A Long-Life Predictive Guidance with Homogeneous Competence Promotion for University Teaching Design

Abstract

Even though planning the educational action to optimize student performance is a very complex task, teachers typically face this challenging issue with no external assistance. Previous experience is, in most cases, the main driving force in curriculum design. This procedure commonly overlooks the students’ perception and weakly integrates the students’ feedback by using a non-systematic approach. Furthermore, transverse competences are, unfortunately, typically omitted in this procedure. This work suggests the use of a predictive tool that determines the optimal application time of different methodological instruments. The suggested method can be used for an infinite number of scenarios of promoted competences. The results can be regarded as a guide to modify the course structure, but, more importantly, it offers valuable information to understand better what is occurring in the teaching-learning process and detect anomalies in the subject and avoid the students’ exclusion. The predictive scheme simultaneously considers the teacher’s perspective, the student’s feedback, and the previous scores in a systematic manner. Therefore, results provide a broader picture of the educational process. The proposal is assessed in a course of Electrical Machines at the University of Malaga during the academic year 2021–2022.

Keywords

• education
• course design
• model predictive control