Optimizing DSA Education Using Learning Strategies and Research Trends

As data structures and algorithms (DSA) continue to play a critical role in computer science education and industry recruitment, identifying the most effective learning methods for mastering these concepts is crucial. This paper reviews existing educational approaches, including project-based learning, inquiry-based learning, collaborative learning, flipped learning, and problem-based learning, to evaluate their applicability to DSA instruction. Through an analysis of the current research landscape, we identify significant gaps in empirical studies on non-project-based methods, particularly concerning their impact on DSA proficiency. Additionally, we provide a quantitative overview of the distribution of research efforts across these learning methods. The results indicate that further targeted study on different strategies is needed, especially to enhance the instruction and learning of intricate algorithms and data structures.