Evaluating gamified learning strategies in internet of things-based software engineering education

Abstract

This study examines the effectiveness of gamified formative assessment
in undergraduate internet of things (IoT) education, focusing on how
content complexity and question format influence student performance.
A quasi-experimental comparative design was employed, administering two gamified quizzes to 75 undergraduate students enrolled in two IoT-related courses DevOps for IoT (n=41) and human computer interaction in IoT (n=34) during spring 2025. The gamified platform incorporated visual feedback, progress indicators, and interactive components. Results revealed statistically significant differences in student performance between the two quiz conditions, with human–computer interface (HCI) students substantially outperforming DevOps students. Question-level analysis further indicated that fill-in-the-blank formats impaired performance more than multiple-choice formats, and a pronounced ceiling effect was observed in the HCI assessment. These findings suggest that gamification effectiveness is contingent on alignment between content complexity, question format, and students’ prior knowledge. Educators are advised to calibrate assessment difficulty and question types carefully when designing gamified learning experiences in technical education.