Predicting Students' Outcomes in Blended Learning: An Empirical Investigation in the Higher Education Context

Abstract

The main goal of this research was to clarify which aspects of blended learning increase a student's knowledge level measured by the course final grade. The questionnaire-based survey was used for gathering students' attitudes towards some aspects of blended learning. A principal component analysis and hierarchical clustering of variables were applied to extract the components that describe dimensions of blended learning and represent the explanatory variables in a multiple regression model with student's final grade as a dependent variable. Using a Two-Step cluster analysis to reveal natural groupings based on the answers in the questionnaire, two clusters were formed having a statistically significant difference between the means of final grades. The research revealed that the organization of a course and the study material supporting face-to-face teaching are essential features with an impact on student's final success. The study also showed that the aspects of traditional face-to-face teaching are more strongly linked to higher grades than the aspects of e-courses.

Keywords

• blended learning
• education data mining
• higher education
• performance
• satisfaction