Purpose: The research objective is to address the problem of students’ attrition by identifying students who are liable to fail their courses. Given that the blended courses have been on the boom, our research is directed into identifying students at risk in blended courses with a view to controlling attrition.
Design/methodology/approach: Students’ behavioral engagement data were analyzed in terms of a binary logistics regression with a view to developing a model to decide on the risk factors. A binary variable was modeled to describe students at risk and students not at risk. The students’ behavioral engagement data constituted the independent variables in our regression analysis whereas the variable describing students at risk was the dependent variable. The students’ behavioral engagement data was collected by students’ learning activities. The elearning part was implemented by MS-Teams. The data were collected after the final test. The regression analysis outcome was a classification table indicating the correct classification percentage of our model.
Findings: Factors related to the conventional part of the course-delivery process were not deemed to be significant. On the other hand, factors related to the elearning part, such as the number of the assessment quizzes completed and the total logins into MS-Teams appeared to play a cardinal role in the students’ critical performance.
Practical implications: Our model could be verified by being applied to a plethora of blended courses with a view to generating a prediction model for students at risk for blended courses that share the same learning design.
Originality/value: The added value of our research is centered on the fact that our model could potentially be applied to any blended course in order to come up with the respective risk factors. The originality of our research lies in the fact that the issue of controlling students’ attrition is not addressed in a fragmentary way. Thereby, a concrete methodology was developed on the basis of an established generic risk management framework.