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An Exercise Collection Auto-Assembling Framework with Knowledge Tracing and Reinforcement Learning

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Abstract

In educational practice, teachers often need to manually assemble an exercise collection as a class quiz or a homework assignment. A well-assembled exercise collection needs to have the proper difficulty index and discrimination index so that it can better develop students’ abilities. In this paper, we propose an exercise collection auto-assembling framework, in which a teacher provides the target values of difficulty and discrimination indices and a qualified exercise collection is automatically assembled. The framework consists of two stages. At the answer prediction stage, a knowledge tracing model is utilized to predict the students’ answers to unseen exercises based on their history interaction records. In addition, to better represent the exercises in the model, we propose exercise embeddings and design a pre-training approach. At the collection assembling stage, we propose a deep reinforcement learning model to assemble the required exercise collection effectively. Since the knowledge tracing model in the first stage has different confidences in the predicted answers, it is also taken into account in the objective. Experimental results show the effectiveness and efficiency of the proposed framework.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, 100190, China

    Tian-Yu Zhao & Jian-Hua Feng

  2. Sanfan Chaoyang School Attached to Beijing Normal University, Beijing, 100190, China

    Man Zeng

Authors
  1. Tian-Yu Zhao
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  2. Man Zeng
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  3. Jian-Hua Feng
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Correspondence to Tian-Yu Zhao.

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Zhao, TY., Zeng, M. & Feng, JH. An Exercise Collection Auto-Assembling Framework with Knowledge Tracing and Reinforcement Learning. J. Comput. Sci. Technol. 37, 1105–1117 (2022). https://doi.org/10.1007/s11390-022-2412-2

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