使用鼠标交互功能预测学生在交互式在线题库中的表现(Human-Computer Interaction)
- 2020 年 1 月 14 日
- 筆記
建立学生学习模型并进一步预测学习效果是网络学习中一个很好的任务，通过根据不同学生的需求向他们推荐不同的学习资源，对个性化教育至关重要。互动式网络题库(如教育游戏平台)是网络教育的重要组成部分，近年来越来越受欢迎。然而，现有的大多数关于学生成绩预选的工作都是针对在线学习平台的，这些平台拥有结构良好的课程、预定义的问题顺序和领域专家提供的准确的知识标签。目前还不清楚的是，如果没有这些组织良好的问题顺序或专家的知识标签，如何在交互式在线题库中进行学生成绩预测。本文提出了一种基于学生交互特征和问题间相似性的在线互动题库预测方法。特别地，我们根据学生的鼠标移动轨迹引入新的特性(例如，思考时间、第一次尝试和第一次拖放)来描述学生解决问题的方式。此外，将异构信息网络应用于整合学生在类似问题上的历史解题信息，提高学生对新问题的成绩预测。我们使用四种典型的中文学习模型，从真实世界的交互问题池中评估所提出的方法。结果表明，我们的方法在交互式在线题库中对学生成绩预测的准确性比传统方法在各种模型中仅使用统计特征(如学生的历史成绩)要高得多。我们进一步讨论了我们的方法在不同预测模型和问题类之间的性能一致性，以及所提出的交互特性的重要性。
原文题目：Predicting Student Performance in Interactive Online Question Pools Using Mouse Interaction Features
原文：Modeling student learning and further predicting the performance is a well-established task in online learning and is crucial to person- alized 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 pre- diction 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 fur- ther considering student interaction features and the similarity be- tween 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 de- tails. 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 ma- chine learning models. The result shows that our approach can achieve a much higher accuracy for student performance predic- tion in interactive online question pools than the traditional way of only using the statistical features (e.g., students’ historical ques- tion scores) in various models. We further discuss the performance consistency of our approach across different prediction models and question classes, as well as the importance of the proposed interaction features in detail.
原文作者：Huan Wei, Haotian Li, Meng Xia, Yong Wang, Huamin Qu
原文地址： https://arxiv.org/abs/2001.03012
