Abstract
Learning algorithmic skills is fundamental for computer science students, as it develops critical thinking and problem-solving abilities. These skills enable students to analyze real-world problems and design efficient, practical solutions. Designing algorithms requires abstract thinking, problem solving, and solid mathematical backgrounds making algorithm design challenging. More importantly, due to the lack of conceptual understanding, students usually find it difficult to relate different algorithms to solve a similar problem. Also, it is challenging to modify an algorithm designed for one problem to solve another. Although many pedagogies have been proposed for teaching algorithms, many students still find the subject difficult.The Decoding the Disciplines Paradigm (DDP) has been successfully employed to facilitate meaningful understanding in many disciplines. However, little attention has been given to employing DDP for teaching algorithms in large cohorts with hundreds of students from diverse backgrounds. This study evaluates the DDP from a student perspective in teaching algorithm design. To teach algorithms using the DDP first, the bottlenecks in learning algorithms are identified, and the thought processes aredissected. Then, the instructor models the thought processes to students, who are required to work through the bottleneck. For meaningful understanding, the instructor provides opportunities to help students practice through the modeled steps. This study evaluates the advantages and disadvantages of the DDP and traditional teaching in teaching algorithm design from a student perspective via a questionnaire in a large cohort of international students. Our results show that majority of students preferred the DDP method for learning algorithms in all aspects of teaching and learning.
Bibliographic data
Yaqoob, M., Tayarani-Najaran, M. H., Singh, H., Khan, J. A., & Mehmood, T. (2025). Evaluating the Decoding the Disciplines Paradigm From a Student Perspective in Teaching Algorithm Design. Human Behavior and Emerging Technologies, 2025(1), 9930449.
