Aplicação de estratégias de gamificação personalizadas no ensino de algoritmos e programação em curso introdutório de computação

Abstract

This dissertation, structured as a collection of academic articles, investigates the use of personalized gamification strategies in programming education, focusing on pedagogical alignment with students’ motivational profiles and learning styles. The work comprises three interdependent articles that complement each other in addressing how to personalize the teaching of algorithms and programming to foster greater engagement, motivation, and academic performance The first article, titled “Personalized Gamification Strategies in Programming Education: A Systematic Literature Review”, published in the proceedings of SBIE 2024, presents a systematic review of 45 studies published between 2019 and 2023. Its aim was to identify the effects of competitive, cooperative, and hybrid gamification elements in programming education. The results revealed important patterns regarding the impact of these strategies on student engagement and highlighted gaps related to personalization and the use of adaptive feedback, providing a theoretical foundation for the subsequent applied studies. The second article, “Personalized Gamification in Programming Education: An Experience Report with Adaptive Feedback”, awarded at EDUCOMP 2025, reports on the application of a pedagogical methodology based on the HEXAD model (motivational profiles) and the GRSLSS model (learning styles). The approach was implemented in a technical computing class, where students were categorized into specific profiles and received gamified activities tailored to their characteristics. The experience revealed increased engagement, greater autonomy, and more active participation in the proposed tasks. The third article, “Applying Personalized Gamification Strategies in Teaching Algorithms and Programming in an Introductory Computing Course”, represents the final stage of the project and was presented to the dissertation committee. It describes the application of two main activities: the first focused on programming logic (Animal Duel), and the second on Object-Oriented Programming (OOP), developed based on the adaptive feedback collected in the previous phase. The personalization of tasks and the use of individualized feedback led to significant improvements in academic performance, content comprehension, and student motivation Overall, the three articles demonstrate that personalized gamification, combined with adaptive feedback, is an effective and replicable strategy to make programming education more inclusive and student-centered. This dissertation contributes to the field of Computer Science Education by providing empirical evidence and pedagogical practices that can be applied in diverse educational contexts.