Abstract
Traditional intelligent systems recommend a teaching sequence to individual students without monitoring their ongoing learning attitude. It causes frustrations for students to learn a new skill and move them away from their target learning goal. As a step to make the best teaching strategy, in this paper a Personalized Skill-Based Math Recommender (PSBMR) framework has been proposed to automatically recommend pedagogical instructions based on a student’s learning progress over time. The PSBMR utilizes an adversarial bandit in contrast to the classic multi-armed bandit (MAB) problem to estimate the student’s ability and recommend the task as per his skill level. However, this paper proposes an online learning approach to model a student concept learning profile and used the Exp3 algorithm for optimal task selection. To verify the framework, simulated students with different behavioral complexity have been modeled using the Q-matrix approach based on item response theory. The simulation study demonstrates the effectiveness of this framework to act fairly with different groups of students to acquire the necessary skills to learn basic mathematics.
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Acknowledgment
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A4A1023746, No. 2019R1F1A1060799).
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Islam, M.Z., Mehmood, K., Kim, H.S. (2020). Reinforcement Learning Based Interactive Agent for Personalized Mathematical Skill Enhancement. In: Slamanig, D., Tsigaridas, E., Zafeirakopoulos, Z. (eds) Mathematical Aspects of Computer and Information Sciences. MACIS 2019. Lecture Notes in Computer Science(), vol 11989. Springer, Cham. https://doi.org/10.1007/978-3-030-43120-4_31
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