Data-Driven Methods to Improve Student Learning from Online Courses

提高学生在线课程学习的数据驱动方法

• 批准号: 1418244
• 负责人: Noboru Matsuda
• 金额: $ 50.47万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1418244&HistoricalAwards=false
• 关键词: Data; Driven; Methods; Improve; Student

This research aims to improve student learning effectiveness, efficiency, and enjoyment in online courses by using online learning data to provide constructive feedback to course developers and instructors. The feedback provides assistance with the design and improvement of course activities and interactive instruction. Online course development is often guided primarily by the intuition of the instructor. This research seeks to enable course improvements using a data-driven approach by developing methods to measure the effects of course redesign. This project integrates diagnostic feedback on course content and activities, analytic methods and course-inspection tools for discovering barriers to student learning and opportunities for course improvement, and authoring tools to translate discoveries to course improvements. An Integrated Development Environment with Analytics (IDEA) that allows course developers and instructors to validate courseware contents before actual use will be developed. Once the courseware becomes available online and used by students, IDEA will support the use of logged data and analytics to discover barriers to learning, to hypothesize modifications in the underlying cognitive model of learning, and to evaluate and select the modifications that best predict the data. IDEA will provide performance profiling to developers and instructors to summarize students' learning and identify issues to be improved by analyzing learning curves and modifying knowledge component models. IDEA will also provide developers with a tight connection between performance profiling and courseware contents so that developers can review and modify troublesome courseware contents.

这项研究旨在通过使用在线学习数据为课程开发人员和讲师提供建设性的反馈，以提高在线课程中的学生学习效率，效率和享受。 反馈为课程活动和互动教学的设计和改进提供了帮助。 在线课程发展通常主要是由讲师的直觉来指导。 这项研究旨在通过开发方法来衡量当课程重新设计的效果的方法来使用数据驱动的方法来改进课程。 该项目集成了有关课程内容和活动，分析方法和课程调查工具的诊断反馈，以发现学生学习的障碍和课程改进的机会，并创建了将发现转化为改进课程的工具。具有分析（IDEA）的集成开发环境，允许课程开发人员和讲师在实际使用之前验证课程软件内容。课程软件在网上可用并被学生使用，Idea将支持使用记录的数据和分析来发现学习障碍，假设在基本的学习认知模型中进行修改，并评估和选择最能预测数据的修改。想法将为开发人员和讲师提供绩效分析，以总结学生的学习，并通过分析学习曲线和修改知识组件模型来确定要改善的问题。想法还将为开发人员提供性能分析和课件内容之间的紧密联系，以便开发人员可以查看和修改麻烦的课件内容。