Improving JavaScript Learning Engagement with Gamification Techniques
DOI:
https://doi.org/10.5614/itbj.ict.res.appl.2026.20.1.3Keywords:
gamification, Javascript learning, ReactJS library, technology acceptance model, web applicationAbstract
JavaScript is highly demanded in modern development, but traditional learning methods often fail to engage students. To address this, this study designed and developed a responsive, gamified web-based JavaScript learning application using the ReactJS library. The application integrates gamification elements such as dashboards, learning modules, challenges, points, levels, ranks, and achievements to enhance learning engagement. The application?s usability and acceptance were evaluated using the Technology Acceptance Model (TAM) through a survey involving 35 participants. The evaluation yielded a Perceived Usefulness score of 90.00% and a Perceived Ease of Use score of 89.71%, resulting in a high overall average score of 89.85%. These results indicate that respondents strongly agree that the application is highly useful for escalating learning motivation and is easy to use. While the TAM results confirm high user acceptance and demonstrate that gamification effectively eases the JavaScript learning process, our future research will incorporate objective cognitive assessments to definitively measure its absolute pedagogical effectiveness.
Downloads
References
Gayatri, G., Jaya, I G.N.M. & Rumata, V.M., The Indonesian Digital Workforce Gaps in 2021-2025, Sustain., 15(1), 754, 2023. doi:10.3390/su15010754
Rinartha, K. & Kartika, L.G.S., Distribution of Skill Requirements for Information Technology Graduates in Indonesia, Sam? Jiva Jn?nam (International J Soc Stud.), 1(2), pp. 38-58, 2024. doi:10.25078/ijoss.v1i2.3311
de Ruiter, L.E. & Bers, M.U., The Coding Stages Assessment: Development and Validation of an Instrument for Assessing Young Children?s Proficiency in the Scratchjr Programming Language, Comput Sci Educ., 32(4), pp. 388-417, 2022. doi:10.1080/08993408.2021.1956216
Jiang Z, Zhong H, Meng N. Investigating And Recommending Co-Changed Entities for JavaScript Programs, J Syst Softw., 180(1), pp.1-40, 2021. doi:10.1016/j.jss.2021.111027
?ur?ev, D., Popularity of Programming Language. Aidasco Rev., 2(2), pp. 24-29, 2024. doi:https://doi.org/10.59783/aire.2024.59
Misu MRH, Satter A. An Exploratory Study of Analyzing JavaScript Online Code Clones. IEEE Int Conf Progr Compr. 2022;2022-March:94?8. doi:10.1145/3524610.3528390
Moutidis, I., Williams, H.T.P., Community Evolution on Stack Overflow, PLoS One, 16(6 June 2021), pp. 1-23, 2021. doi:10.1371/journal.pone.0253010 PubMed PMID: 34138921.
Khan Mohd, T., Thompson, J., Carmine, A. & Reuter, G., Comparative Analysis on Various CSS and JavaScript Frameworks, J Softw., 17(6):282-291, 2022. doi:10.17706/jsw.17.6.282-291
Ferreira, F., Borges, H.S. & Valente, M.T., On the (Un-)Adoption of Javascript Front-End Frameworks, Softw - Pract Exp., 52(4), pp.947-966, 2022. doi:10.1002/spe.3044
Fariz, M., Lazuardy, S. & Anggraini, D., Modern Front End Web Architectures with React.Js and Next.Js., Int Res J Adv Eng Sci., 7(1), pp.132-141, 2022.
