Abstract
Recommending challenging and suitable exercises to students in an online learning environment is important, as it helps to stimulate their engagement and motivation. This requires considering their individual goals to improve learning efficiency on one side and on the other to provide tasks with an appropriate difficulty for the particular person. Apparently, this is not a trivial issue, and various approaches have been investigated in the areas of adaptive assessment and dynamic difficulty adjustment. Here, we present a solution for the domain of mathematics that rests on two pillars: Reinforcement Learning (RL) and Item Response Theory (IRT). Specifically, we investigated the effectiveness of two RL algorithms in recommending mathematical tasks to a sample of 125 first-year Bachelor’s students of computer science. Our recommendation was based on the Estimated Total Score (ETS) and item difficulty estimates derived from IRT. The results suggest that this method allowed for personalized and adaptive recommendations of items within the user-selected threshold while avoiding those with an already achieved target score. Experiments were performed on a real data set to demonstrate the potential of this approach in domains where task performance can be rigorously measured.
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
Arey, D., Wells, E.: Balancing act: «the art and science of dynamic difficulty adjustment». In: Game Developers Conference (2001)
Chalmers, R.P.: Mirt: a multidimensional item response theory package for the R environment. J. Stat. Softw. 48, 1–29 (2012)
Constant, T., Levieux, G.: Dynamic difficulty adjustment impact on players’ confidence. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2019)
Csikszentmihalyi, M.: Flow. The Psychology of Optimal Experience. New York (Harperperennial) (1990)
Embretson, S.E., Reise, S.P.: Item Response Theory. Psychology Press (2013)
Hill, A., et al.: Stable baselines3 (2020). https://github.com/DLR-RM/stable-baselines3
Hori, K., Fukuhara, H., Yamada, T.: Item response theory and its applications in educational measurement part i: item response theory and its implementation in R. WIREs Comput. Stat. 14(2), e1531 (2022)
Leite, W.L., et al.: A novel video recommendation system for algebra: an effectiveness evaluation study. Association for Computing Machinery, New York (2022)
Lopes, J.C., Lopes, R.P.: A review of dynamic difficulty adjustment methods for serious games. In: Pereira, A.I., Košir, A., Fernandes, F.P., Pacheco, M.F., Teixeira, J.P., Lopes, R.P. (eds.) OL2A 2022. CCIS, vol. 1754, pp. 144–159. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-23236-7_11
Mnih, V., et al.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928–1937 (2016)
AI for Research: Student goal setting: an evidence-based practice student goal setting (2018)
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. In: International Conference on Learning Representations (2017)
Stelnicki, A.M., Nordstokke, D.W., Saklofske, D.H.: who is the successful university student? an analysis of personal resources. Can. J. High. Educ. 45(2), 214–228 (2015)
Sutton, R.S., Barto, A.G.: Reinforcement learning: an introduction. J. Artif. Intell. Res. 4, 1–53 (1998)
Tvarožek, J., Kravčík, M., Bieliková, M.: Towards computerized adaptive assessment based on structured tasks. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 224–234. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-70987-9_25
Vygotsky, L.S., Cole, M.: Mind in Society: Development of Higher Psychological Processes. Harvard University Press (1978)
Xue, S., Wu, M., Kolen, J., Aghdaie, N., Zaman, K.A.: Dynamic difficulty adjustment for maximized engagement in digital games. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 465–471 (2017)
Zhang, Y., Goh, W.: Personalized task difficulty adaptation based on reinforcement learning. User Model. User-Adap. Inter. 31, 753–784 (2021)
Acknowledgments
The authors would like to thank the German Federal Ministry of Education and Research (BMBF) for their kind support within the project Personalisierte Kompetenzentwicklung und hybrides KI-Mentoring (tech4compKI) under the project id 16DHB2208.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Orsoni, M., Pögelt, A., Duong-Trung, N., Benassi, M., Kravcik, M., Grüttmüller, M. (2023). Recommending Mathematical Tasks Based on Reinforcement Learning and Item Response Theory. In: Frasson, C., Mylonas, P., Troussas, C. (eds) Augmented Intelligence and Intelligent Tutoring Systems. ITS 2023. Lecture Notes in Computer Science, vol 13891. Springer, Cham. https://doi.org/10.1007/978-3-031-32883-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-32883-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-32882-4
Online ISBN: 978-3-031-32883-1
eBook Packages: Computer ScienceComputer Science (R0)