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    面向作答序列数据的情境自适应学习推荐方法

    Context-Aware Adaptive Exercise Recommendation Method for Response Sequences Data

    • 摘要: 学习推荐作为智能教育领域的核心研究任务,目的是根据学习者的认知状态,提供个性化的学习资源. 现有的学习推荐方法依赖于学习者对知识概念的掌握程度,缺少对作答序列数据中情境信息的充分挖掘,无法有效提高学习者的知识迁移能力. 提出了面向作答序列数据的情境自适应学习推荐方法. 该方法构建包括知识概念情境和习题情境的学习情境感知表征模块,融合时序大语言模型的认知状态表征模块,实现基于知识图谱的情境自适应学习推荐模块,将习题、知识概念和学习者特征进行动态关联,依据学习情境特征实现自适应精准推荐,解决了学习情境自适应推荐难的问题,提高了模型的情境感知和自适应能力,在保证学习效果的同时,增强了学习过程的新颖性以及学习推荐系统的效率和准确度. 在AAAI 2023和NeurIPS_t34数据集上的比较和消融实验表明,所提模型的准确率和新颖性均优于基线模型.

       

      Abstract: Efficient exercise recommendation is crucial in intelligent education to provide learners with personalized learning resources based on their cognitive states. Existing methods often rely on learners’ mastery levels of knowledge concepts, yet critically overlook contextual information embedded in response sequences data, thus limiting the enhancement of learners’ knowledge transfer abilities. This study introduces a context-aware adaptive exercise recommendation method for response sequences data (CAE-RRS). By constructing a learning context representation module that integrates knowledge concept and exercise contexts, the method offers a richer contextual representation. Furthermore, it incorporates a cognitive state representation module fused with temporal large language models (LLMs) and implements a context-aware adaptive exercise recommendation module based on knowledge graphs. This module dynamically links exercises, knowledge concepts, and learner characteristics, enabling precise and adaptive recommendations according to learning context features. It effectively addresses the challenge of context-aware adaptive recommendations, bolstering the model’s contextual perception and adaptability. Consequently, it ensures learning effectiveness while enhancing the novelty of the learning process and the efficiency and accuracy of exercise recommendation systems. Comparative and ablation experiments across the AAAI 2023 and NeurIPS_t34 datasets demonstrate that our model surpasses baseline models in both accuracy and novelty.

       

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