Concept-Aware Deep Knowledge Tracing and Exercise Recommendation in an Online Learning System
Concept-Aware Deep Knowledge Tracing and Exercise Recommendation in an Online Learning System
复制标题
DOI：
--
复制
发表时间：
2019-07
期刊：
影响因子：
--
通讯作者：
Fangzhe Ai;Yishuai Chen;Yuchun Guo;Yongxiang Zhao;Zhenzhu Wang;Guowei Fu;Guangyan Wang
中科院分区：
文献类型：
--
作者：
Fangzhe Ai;Yishuai Chen;Yuchun Guo;Yongxiang Zhao;Zhenzhu Wang;Guowei Fu;Guangyan Wang
个性化教育系统根据学生的能力向他们推荐学习内容，以加速他们的学习。本文提出了一个个性化的在线自主学习练习推荐系统。我们首先提高知识追踪模型的性能。现有的深度知识跟踪模型，如动态键值记忆网络（DKVMN），忽略了练习的概念标签，这通常是在辅导系统。我们对DKVMN进行了修改，设计了基于课程概念表的存储结构，并在学生知识追踪过程中显式地考虑了习题-概念的映射关系。我们在中国最大的教育集团之一的TAL的五年级学生数学练习数据集上对模型进行了评估，发现我们的模型比现有的模型有更高的性能。我们还增强了DKVMN模型，以支持更多的输入功能，并获得更高的性能。其次，我们使用该模型来构建学生模拟器，并使用它来训练具有深度强化学习的练习推荐策略。实验结果表明，我们的政策取得了更好的性能比现有的启发式政策方面，最大限度地提高学生的知识水平。据我们所知，这是深度强化学习首次应用于个性化数学练习。
Personalized education systems recommend learning contents to students based on their capacity to accelerate their learning. This paper proposes a personalized exercise recommendation system for online self-directed learning. We first improve the performance of knowledge tracing models. Existing deep knowledge tracing models, such as Dynamic Key-Value Memory Network (DKVMN), ignore exercises’ concept tags, which are usually available in tutoring systems. We modify DKVMN to design its memory structure based on the course’s concept list, and explicitly consider the exercise-concept mapping relationship during students’ knowledge tracing. We evaluated the model on the 5th grade students’ math exercising dataset in TAL, one of the biggest education groups in China, and found that our model has higher performance than existing models. We also enhance the DKVMN model to support more input features and obtain higher performance. Second, we use the model to build a student simulator, and use it to train an exercise recommendation policy with deep reinforcement learning. Experimental results show that our policy achieves better performance than existing heuristic policy in terms of maximizing the students’ knowledge level. To the best of our knowledge, this is the first time that deep reinforcement learning has been applied to personalized mathematic exercise