Exploring Strategies to Improve Learning Outcomes in Video Analytics and Machine Learning in Large Classes

The integration of Artificial Intelligence (AI) across various fields has transformed the educational landscape and demands a targeted approach to teaching AI in an academic setting. As lecturers aim to prepare students for an AI-driven future, they face various challenges arising from the complex and mathematical nature of AI. This paper explores the challenges of teaching and assessing AI modules in large classrooms by implementing a student-centred approach alongside formative assessment and feedback. It also examines issues related to the diversity of students' skill sets and learning style. This study was conducted on two different cohorts of the same module, Video Analytics and Machine Learning during 2022-2024. Two distinct cohorts were chosen to ensure unbiased conclusions in our study. By recognising and actively addressing these challenges, lecturers can more effectively equip students with the skills needed to navigate this rapidly evolving field. In conclusion, this study shows that implementing formative assessments like quizzes and student-centred approaches are highly beneficial in large classrooms and lead to a significant improvement in student performance and learning outcomes. In addition, the analysis shows that students who are more actively engaged with quizzes tend to score higher on the module. While the overall student feedback has been positive and there has been noticeable improvement in performance, it is important to recognise that there have been instances of unsatisfactory student outcomes as well.