Gamified Learning Using LoGaLab to Enhance Student Engagement and Conceptual Understanding

Authors

  • Nita Prasyama Azhari Universitas Negeri Yogyakarta Author
  • Restu Ayu Auliyah Universitas Negeri Yogyakarta Author
  • Tri Diaz Elvana Rose Universitas Negeri Yogyakarta Author
  • Laifa Rahmawati Universitas Negeri Yogyakarta Author
  • Ghoffar Amin University of Otago Author

DOI:

https://doi.org/10.15294/usej.v15i2.41285

Keywords:

Conceptual Understanding, Gamification Approach, Mixed-Methods Research, Science Learning, Student Engagement

Abstract

The rapid development of educational technology necessitates innovative approaches to enhance student engagement and conceptual understanding in science learning. However, international assessments indicate that Indonesian students’ performance remains below the global average, particularly in engagement. This study aimed to examine the effectiveness of gamified learning through Locomotor Game Laboratory (LoGaLab) in improving middle school students’ engagement and conceptual understanding of the human locomotor system. A mixed-methods approach with an explanatory sequential design was employed. Quantitative data were collected using learning outcome tests and engagement questionnaires, analyzed through one-sample and paired-sample t-tests. Qualitative data were obtained from classroom observations and semi-structured interviews. The results revealed that student engagement significantly exceeded the theoretical mean (p < 0.001), while conceptual understanding improved significantly between pretest and posttest scores (p < 0.001). Qualitative findings indicated that LoGaLab enhanced motivation, collaboration, persistence, and active participation through interactive activities. These findings suggest that gamified learning using LoGaLab is an effective instructional strategy for improving student engagement and conceptual understanding in science education. This study contributes to science education by providing novel empirical evidence on how gamification, when structurally integrated with a discovery learning model, can simultaneously optimize both affective engagement and cognitive outcomes within a specific scientific domain.

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Published

2026-08-31

Article ID

41285