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Towards Goal-oriented Intelligent Tutoring Systems in Online Education

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arxiv 2312.10053 v2 pith:PV7OORHG submitted 2023-12-03 cs.CY cs.AIcs.IR

Yang Deng , Zifeng Ren , An Zhang , Tat-Seng Chua This is my paper · ORCID
classification cs.CYcs.AIcs.IR
keywords learninggitsgoal-orientedintelligentplanningsystemstutoringaction
verification ladder T0 review T1 audit T2 compute T3 formal
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Interactive Intelligent Tutoring Systems (ITSs) enhance traditional ITSs by promoting effective learning through interactions and problem resolution in online education. Yet, proactive engagement, prioritizing resource optimization with planning and assessment capabilities, is often overlooked in current ITS designs. In this work, we investigate a new task, named Goal-oriented Intelligent Tutoring Systems (GITS), which aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment. To address the problem of goal-oriented policy learning in GITS, we propose a novel graph-based reinforcement learning framework, named Planning-Assessment-Interaction (PAI). Specifically, we first leverage cognitive structure information to improve state representation learning and action selection for planning the next action, which can be either to tutor an exercise or to assess the target concept. Further, we use a dynamically updated cognitive diagnosis model to simulate student responses to exercises and concepts. Three benchmark datasets across different subjects are constructed for enabling offline academic research on GITS. Experimental results demonstrate the effectiveness and efficiency of PAI and extensive analyses of various types of students are conducted to showcase the challenges in this task.

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Cited by 3 Pith papers

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