【作 者】：
牟智佳
【关 键 词 】：
MOOCs； 学习分析； 学习结果预测； 学习行为指标； 数据挖掘
【栏 目】：
课程与教学
【中文摘要】：
设计具有可指导、可理解和可操作的系统化学习结果预测理论是改善学习成效的一种教学处方。研究从学习活动理论视角阐述了MOOCs环境下的学习过程与学习结果，并提出CIEO学习结果预测分析思想和学习结果预测工作模型。该模型包括理论层、参与层和行为层，其中，理论层包括个性化学习理论、项目反应理论和社会认知理论，分别对参与层中的学习内容、学习互动和学习评价进行指导；参与层是从学习活动的主要表现形式层面进行设计；行为层则遵循“目标—过程—结果”的分析思路，从学习行为的具体表现层面进行设计。在此基础上，对学习行为分析指标进行设计，形成面向学习结果的六类分析指标，即基于学习内容行为的完成度和掌握度、基于学习互动行为的参与度和贡献度、基于学习评价行为的测评完成度和通过率。最后，采用多元回归分析法对学习行为指标与学习结果的相关性进行探索；采用属性选择法、预测分类法、文本分析法对学习行为分析指标的重要性、预测准确率进行验证性分析，并得出学习结果预测计算方程。
【英文摘要】：
Designing systematic learning outcome prediction theory, which can be guided, understood and operated, is a prescription to improve learning effectiveness. From the perspective of learning activity theory, this paper expounds the learning process and learning outcomes in MOOCs environment, and proposes the analysis of CIEO learning outcome prediction and the working model of learning outcome prediction. The model includes theoretical level, participation level and behavioral level. Among them, the theoretical level covers personalized learning theory, project response theory and social cognitive theory, which guide the learning content, learning interaction and learning evaluation in the participation level respectively. The participation level is designed from the main manifestation of learning activities. The behavioral layer, following the "goal-process-result", is designed based on the specific performance of learning behaviors. Then, the indicators of learning behavior analysis are designed, and six kinds of indicators oriented to learning results are formed. That is, completion and mastery degree based on learning content, participation and contribution degree based on learning interaction, completion degree and pass rate based on learning evaluation behavior. Finally, multiple regression analysis is used to explore the correlation between learning behavior indicators and learning results. Attribute selection method, prediction classification and text analysis are used to verify the importance and accuracy of learning behavior analysis indicators, and the calculation equation of learning result prediction is obtained.