Abstract
In this paper, we present an approach for personalizing the sequencing of learning activities that relies on the Q-learning. The Q-learning learns a sequencing policy to select learning activities that aims to maximize the learning gain of students.
On the one hand, the core of this approach is the use of the Bayesian knowledge tracing (BKT) to model the student knowledge state and to define the Q-Learning reward function. On the other hand, we defined with experts rules to generate simulated students. These simulated data were used to initialize the Q-table of the Q-Learning and answer its“cold start” problem.
We present empirical results showing that the sequencing policy learned from the expert-based initialization of the Q-table provides the system with an efficient strategy to improve the students’ knowledge states in comparaison with the Q-table randomly initialized. We further show that Q-Learning approach based on the knowledge states of the students inferred by the BKT are promising way for adaptive instruction in intelligent tutoring systems.
ANR PROJECT IECARE
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aleven, V., McLaughlin, E.A., Glenn, R.A., Koedinger, K.R.: Instruction based on adaptive learning technologies. Handb. Res. Learn. Instr. 522–560 (2016)
Bassen, J., et al.: Reinforcement learning for the adaptive scheduling of educational activities. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2020)
Corbett, A.T., Anderson, J.R.: Knowledge tracing: modeling the acquisition of procedural knowledge. User Model. User-Adap. Inter. 4(4), 253–278 (1994). https://doi.org/10.1007/BF01099821
Doroudi, S., Aleven, V., Brunskill, E.: Where’s the reward? Int. J. Artif. Intell. Educ. 29(4), 568–620 (2019). https://doi.org/10.1007/s40593-019-00187-x
Efremov, A., Ghosh, A., Singla, A.: Zero-shot learning of hint policy via reinforcement learning and program synthesis. In: EDM (2020)
Watkins, C.J.C.H.: Learning from delayed rewards (1989)
Yudelson, M.V., Koedinger, K.R., Gordon, G.J.: Individualized Bayesian knowledge tracing models. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS (LNAI), vol. 7926, pp. 171–180. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39112-5_18
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yessad, A. (2022). Personalizing the Sequencing of Learning Activities by Using the Q-Learning and the Bayesian Knowledge Tracing. In: Hilliger, I., Muñoz-Merino, P.J., De Laet, T., Ortega-Arranz, A., Farrell, T. (eds) Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption. EC-TEL 2022. Lecture Notes in Computer Science, vol 13450. Springer, Cham. https://doi.org/10.1007/978-3-031-16290-9_61
Download citation
DOI: https://doi.org/10.1007/978-3-031-16290-9_61
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16289-3
Online ISBN: 978-3-031-16290-9
eBook Packages: Computer ScienceComputer Science (R0)