Improving students' performance in quantitative courses: The case of academic motivation and predictive analytics. Issue 1 (March 2016) - Rahal, Ahmad; Zainuba, Mohamed
Abstract: This 5 years longitudinal study explores and tests the effect of the combined use of some principles from the motivation achievement theories of educational psychology and predictive analytics (pedagogical innovation) on enhancing students' academic self-monitoring, engagement, and performance in a junior level quantitative business course. If and when unsatisfied with their class performance, or their predicted grade and likelihood of success of the pedagogical innovation, students in the post-innovation group were directed to either self-regulate their class engagement, and/or seek the intervention of the instructor for remedies to facilitate their success. Results show the post-innovation group outperforming the pre-innovation group with more As (+43%), Bs (+35%), with fewer Cs (−20%) supporting the hypothesis that the suggested innovation significantly improved students' performance. However, no significant improvement in the failure rate of the at-risk students (DFWs) was observed. While most students with high predicted probability of passing were able to self-regulate their academic engagement, only few of the at-risk students sought the intervention of the instructor, with the majority eventually succeeding in passing the course (some after several trials) due to their improved class engagement, and their perceptions of the instructor's positive role in facilitating their success. Highlights: Students played an active role in the development of the predictive tools. We provide predictive tools to positively influence academic performance. Real-time feedback enabled students to relate educational activities & future goals. Tools promoted self-efficacy, competence, and higher levels of class engagement.