Modeling student learning and further predicting the performance is a well-established task in online learning and is crucial to personalized education by recommending different learning resources to different students based on their needs. Interactive online question pools (e.g., educational game platforms), an important component of online education, have become increasingly popular in recent years. However, most existing work on student performance prediction targets at online learning platforms with a well-structured curriculum, predefined question order and accurate knowledge tags provided by domain experts. It remains unclear how to conduct student performance prediction in interactive online question pools without such well-organized question orders or knowledge tags by experts. In this paper, we propose a novel approach to boost student performance prediction in interactive online question pools by further considering student interaction features and the similarity between questions. Specifically, we introduce new features (e.g., think time, first attempt, and first drag-and-drop) based on student mouse movement trajectories to delineate students' problem-solving details. In addition, heterogeneous information network is applied to integrating students' historical problem-solving information on similar questions, enhancing student performance predictions on a new question. We evaluate the proposed approach on the dataset from a real-world interactive question pool using four typical machine learning models.

中文翻译：

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使用鼠标交互功能预测学生在交互式在线问题池中的表现

对学生学习进行建模并进一步预测表现是在线学习中一项成熟的任务，并且通过根据不同学生的需求向不同学生推荐不同的学习资源，这对于个性化教育至关重要。交互式在线问题池（例如，教育游戏平台）作为在线教育的重要组成部分，近年来变得越来越流行。然而，大多数现有的关于学生表现预测目标的工作都是在具有结构良好的课程、预定义的问题顺序和领域专家提供的准确知识标签的在线学习平台上进行的。目前还不清楚如何在没有专家这样组织良好的问题顺序或知识标签的交互式在线问题池中进行学生表现预测。在本文中，我们提出了一种通过进一步考虑学生交互特征和问题之间的相似性来提高交互式在线问题池中学生表现预测的新方法。具体来说，我们引入了基于学生鼠标移动轨迹的新功能（例如，思考时间、第一次尝试和第一次拖放）来描绘学生解决问题的细节。此外，异构信息网络被应用于整合学生对类似问题的历史解题信息，增强学生对新问题的表现预测。我们使用四种典型的机器学习模型在来自真实世界交互式问题池的数据集上评估所提出的方法。并首先拖放）基于学生鼠标移动轨迹来描绘学生解决问题的细节。此外，异构信息网络被应用于整合学生对类似问题的历史解题信息，增强学生对新问题的表现预测。我们使用四种典型的机器学习模型在来自真实世界交互式问题池的数据集上评估所提出的方法。并首先拖放）基于学生鼠标移动轨迹来描绘学生解决问题的细节。此外，异构信息网络被应用于整合学生对类似问题的历史解题信息，增强学生对新问题的表现预测。我们使用四种典型的机器学习模型在来自真实世界交互式问题池的数据集上评估所提出的方法。