Abstract
This research full paper presents two main outcomes: 1) a novel classification system for gamification implementations including proposed genres, and 2) a comprehensive study and categorization of existing DSA gamification applications and a discussion of genres absent existing applications. Gamification presents a great potential to improve user engagement, motivation, and learning in nearly all fields of study including computer science (CS) education. However, it lacks formalized study and comprehensive analysis in CS education, and thus what makes for effective gamification is still a key question. Rather than initially trying to examine and catalog existing gamification applications and studies across the breadth of CS education as a whole, this paper instead focuses on Data Structures and Algorithms (DSA) courses. In general, DSA courses tend to be difficult due to the inherent complexity and abstraction exhibited by the fundamental concepts. As such, gamification presents a potential opportunity to convey these complex ideas in meaningful and unique ways. To carry out this work, a literature review of current DSA gamification applications is presented, the applications are categorized, and the pros and cons analyzed. Based on this analysis, a classification system is created and two new abstract genres are identified: dynamic gamification and collaborative gamification development. Potential uses, benefits and detriments are suggested for these newly identified genres. With this analysis and classification of gamification along with the identification of new abstract genres, the practice of gamification in DSA coursework can be made more efficient and effective. Upon a more thorough understanding of DSA gamification, pedagogical considerations can be made to better aid teachers and instructors in the integration of gamification into existing curriculum. The paper also touches on the applicability of the classification system to CS gamification examples outside of DSA. © 2021 IEEE.