Adaptive Gamified Coding Learning Model to Increase Engagement and Computational Thinking Skills in Junior High
DOI:
https://doi.org/10.24036/jlils.v6i1.224Keywords:
adaptive learning, gamification, coding education, junior high school, student engagementAbstract
Computational thinking in the digital era encourages integrating coding education into junior high school curricula. This study examines the effectiveness of an adaptive gamified coding-based learning model in creating a more personalized and engaging learning experience. The model integrates adaptive strategies, gamification elements, and interactive challenges to enhance student motivation and participation. Using a quasi-experimental design, data were collected through engagement questionnaires and computational thinking tests. Results show an 87% improvement in computational thinking skills in the experimental group, compared to 13% in conventional learning. A positive relationship between engagement and computational thinking was also identified, indicating benefits for cognitive development and learning motivation. Additionally, a positive relationship was identified between student engagement and computational thinking ability. These results suggest that integrating adaptive learning and gamification effectively supports both cognitive development and learning motivation. This study contributes to advancing innovative learning models relevant to digital-era education
References
Adipat, S. et al. (2021) ‘Engaging Students in the Learning Process with Game-Based Learning: The Fundamental Concepts’, International Journal of Technology in Education, 4(3), pp. 542–552. Available at: https://doi.org/10.46328/ijte.169.
Ağaoğlu, O. and Demİr, M. (2020) ‘The integration of 21 st century skills into education : an evaluation based on an activity example’, 7(3), pp. 105–114.
Akama, K. and Nantajeewarawat, E. (2022) ‘A Foundation of Logical Problem Solving’, International Journal of Innovative Computing, Information and Control, 18(5), pp. 1559–1570. Available at: https://doi.org/10.24507/ijicic.18.05.1559.
Azhari, F.A. et al. (2020) ‘Students ’ Perceptions about Social Constructivist Learning Environment in e-learning’. Available at: https://doi.org/10.5530/ijper.54.2.31.
Bessadok, A. (2021) ‘Exploring students digital activities and performances through their activities logged in learning management system using educational data mining approach’, pp. 58–72. Available at: https://doi.org/10.1108/ITSE-08-2021-0148.
Cahyono, B.T. et al. (2026) ‘Enhancing Digital Literacy and 4Cs Skills through Project-Based Deep Learning in Kurikulum Merdeka A . Introduction’, Edunesia : Jurnal Ilmiah Pendidikan, 7(2), pp. 1215–1231.
Dewanti, B.A. (2022) ‘Comparing Student Creativity Skills in Experiment-Based and Project-Based Science Learning’, Prisma Sains : Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram, 10(3), p. 786. Available at: https://doi.org/10.33394/j-ps.v10i3.5348.
Elshareif, E. (2021) ‘The Effects of E-Learning on Students ’ Motivation to Learn in Higher Education’, 25(3), pp. 128–143. Available at: https://doi.org/10.24059/olj.v25i3.2336.
Esteve-mon, F.M. et al. (2020) ‘Digital Competence and Computational Thinking of Student Teachers’, 15(2), pp. 29–41.
Hsu, W.K.T. (2020) ‘Learning Computational Thinking Without a Computer : How Computational Participation Happens in a Computational Thinking Board Game’, The Asia-Pacific Education Researcher, 29(1), pp. 67–83. Available at: https://doi.org/10.1007/s40299-019-00479-9.
Khaldi, A., Bouzidi, R. and Nader, F. (2023) ‘Gamification of e ‑ learning in higher education : a systematic literature review’, Smart Learning Environments [Preprint]. Available at: https://doi.org/10.1186/s40561-023-00227-z.
Khaleel, F.L. et al. (2020) ‘The impact of gamification on students learning engagement’, International Journal of Electrical and Computer Engineering (IJECE), 10(5), pp. 4965–4972. Available at: https://doi.org/10.11591/ijece.v10i5.pp4965-4972.
Kocagul, M. (2024) ‘Learning Activity Matters : Tips for Student Engagement’, 11(January), pp. 1–15.
Laureiro-martinez, D., Arrieta, J.P. and Brusoni, S. (2023) ‘Microfoundations of Problem Solving : Attentional Engagement Predicts Problem-Solving Strategies Microfoundations of Problem Solving : Attentional Engagement Predicts Problem-Solving Strategies’, Institute for Operations Research and the Management Sciences (INFORMS) INFORMS is located in Maryland, USA Organization, 34(6), pp. 2206–2230.
Lu, C. et al. (2022) ‘A scoping review of computational thinking assessments in higher education’, Journal of Computing in Higher Education, 34(2), pp. 416–461. Available at: https://doi.org/10.1007/s12528-021-09305-y.
Papadakis, S. (2021) ‘The Impact of Coding Apps to Support Young Children in Computational Thinking and Computational Fluency . A Literature Review’, Frontiers in Education, 6(June), pp. 1–12. Available at: https://doi.org/10.3389/feduc.2021.657895.
Peixoto, A. and Pedro, A. (2022) ‘Exploring Design Concepts to Enable Teachers to Monitor and Adapt Gamification in Adaptive Learning Systems : A Qualitative Research Approach’, pp. 867–891.
Raza, S.A. (2020) ‘Examining the impact of case-based learning on student engagement , learning motivation and learning performance among university students’, 12(3), pp. 517–533. Available at: https://doi.org/10.1108/JARHE-05-2019-0105.
Rifqi, Q.A. (2024) ‘THE ROLE OF EDUCATIONAL TRANSFORMATION IN THE DIGITAL ERA Rifqi Qadar Aditya Universitas Muhammadiyah Surakarta , Surakarta , Indonesia Suranto Universitas Muhammadiyah Surakarta , Surakarta , Indonesia Abstrak’, Al Qalam: Jurnal Ilmiah Keagamaan dan Kemasyarakatan, 18(3), pp. 1756–1772.
Santo, A. De et al. (2022) ‘Promoting Computational Thinking Skills in Non-Computer-Science Students : Gamifying Computational Notebooks to Increase Student Engagement’, 15(3), pp. 392–405. Available at: https://doi.org/10.1109/TLT.2022.3180588.
Sueki, D.G. et al. (2022) ‘The Differing Effects of Nociception and Pain Memory on Pain Thresholds in Participants with and without a History of Injury : A Pretest-Posttest Quasi Experimental Study’, pp. 359–379. Available at: https://doi.org/10.4236/jbbs.2022.128021.