Vyas, R., Comparative Analysis on Front-End Frameworks for Web Applications, Int J Res Appl Sci Eng Technol., 10(7), pp. 298-307, 2022. doi:10.22214/ijraset.2022.45260
Goh, H.A., Ho, C.K. & Abas, F.S., Front-End Deep Learning Web Apps Development and Deployment: A Review, Appl Intell., 53(12), pp.15923-15945, 2023. doi:10.1007/s10489-022-04278-6
Li, C., Dewaele, J.M. & Hu, Y. Foreign Language Learning Boredom: Conceptualization and Measurement, Appl Linguist Rev., 14(2), pp.223-249, 2023. doi:10.1515/applirev-2020-0124
Jawad, H.M. & Tout, S., Gamifying Computer Science Education for Z Generation, Inf., 12(11), pp.1-18, 2021. doi:10.3390/info12110453
Kalogiannakis, M., Papadakis, S. & Zourmpakis, A.I., Gamification in Science Education, A Systematic Review of the Literature, Educ Sci. 2021, 11(1), pp.1-36. doi:10.3390/educsci11010022
Istiono, W., Mandarin Language Learning with Gamification Method, Int J Adv Trends Comput Sci Eng., 10(5), pp. 3046-3052, 2021. doi:10.30534/ijatcse/2021/171052021
Oswald, P.A. & Gunawan, D., Avia Saga : A Gamified Mobile-Based Learning Management System, Int J New Media Technol., 11(1), pp. 35-42, 2024. doi: 10.31937/ijnmt.v11i1.3675
Jonathan, L. & Istiono, W., Impact of Learning Computer Science in the Side Scrolling 2D Forms, J Appl Comput Sci Math., 17(1), pp. 9-14, 2023. doi:10.4316/jacsm.202301001
Johan, M.E., Sanjaya, S.A., Brata, A.Y., Brillyant, A., Gamification to Study Machine Learning using Octalysis Framework, Ultim J Tek Inform., 15(1), pp. 35-43, 2023. doi:10.31937/ti.v15i1.3118
Dimitrijevi?, N., Zdravkovi?, N. & Mili?evi?, V., An Automated Grading Framework for the Mobile Development Programming Language Kotlin. Int J Qual Res., 17(2), pp. 313-324, 2023. doi:10.24874/IJQR17.02-01
Arao, T.A., Campos, J., Ferreira, M.C. & Fernandes, C.S., Improving Accessibility with Gamification Strategies: Development of a Prototype App., Int Conf Inf Commun Technol Ageing Well e-Health, ICT4AWE - Proc., (Ict4awe), pp. 237-242, 2024. doi:10.5220/0012702700003699
Haider, A., Javascript frontend web app frameworks: React vs Svelte, DiVA-portal, 10, 2022. Available from: https://www.diva-portal.org/smash/get/diva2:1681585/FULLTEXT01.pdf
Skrzypiec, S. & Plechawska-Wcik, M., Comparative analysis of Angular and React frameworks Analiza pornawcza szkielet programistycznych Angular i React, J Comput Sci Inst., 28(June), pp. 256-263, 2023.
Masrom, M., Technology Acceptance Model and E-learning, 12th Int Conf Educ., May, pp. 21-24, 2007.
Weber, P., Grewald L. & Ludwig, T., Reflection on the Octalysis Framework as a Design and Evaluation Tool, CEUR Workshop Proc., 3147, pp.75-84, 2022.
Jebb, A.T., Ng ,V. & Tay, L., A Review of Key Likert Scale Development Advances: 1995?2019, Front Psychol., 12(May), pp. 1-14, 2021.
Chen, M., Tworek, J., Jun, H., Yuan, Q., Ponde, H., Pinto, D.O., et al. Evaluating Large Language Models Trained on Code. arXiv (Cornell Univ. )2021. doi: 10.48550/arXiv.2107.03374
Tan, C.W., Guo, S., Wong, M.F. & Hang, C.N., Copilot for Xcode: Exploring AI-Assisted Programming by Prompting Cloud-based Large Language Models. arXiv. 2023. doi:10.48550/arXiv.2307.14349
Tan, C.W., From Code Generation to Software Testing: AI Copilot With Context-Based Retrieval-Augmented Generation, IEEE Softw., 42(4), pp.34-42, 2025. doi:10.1109/MS.2025.3549628
Wong, M.F. & Tan, C.W., Aligning Crowd-sourced Human Feedback for Reinforcement Learning on Code Generation by Large Language Models, IEEE Trans Big Data, pp. 1-12, 2024. doi:10.1109/TBDATA.2024.3523581
