To accurately analyse and evaluate the online learning situation of MOOC learners, this article focuses on MOOC learners and proposes innovative evaluation methods. First, using techniques such as association feature extraction and adaptive mining, the online learning behaviour data of MOOC learners are collected. Second, by analysing in detail the behavioural data of course access, video learning, homework submission, and daily grades, the external performance characteristics of MOOC learners' online learning behaviour can be quantitatively analysed. Finally, a multi-level fuzzy comprehensive evaluation method is used to construct a learning performance evaluation system, which quantitatively evaluates learning performance by setting evaluation factor sets, weight allocation, and comment sets. The test results show that the proposed method exhibits the highest feature analysis accuracy of 98% and performance evaluation accuracy of 95%.
Shan Li (Thu,) studied this question.