基于在MOOC论坛中深入讨论解决问题的预测模型的研究
Jiansheng Li1, Linlin Li1, Zhixin Zhu1
1Nanjing Normal University, No.122, Ninghai Road, Nanjing, 210097 Jiangsu Province China.
概括
这项研究使用机器预测模型分析了大规模开放在线课程 (MOOC) 论坛的讨论深度. 它确定了影响讨论深度的关键因素,以改善在线学习环境.
科学领域:
- 教育技术的教育技术
- 计算机科学 计算机科学
背景情况:
- 讨论论坛对于大规模开放在线课程 (MOOC) 的知识构建至关重要.
- 在论坛中学习者对学习者的互动有助于解决问题和更深入的理解.
研究的目的:
- 建立一个机器预测模型来分析MOOC论坛中解决问题的讨论的深度.
- 确定影响在线学习环境中讨论深度的最重要的因素.
主要方法:
- 从使用Selenium与Python的现代教育技术课程中收集的数据.
- 分析了来自11184名学生的讨论数据,涉及7个课程.
- 开发一个预测模型,包括讨论深度和其概率的公式.
主要成果:
- 该研究建立了一个模型来预测解决问题讨论的深度.
- 确定了影响MOOC论坛讨论深度的关键因素.
- 评估了预测模型的效率.
结论:
- 这些发现为MOOC论坛的有效教学策略提供了洞察力.
- 提供了建议,以加强在线课程中的学习者参与和知识构建.
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