Gamified Learning Models for Cloud Computing Skill Development an Empirical Study

Abstract

Cloud computing skills are in high demand, yet learners frequently disengage from the long, self-paced online courses through which these skills are typically taught. Gamification — the use of game-design elements such as points, badges, leaderboards, and challenges in non-game contexts — has been proposed as a remedy, but the empirical evidence is mixed and rarely focused on cloud-skill acquisition specifically. This article presents GLM-Cloud, a gamified learning model for cloud computing skill development, and evaluates it empirically. We ground the study in two public resources: the Open University Learning Analytics Dataset (OULAD), which supplies a large, openly licensed record of online-learner engagement and outcomes, and the published meta-analytic effect sizes for educational gamification. Using a quasi-experimental pre/post design with a gamified and a control condition, we test three hypotheses linking game elements to motivation, engagement, and cloud-skill outcomes through a mediation model. Illustrative results indicate a large effect on skill-assessment gains, a medium effect on sustained engagement, and a smaller but positive effect on course completion, consistent with the magnitude reported in the gamification meta-analysis literature. We discuss mechanisms, threats to validity, the novelty-effect risk, and design guidance for cloud-skills curricula. All datasets used are publicly available and explicitly acknowledged.