Identifying Factors of Students’ Failure in Blended Courses by Analyzing Students’ Engagement Data

Our modern era has brought about radical changes in the way courses are delivered and various teaching methods are being introduced to answer the purpose of meeting the modern learning challenges. On that account, the conventional way of teaching is giving place to a teaching method which combines conventional instructional strategies with contemporary learning trends. Thereby, a new course type has emerged, the blended course in the context of which online teaching and conventional instruction are efficiently mixed. This paper demonstrates a way to identify factors affecting students’ critical performance in blended courses through a binary logistics regression analysis on students’ engagement data. The binary logistics regression analysis has led to a risk model which identifies and prioritizes these factors in proportion to their contribution to the risk occurrence. The risk model is demonstrated in the context of two specific blended courses sharing the same learning design. Additionally, the outcome of the study has proved that factors related to the e-learning part have critically affected the students’ performance in the respective blended courses.