Learning Analytics in MOOCs as an Instrument for Measuring Math Anxiety

Abstract

In this paper, math anxiety descriptions are extracted from Massive Open Online Course (MOOC) reviews using text mining techniques. Learners’ emotional states associated with math phobia represent substantial barriers to learning mathematics and acquiring basic mathematical knowledge required for future career success. MOOC platforms accumulate big sets of educational data, learners’ feedback being of particular research interest. Thirty-eight math MOOCs on Udemy and 1,898 learners’ reviews are investigated in this study. VADER sentiment analysis, k-means clustering of content with negative sentiment, and sentence embedding based on the Bidirectional Encoder Representations from Transformers (BERT) language model allow identifying a few clusters containing descriptions of various negative emotions related to bad math experiences in the past, a cluster with descriptions of regrets about missed opportunities due to negative attitudes towards math in the past, and a cluster describing gradual overcoming of math anxiety while progressing through a math MOOC. The constructed knowledge graph makes it possible to visualize some regularities pertaining to different negative emotions experienced by math MOOC learners.