Self-directed Learning Compared to Traditional Engineering Approach: Case Studies in Developing Machine Learning Capabilities to Solve Practical Problems

In this work, the authors present the study from personal experience to gain some insight into the different aspects of efficient self-directed learning. Two case studies regarding the self-directed learning approach and process are conducted and elaborated. The first is more focused on how learning objectives are achieved based on the implementation of detailed plans via concrete steps. The second one is more focused on the online learning of Reinforcement Learning. This work also provides insight from the perspective and teaching experience of AI/ML instructors. Overall, the proposed approach of self-directed learning is an individual initiative, which involves consultation with mentors/professors, formulation of learning goals, comprehension of knowledge, assessment of knowledge, monitoring of minds-on and hands-on training, identification of resources, planning of learning roadmaps, and evaluation of learning outcomes. The proposed approach is also applicable for self-directed learning of other engineering and computer science courses.