RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning Processes

• URL: http://arxiv.org/abs/2406.12465v1
• Date: Tue, 18 Jun 2024 10:16:18 GMT
• Title: RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning Processes
• Authors: Xiaoshan Yu, Chuan Qin, Dazhong Shen, Shangshang Yang, Haiping Ma, Hengshu Zhu, Xingyi Zhang,
• Abstract summary: We propose RIGL, a unified Reciprocal model to trace knowledge states at both the individual and group levels. In this paper, we introduce a time frame-aware reciprocal embedding module to concurrently model both student and group response interactions. We design a relation-guided temporal attentive network, comprised of dynamic graph modeling coupled with a temporal self-attention mechanism.
• Score: 22.379764500005503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the realm of education, both independent learning and group learning are esteemed as the most classic paradigms. The former allows learners to self-direct their studies, while the latter is typically characterized by teacher-directed scenarios. Recent studies in the field of intelligent education have leveraged deep temporal models to trace the learning process, capturing the dynamics of students' knowledge states, and have achieved remarkable performance. However, existing approaches have primarily focused on modeling the independent learning process, with the group learning paradigm receiving less attention. Moreover, the reciprocal effect between the two learning processes, especially their combined potential to foster holistic student development, remains inadequately explored. To this end, in this paper, we propose RIGL, a unified Reciprocal model to trace knowledge states at both the individual and group levels, drawing from the Independent and Group Learning processes. Specifically, we first introduce a time frame-aware reciprocal embedding module to concurrently model both student and group response interactions across various time frames. Subsequently, we employ reciprocal enhanced learning modeling to fully exploit the comprehensive and complementary information between the two behaviors. Furthermore, we design a relation-guided temporal attentive network, comprised of dynamic graph modeling coupled with a temporal self-attention mechanism. It is used to delve into the dynamic influence of individual and group interactions throughout the learning processes. Conclusively, we introduce a bias-aware contrastive learning module to bolster the stability of the model's training. Extensive experiments on four real-world educational datasets clearly demonstrate the effectiveness of the proposed RIGL model.

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