A flipped classroom approach for teaching a master’s course on artificial intelligence

Abstract

In this paper, I present a flipped classroom approach for teaching a master’s course on artificial intelligence. Traditional lectures from the classroom are outsourced to an open online course that contains high quality video lectures, step-by-step tutorials and demonstrations of intelligent algorithms, and self-tests, quizzes, and multiple-choice questions. Moreover, selected problems, or coding challenges, are cherry-picked from a suitable game-like coding development platform that rids both students and the teacher of having to implement much of the fundamental boilerplate code required to generate a suitable simulation environment in which students can implement and test their algorithms. Using the resources of the online course and the coding platform thus free up much valuable time for active learning in the classroom. These learning activities are carefully chosen to align with the intended learning outcomes, curriculum, and assessment to allow for learning to be constructed by the students themselves under guidance by the teacher. Thus, I perceive the teacher’s role as a facilitator for learning, much similar to that of a personal trainer or a coach. Emphasising problem-solving as key to achieving intended learning outcomes, the aim is to select problems that strike a balance between detailed step-by-step tutorials and highly open-ended problems. This paper consists of an overview of relevant literature, the course content and teaching methods, recent evaluation reports and a student evaluation survey, results from the final oral exams, and a discussion regarding some limiting frame factors, challenges with my approach, and future directions.