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科学领域:

  • 教育技术的教育技术
  • 统计学 教育 统计学 教育
  • 学习分析学习分析

背景情况:

  • 具有有限数学背景的研究生在统计学方面扎.
  • 学习管理系统 (LMS) 和基于问题的学习 (PBL) 是常见的,但在混合方法研究中研究不足.
  • 在学习分析 (LA) 中的机器学习为理解学生行为提供了潜力.

研究的目的:

  • 在研究生统计学课程中检查Canvas上的学生参与模式和学习成果.
  • 探索LMS参与度,PBL和学业成绩之间的关系.
  • 用混合方法识别不同的学生参与行为.

主要方法:

  • 解释性的顺序混合方法设计.
  • 收集了LMS日志数据和31名研究生的调查回复.
  • 通过对日志数据进行K-means集群和对19名学生的采访进行主题分析.

主要成果:

  • 通过K-means集群确定了两个组:高性能 (LMS参与度较低) 和低性能 (LMS参与度较高).
  • 主题分析揭示了参与行为,评估角色,情感斗争,自我效能和感知学习的差异.
  • 低成绩的学生从结构化的指导和重复的接触中受益,更频繁地参与.

结论:

  • 在LMS上的学生参与模式有很大的不同,并且与毕业生统计数据中的表现相关.
  • 低成绩的学生从结构化的支持和频繁的参与中受益,而高成绩的学生则表现出积极主动的习惯.
  • 整合PBL和LMS功能的课程设计对于支持统计学中的多元化研究生学习者至关重要.