Analysing and predicting learning behaviours in computer science: A case study of the blended teaching mode in Digital Signal Processing course

Blended learning is the latest and inevitable trend in the development of education. Although blended learning research is on the rise, fewer studies examine the learning behaviour of college students in blended learning environments. This study aimed to investigate the learning behaviours of students in the field of computer science and examine these behaviours using data mining algorithms, taking the teaching practice of the Digital Signal Processing course as a case study. A total of 18 behavioural indicators were extracted and divided into three categories: basic learning behaviours, self-regulated learning behaviours, and extended learning behaviours. Data analysis of the behavioural indicators yielded the following conclusions: (1) Students did not have the habit of watching course playback and were less receptive to multiple online learning platforms; (2) Students' midterm performance and duration of livestream watching directly affected their basic learning behaviours, with all indicators of self-regulated and extended learning behaviours showing significant correlations; (3) The clustering of learning behaviours yielded four different learner patterns, which calls for personalised teaching strategies; (4) The random forest algorithm had an accuracy of 95.4% in predicting performance of the four types of learners.