Navigating the Pedagogical Landscape

Abstract

This study delves into machine learning (ML) education by conducting a comprehensive literature review, a targeted survey of ML lecturers in Dutch universities, and a comparative experiment. These methods aid in addressing the challenges of aligning teaching methods with the evolving nature of ML and the growing demands of the field, and fill in knowledge gaps on the success of different teaching methods in ML education. The paper investigates whether traditional methods are effective in equipping future engineers with the necessary skills for tomorrow's challenges, amidst the rapid advancement of ML and its applications. The literature review explores the range of teaching methods in ML education and not only, emphasizing a shift towards technology-enhanced and active learning approaches when teaching ML. A survey of ML lecturers explores the landscape of ML education in Dutch universities. The study investigates teaching methodologies, tools, and challenges, providing valuable insights into the evolving practices of ML instruction. Findings indicate a predominant trend towards adopting a blended approach, with lectures, projects, and group work forming core instructional methods. Virtual environments, active learning strategies, and staying informed through community engagement are highlighted. Word frequency and thematic analyses reveal key themes, emphasizing student-centric learning, practical application, and the integration of diverse teaching methods. Additionally, an experimental comparison of two teaching methods, lecture and jigsaw, sheds light on their seemingly similar efficacy when applied to the domain of ML education. The research contributes to the optimization of ML education practices, offering comprehensive insights for educators and policymakers.