes an interpretable and open learner model called MA-Elo that capture an abstract representation of a student’s knowledge state based on their engagement with multiple types of learning activities. We apply MA-Elo to three data sets obtained from an educational system supporting multiple student activities. Results indicate that the proposed approach can provide a higher predictive performance compared with baseline and some state-of-the-art learner models.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Abdi, S., Khosravi, H., Sadiq, S.: Modelling learners in crowdsourcing educational systems. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 3–9. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_1
Abdi, S., Khosravi, H., Sadiq, S.: Modelling learners in adaptive educational systems: a multivariate glicko-based approach. In: 11th International Learning Analytics and Knowledge Conference, LAK21, pp. 497–503. Association for Computing Machinery (2021)
Abdi, S., Khosravi, H., Sadiq, S., Demartini, G.: Evaluating the quality of learning resources: a learner sourcing approach. IEEE Trans. Learn. Technol. 14(1), 81–92 (2021)
Abdi, S., Khosravi, H., Sadiq, S., Gasevic, D.: Complementing educational recommender systems with open learner models. In: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, pp. 360–365 (2020)
Abdi, S., Khosravi, H., Sadiq, S., Gasevic, D.: A multivariate Elo-based learner model for adaptive educational systems. In: Proceedings of the Educational Data Mining Conference, pp. 462–467 (2019)
Boud, D., Soler, R.: Sustainable assessment revisited. Assess. Eval. High. Educ. 41(3), 400–413 (2016)
Bull, S., Ginon, B., Boscolo, C., Johnson, M.: Introduction of learning visualisations and metacognitive support in a persuadable open learner model. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 30–39. ACM (2016)
Bull, S., Kay, J.: Open learner models. In: Nkambou, R., Bourdeau, J., Mizoguchi, R. (eds.) Advances in Intelligent Tutoring Systems. SCI, vol. 308, pp. 301–322. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14363-2_15
Cen, H., Koedinger, K., Junker, B.: Learning factors analysis – a general method for cognitive model evaluation and improvement. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 164–175. Springer, Heidelberg (2006). https://doi.org/10.1007/11774303_17
Choffin, B., Popineau, F., Bourda, Y., Vie, J.J.: DAS3H: modeling student learning and forgetting for optimally scheduling distributed practice of skills. arXiv preprint arXiv:1905.06873 (2019)
Corbett, A.T., Anderson, J.R.: Knowledge tracing: modeling the acquisition of procedural knowledge. User Model. User-Adap. Inter. 4(4), 253–278 (1994)
Darvishi, A., Khosravi, H., Sadiq, S.: Utilising learnersourcing to inform design loop adaptivity. In: Alario-Hoyos, C., Rodríguez-Triana, M.J., Scheffel, M., Arnedillo-Sánchez, I., Dennerlein, S.M. (eds.) EC-TEL 2020. LNCS, vol. 12315, pp. 332–346. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57717-9_24
Denny, P., Hamer, J., Luxton-Reilly, A., Purchase, H.: Peerwise: students sharing their multiple choice questions. In: Proceedings of the Fourth International Workshop on Computing Education Research, pp. 51–58 (2008)
Doroudi, S., et al.: Crowdsourcing and Education: Towards a Theory and Praxis of Learnersourcing. International Society of the Learning Sciences, Inc. [ISLS] (2018)
Gervet, T., Koedinger, K., Schneider, J., Mitchell, T., et al.: When is deep learning the best approach to knowledge tracing? JEDM—J. Educ. Data Min. 12(3), 31–54 (2020)
Guo, P.J., Markel, J.M., Zhang, X.: Learnersourcing at scale to overcome expert blind spots for introductory programming: a three-year deployment study on the python tutor website. In: Proceedings of the Seventh ACM Conference on Learning@ Scale, pp. 301–304 (2020)
Khosravi, H., Demartini, G., Sadiq, S., Gasevic, D.: Charting the design and analytics agenda of learnersourcing systems. In: 11th International Learning Analytics and Knowledge Conference, LAK21, pp. 32–42. Association for Computing Machinery, New York (2021)
Khosravi, H., Gyamfi, G., Hanna, B.E., Lodge, J.: Fostering and supporting empirical research on evaluative judgement via a crowdsourced adaptive learning system. In: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, pp. 83–88 (2020)
Khosravi, H., Kitto, K., Williams, J.J.: Ripple: a crowdsourced adaptive platform for recommendation of learning activities. J. Learn. Anal. 6(3), 91–105 (2019)
Kim, J., et al.: Learnersourcing: improving learning with collective learner activity. Ph.D. thesis, Massachusetts Institute of Technology (2015)
Klinkenberg, S., Straatemeier, M., van der Maas, H.L.: Computer adaptive practice of maths ability using a new item response model for on the fly ability and difficulty estimation. Comput. Educ. 57(2), 1813–1824 (2011)
Lord, F.M.: Applications of Item Response Theory to Practical Testing Problems. Routledge, London (2012)
Pavlik Jr., P.I., Cen, H., Koedinger, K.R.: Performance factors analysis-a new alternative to knowledge tracing. Online Submission (2009)
Pelánek, R., Papoušek, J., Řihák, J., Stanislav, V., Nižnan, J.: Elo-based learner modeling for the adaptive practice of facts. User Model. User-Adap. Inter. 27(1), 89–118 (2017)
Piech, C., et al.: Deep knowledge tracing. In: Advances in Neural Information Processing Systems, pp. 505–513 (2015)
Piech, C., et al.: Deep knowledge tracing. In: Advances in Neural Information Processing Systems, vol. 28, vol. 505–513 (2015)
Reddick, R.: Using a glicko-based algorithm to measure in-course learning. In: Proceedings of the Educational Data Mining Conference, pp. 754–759. ERIC (2019)
Tai, J., Ajjawi, R., Boud, D., Dawson, P., Panadero, E.: Developing evaluative judgement: enabling students to make decisions about the quality of work. High. Educ. 76(3), 467–481 (2018)
Wang, X., Talluri, S.T., Rose, C., Koedinger, K.: Upgrade: sourcing student open-ended solutions to create scalable learning opportunities. In: Proceedings of the 6th ACM Conference on Learning@ Scale, pp. 1–10, June 2019
Zahirović Suhonjić, A., Despotović-Zrakić, M., Labus, A., Bogdanović, Z., Barać, D.: Fostering students’ participation in creating educational content through crowdsourcing. Interact. Learn. Environ. 27(1), 72–85 (2019)
Zhao, S., Wang, C., Sahebi, S.: Modeling knowledge acquisition from multiple learning resource types. arXiv preprint arXiv:2006.13390 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Abdi, S., Khosravi, H., Sadiq, S., Darvishi, A. (2021). Open Learner Models for Multi-activity Educational Systems. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-78270-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78269-6
Online ISBN: 978-3-030-78270-2
eBook Packages: Computer ScienceComputer Science (R0)