基于DEA的在线学习者学习效率评价

Evaluation of the Efficiency of Online Learning Learners Based on Data Envelopment Analysis

针对现有在线学习者学习效率评价方法的局限性,本研究选取英国开放大学数据集中的一门STEAM课程,构建了在线学习者学习活动效率输入/输出评价体系,之后采用数据包络分析的方法,使用CCR模型、BCC模型、超效率模型与Malmquist指数四种模型对学习效率进行了分析。研究发现,此门课程学生综合效率偏低,其原因偏向于学生没有使用合适的学习方法与技巧,而并非学习活动投入时间不够;学生普遍存在投入不足与产出冗余的情况;超效率分析所区分出的综合效率有效的学生的差异较小;连续选修了两个学期课程的55位同学其全要素生产率提升,且与第二次课程评价结果一致。数据包络分析原理及方法对分析在线学习者的学习过程具有一定的适用性,但在模型应用与结果解释方面仍需要优化改进。

In view of the limitation of the existing online learners’learning efficiency evaluation methods,this study selects a STEAM course in the Open University UK learning analytics dataset to build online learners learning activity input/output efficiency evaluation system using the method of Data Envelopment Analysis(DEA).The learning efficiency is analyzed with CCR model,BCC model,super efficiency model and Malmquist Index model.The results suggest that the overall efficiency of students in this course is low,due to the fact that students do not use appropriate learning strategies and skills,rather than the lack of time devoted to learning activities.Students generally have the situation of insufficient input and output redundancy.The results of the super efficiency model show that the difference between the students whose efficiency values are equal to 1 is small.The total factor productivity of 55 students who have been enrolled in two consecutive semesters of courses is consistent with the results of the second course evaluation.The principles and methods of Data Envelopment Analysis are applicable to the analysis of online learners’learning process,but the application of the model and the interpretation of results still need to be improved.