<![CDATA[Purpose—This study examines the effectiveness and structural impact of adaptive gamified learning environments on students’ computational thinking and real-world problem-solving in higher education. It specifically investigates whether Scratch-based adaptive gamification improves learning achievement and whether computational thinking contributes to students’ ability to solve authentic and complex problems. Design/methodology/approach—This study employed a quasi-experimental nonequivalent control group design involving 60 undergraduate informatics students, equally divided into an experimental group receiving Scratch-based adaptive gamified instruction and a control group receiving conventional instruction. Learning outcomes were assessed using pre-tests, post-tests, project-based assessments, and structured classroom observations. Descriptive statistics and Pearson’s chi-square test were used to evaluate academic achievement, while Partial Least Squares Structural Equation Modeling was applied to examine the structural relationships among adaptive gamification, computational thinking, and real-world problem-solving. Findings—The experimental group’s mean score increased from 65.30 in the pre-test to 84.70 in the post-test, representing a gain of 19.40 points, whereas the control group’s mean score increased from 64.90 to 72.50, representing a gain of 7.60 points. Pearson’s Chi-Square test confirmed a statistically significant association between the instructional method and categorized academic achievement, χ²(1, N = 60) = 5.690, p = .017, with Cramér’s V = .308. The structural model further showed that computational thinking positively affected real-world problem-solving (β = .301, p = .006), while adaptive gamification exerted a stronger positive direct effect on real-world problem-solving (β = .553, p < .001). However, the mediating role of computational thinking requires confirmation through a bootstrapped specific indirect-effect analysis. Originality/value—This study integrates quasi-experimental evidence of instructional effectiveness with PLS-SEM-based structural validation in a unified analytical framework. It extends adaptive gamification research beyond motivation, participation, and general academic achievement by focusing on higher-order cognitive outcomes and positioning Computational Thinking as a potential cognitive pathway connecting adaptive learning experiences with real-world problem-solving. Implications—Higher education institutions, lecturers, instructional designers, and educational technology developers should implement adaptive gamification as an integrated instructional system rather than as a superficial collection of rewards. Personalized challenges, progressive task difficulty, diagnostic feedback, repeated experimentation, and authentic project-based scenarios should be aligned with learning objectives to strengthen computational reasoning and transferable problem-solving capabilities. ]]>
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