融合数据驱动生成在线学习迷航模型的构建及验证

Construction and Verification of Online Learning Puzzle Model Driven Based on Fusion Data

研究在数据驱动技术支持下,聚焦在线学习者的学习行为表征学习迷航状态,探讨精准识别在线学习迷航状态,提高学习者的在线学习效率、效益和成效。通过数据收集与预处理形成干净且标准的数据,研究基于XGBoost的在线学习迷航诊断方法,通过数据验证方法的有效性,提供技术保障。研究设计了迷航诊断系统,通过应用效果分析,验证了迷航诊断系统在提高学习者学习效用的同时,确实可以有效降低学习者的在线学习迷航程度,实现在线学习迷航模型的有效落地。

With the support of data-driven technology,the study focused on online learners'learning behavior to represent learning puzzle and discussed how to accurately identify online learning puzzle,so as to improve learners'online learning efficiency and effectiveness.Clean and standard data were formed through data collection and preprocessing,and the XGBoost-based online learning puzzle diagnosis method was studied to verify the effectiveness and provide technical support.The research and design of the online learning puzzle diagnosis system verified that it can effectively reduce the degree of learners’online learning puzzle while improving the learning effectiveness through the analysis of application effects and realize the effectiveness of online learning puzzle model.