基于MOOC+SPOC平台的在线学习行为分析研究

Analysis & Research on the Online Learning Behavior Based on MOOC + SPOC Platform

为深入分析线上学习行为与学习成绩的关联,本文通过对MOOC和SPOC平台的在线学习行为数据进行分析研究,将在线学习的行为特征作为输入,构建基于BP神经网络和时间规律在线学习模型,成绩预测作为输出。同时提出学生在线学习成绩、教师评定成绩和期末成绩的综合评价机制,并从学生成绩和分析方法两个维度进行对比实验,模型预测成绩与学习者综合成绩情况基本一致,该模型能客观反映学生平时学习的状况,对在线学习、个性化推荐具有一定的参考价值。

In order to analyze deeply the relationship between online learning behavior and academic performance, this paper analyzes and researches the online learning behavior data of MOOC + SPOC platforms, takes the behavioral characteristics of online learning as the input, and constructs an online learning model based on BP neural network and time regularity. The academic performance prediction as output. At the same time, a comprehensive evaluation mechanism for students’ online learning performance, teachers’ evaluation performance and final results is proposed, and a comparative experiment is carried out from the two dimensions of student performance and analysis method. The predicted academic performance of the model are basically consistent with the comprehensive academic performance of learners. It has a certain reference value for online learning and personalized recommendation.