Motivational Impact and Promotion of Research Culture Through the Development of Deep Learning Models

Abstract

The professional training model in Colombia is moving from a competency-based approach to learning outcomes. This path is enriched with contributions both at the level of national policies and each institution, as well as the teaching experiences and interests of the students. In this environment, this research presents the impact on the motivational level, and on the development of research skills, in undergraduate students of the electrical area generated by implementing a training scheme that involves developing research formulated and guided by the students themselves. The students come from a model based on labor competencies, and the current curricular reform proposes a new model based on learning outcomes that demands a closer follow-up of the training process of each individual. The experimental group consisted of students in a new academic space in artificial intelligence (AI), for which specific instructional material was designed so that students could build deep learning classification models for real problems while implementing a scientific development scheme that includes the generation of scientific publications. The control group maintained a traditional training scheme based on content tracking and periodic written evaluations. The results showed that although both groups achieved a high level of learning effectiveness, the experimental group demonstrated more remarkable development in their creative thinking and more significant appropriation of the academic space and the training process, with a solid self-critical position. The research concluded that the follow-up model, as well as the material developed, manages to increase the student’s commitment to their training process and to generate positions on how this process is developed.