Integration of Active Learning Techniques for Effective Delivery of Machine Learning Course - Bhoga, Ujwala; G., Vishnu Murthy; Prasad, D. V. R.; Rajasekhar Reddy, P. (Journal of Engineering Education Transformations, 2025)
The field of machine learning (ML) is rapidly advancing and has important implications across a wide variety of industries, spanning from the healthcare industry to the financial sector and beyond. Computers are given the ability to gain information from data and make projections or conclusions through the process of machine learning, which is comprised of complex algorithms, statistical techniques, and data processing procedures. Given the complex nature of machine learning and the fact that it is always evolving, providing it with the appropriate instruction may be a challenging endeavor. In the field of machine learning, the conventional educational methods often consist of theoretical lectures, textbooks, and examples that are predetermined. Despite the fact that these methods provide a fundamental understanding of the concepts underlying machine learning, they typically fail to successfully integrate the theoretical knowledge with the execution of those concepts in the actual world. The fact that this gap exists is a topic of significant concern, given that machine learning is primarily utilized to handle practical concerns and to enhance decision-making that is based on data. It is essential to employ effective instructional and learning strategies such as project-based learning (PBL), flipped classrooms, and case studies-based learning in order to address these challenges and ensure that students are adequately prepared to pursue careers in the field of machine learning. Through an examination of several ML course delivery strategies; this study seeks to address the urgent demand for better ML education. These strategies have the potential to improve the learning outcomes of students by bridging the gap between theoretical comprehension and practical application, as this article demonstrates. Through this exploration, we seek to identify and advocate for teaching practices that can better prepare students for the dynamic and evolving landscape of machine learning.