Exercise-Enhanced Sequential Modeling for Student Performance Prediction
Exercise-Enhanced Sequential Modeling for Student Performance Prediction
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DOI：
10.1609/aaai.v32i1.11864
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发表时间：
2018-04
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通讯作者：
Yu Su;Qingwen Liu;Qi Liu;Zhenya Huang;Yu Yin;Enhong Chen;Chris H. Q. Ding;Si Wei;Guoping Hu-G
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文献类型：
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作者：
Yu Su;Qingwen Liu;Qi Liu;Zhenya Huang;Yu Yin;Enhong Chen;Chris H. Q. Ding;Si Wei;Guoping Hu-G
在在线教育系统中，为了向学生提供主动服务（如个性化运动推荐），预测学生在未来运动活动中的表现（如分数）是一个至关重要的需求。现有的预测方法主要是利用学生的历史练习记录，每个练习通常用人工标注的知识概念来表示，而练习的文本描述中所包含的更丰富的信息还有待开发。在本文中，我们提出了一种新的运动增强递归神经网络（EERNN）框架，通过充分利用学生的运动记录和每次运动的文本来预测学生的成绩。具体来说，为了对学生运动过程进行建模，我们首先设计了一个双向LSTM，在没有任何专业知识和信息损失的情况下从文本描述中学习每个运动表示。然后，我们提出了一种新的LSTM架构，通过组合练习表示来跟踪学生在顺序练习过程中的状态（即知识状态）。为了进行最终的预测，我们在EERNN下设计了两种策略，即具有马尔可夫性质的EERNNM和具有注意机制的EERNNA。在大规模真实数据上的大量实验清楚地证明了EERNN框架的有效性。此外，通过纳入运动相关性，EERNN可以从学生和运动的角度很好地处理冷启动问题。
In online education systems, for offering proactive services to students (e.g., personalized exercise recommendation), a crucial demand is to predict student performance (e.g., scores) on future exercising activities. Existing prediction methods mainly exploit the historical exercising records of students, where each exercise is usually represented as the manually labeled knowledge concepts, and the richer information contained in the text description of exercises is still underexplored. In this paper, we propose a novel Exercise-Enhanced Recurrent Neural Network (EERNN) framework for student performance prediction by taking full advantage of both student exercising records and the text of each exercise. Specifically, for modeling the student exercising process, we first design a bidirectional LSTM to learn each exercise representation from its text description without any expertise and information loss. Then, we propose a new LSTM architecture to trace student states (i.e., knowledge states) in their sequential exercising process with the combination of exercise representations. For making final predictions, we design two strategies under EERNN, i.e., EERNNM with Markov property and EERNNA with Attention mechanism. Extensive experiments on large-scale real-world data clearly demonstrate the effectiveness of EERNN framework. Moreover, by incorporating the exercise correlations, EERNN can well deal with the cold start problems from both student and exercise perspectives.