基于K-12数学网络学习平台的学生学习行为可视化分析
Visual Analytics of Student Learning Behaviors on K-12 Mathematics E-learning Platforms
摘要：
随着在线学习的日益普及，出现了大量的电子学习平台，以促进K-12（从幼儿园到12年级）学生的教育机会，并记录了大量关于他们学习日志的信息。然而，如何利用这些详细的学习行为数据来改进学习材料的设计，并更深入地了解学生的思维和学习风格，目前仍不清楚。在这项工作中，我们提出了一个可视化的分析系统来分析学生的学习行为的K-12数学网络学习平台。它支持不同属性之间的相关性分析和用户鼠标移动日志的详细可视化。我们对真实数据集的案例研究表明，我们的系统可以更好地指导学习资源的设计（例如，数学问题），并促进快速解释学生的解决问题和学习风格。
英文摘要：
With increasing popularity in online learning, a surge of E-learning platforms have emerged to facilitate education opportunities for k-12 (from kindergarten to 12th grade) students and with this, a wealth of information on their learning logs are getting recorded. However, it remains unclear how to make use of these detailed learning behavior data to improve the design of learning materials and gain deeper insight into students' thinking and learning styles. In this work, we propose a visual analytics system to analyze student learning behaviors on a K-12 mathematics E-learning platform. It supports both correlation analysis between different attributes and a detailed visualization of user mouse-movement logs. Our case studies on a real dataset show that our system can better guide the design of learning resources (e.g., math questions) and facilitate quick interpretation of students' problem-solving and learning styles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
