系统检测学习者卡在编程学习学习的系统
Hiroki Oka1, Ayumi Ohnishi1, Tsutomu Terada1
1Graduate School of Engineering, Kobe University, 1-1 Rokkodaicho, Nada, Kobe 657-8501, Hyogo, Japan.
识别编程学习者的困难至关重要. 本研究引入了一种多模式方法,使用源代码和心率数据来检测学生何时在心理上陷入困境,从而改善学习支持.
科学领域:
- 计算机辅助教育 计算机辅助教育
- 教育心理学教育心理学
- 人与计算机的交互
背景情况:
- 编程教育在识别滞留的学习者方面面临挑战,这会影响动机和效率.
- 目前的方法依赖于手动代码审查,这不足以衡量学习者真正的难度.
- 区分被卡住和深度思考是很困难的,仅仅从源代码分析.
研究的目的:
- 开发和评估一种检测编程学习者在心理上被困的方法.
- 通过整合多模式数据,提高识别学习者困难的准确性.
- 为遇到重大学习障碍的学习者提供及时的支持.
主要方法:
- 提出了一种新的方法来检测使用多式联络数据的学习者"卡住"的情况.
- 集成源代码分析与通过心率传感器测量心理状态.
- 开发了一个系统来汇总和向教练介绍检测到的困境.
主要成果:
- 与单一指标方法相比,拟议的多式联络方法显示出更好地检测了陷入困境的情况.
- 在实际编程讲座中的系统评估显示了合适的通知时间和感知到的有用性.
- 参与者的反表明,该系统有效地识别了无法解决问题或用代码表达解决方案的学习者.
结论:
- 多模式数据,结合代码和生理信号,增强了对学习者编程困难的检测.
- 开发的系统为教练提供了一个实用的工具,以更有效地识别和支持滞留的学习者.
- 这种方法可以帮助缓解因长时间的滞留而导致的动机下降和学习效率下降.
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