Transformative teaching: Adaptive active learning for higher machine learning education

Abstract

Active learning has gained immense attention in higher learning education in recent years. It is an instructional approach that engages students in the learning process through activities and experiences, as opposed to the traditional methods of learning where students passively receive information from teachers. In this project, we aim to address the pervasive challenge of passive learning in higher education, and assess the potential for improving active learning strategies within the domains of Bioinformatics and GIS (geographic information systems) by taking specific courses on machine learning in two disciplines. The project combines pedagogical theories such as Bloom's Taxonomy and constructivist principles with insights from seminal works by education scholars like Dewey and Freire. We propose comprehensive solutions, spanning from enriched educational contexts to dynamic assessments. This study endeavors to assess the impact of our innovations on student learning, culminating in a process report evaluating the collaborative journey of our interdisciplinary group. Through this endeavor, we aspire to contribute to the broader discourse on transformative teaching methodologies.