When Learning Processes Fail Novices: Technology Acceptance Through Mediation-Moderation Analysis in High School AI-Coding Education
DOI:
10.29303/jppipa.v12i1.13669Published:
2026-01-25Downloads
Abstract
This study examines an expanded Technology Acceptance Model (TAM) to evaluate the implementation of an AI and coding curriculum in high schools. Specifically, it integrates Perceived Usefulness and Perceived Ease of Use into Student Motivation as a mediator to analyze the relationship between Learning Processes and Learning Outcomes, moderated by Prior Coding Experience. Using an explanatory quantitative design, data were collected from 114 students at HelloMotion High School, a benchmarking school for national curriculum piloting. Structural Equation Modeling (SEM-PLS) revealed three critical findings. First, the learning process positively predicts learning outcomes (β = 0.244, p = 0.005). Second, student motivation functions as a significant partial mediator (indirect effect: β = 0.361, p < 0.001, which is stronger than the direct effect), confirming that the direct impact of pedagogy remains significant alongside psychological factors. Third, while the formal moderation of coding experience was statistically nonsignificant (p = 0.321), exploratory multi-group analysis showed that the learning process significantly impacted students with basic experience (p = 0.026) but failed to significantly impact complete novices (p = 0.160). These findings suggest that a "one-size-fits-all" curriculum is insufficient, highlighting the urgency for differentiated instructional strategies to support novice learners in national policy implementation.
Keywords:
HelloMotion High School Mediation Moderation Motivation SEM-PLS Technology acceptance modelReferences
Akyuz, D. (2023). Exploring contextual factors for pre-service teachers teaching with technology through planning, teaching, and reflecting. International Electronic Journal of Mathematics Education, 18(1), em0721. https://doi.org/10.29333/iejme/12624
Angeli, C., & Giannakos, M. (2020). Computational thinking education: Issues and challenges. Computers in Human Behavior, 105, 106185. https://doi.org/10.1016/j.chb.2019.106185
Asrizal, A., Jasmi, L., Pohan, N. R., & Putra, N. (2023). Effect of Modules in Natural Science Learning on Students’ Knowledge and Critical Thinking Skills: A Meta-Analysis. Jurnal Penelitian Pendidikan IPA, 9(6), 134–140. https://doi.org/10.29303/jppipa.v9i6.2685
Bappenas. (2025). Peta Jalan Pendidikan Indonesia 2025-2045. Kementerian PPN/ Bappenas.
Basyah, N. A., Fahmi, I., Ali, I., Rusli, Z., & Majid, R. A. (2025). The Acceptance of Technology Model and Learning Management Systems: A Systematic Review. Jurnal Penelitian Pendidikan IPA, 11(10), 23–32. https://doi.org/10.29303/jppipa.v11i10.7394
Behnamnia, N., Kamsin, A., Ismail, M. A. B., & Hayati, S. A. (2025). Relationship between creative thinking and outcomes in a digital STEM-based learning environment: A mixed methods case study. Thinking Skills and Creativity, 57, 101816. https://doi.org/10.1016/j.tsc.2025.101816
BSKAP. (2025). Naskah Akadmik Pembelajaran Koding dan Kecerdasan Artifisial pada Pendidikan Dasar dan Menengah. Kemendikdasmen RI.
Cahyani, C. P. N., & Setiawan, E. P. (2024). How Motivation mediated Science Achievement of Indonesian Students: a Path Analysis on PISA 2018 data. Jurnal Penelitian Pendidikan IPA, 10(12), 10493–10501. https://doi.org/10.29303/jppipa.v10i12.10012
Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138(May 2022), 107468. https://doi.org/10.1016/j.chb.2022.107468
Creswell, J. W. (2008). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research. Sage Publications.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Dhany, K. R., Yulianti, D., & Abdurahman. (2025). The Prospects and Challenges of STEM-PjBL with Design Thinking Strategies in Enhancing Students’ Creativity and Entrepreneurial Thinking in the Context of Plastic Waste Recycling: Teachers’ Perspectives. Jurnal Penelitian Pendidikan IPA, 11(1), 28–37. https://doi.org/10.29303/jppipa.v11i1.9357
Dhomke, T. (2023). The economic impact of the AI-powered developer lifecycle and lessons from GitHub Copilot - The GitH. Github.Blog.
Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, 101859. https://doi.org/10.1016/j.cedpsych.2020.101859
Falloon, G. (2020). From digital literacy to digital competence: the teacher digital competency (TDC) framework. Educational Technology Research and Development, 68(5), 2449–2472. https://doi.org/10.1007/s11423-020-09767-4
Falloon, G. (2024). An exploration of online technoliteracy capability teaching and learning in early years classrooms. Education and Information Technologies, 29(1), 625–654. https://doi.org/10.1007/s10639-023-12239-w
Fitrah, M., Sofroniou, A., Setiawan, C., Widihastuti, W., Yarmanetti, N., Jaya, M. P. S., Panuntun, J. G., Arfaton, A., Beteno, S., & Susianti, I. (2025). The Impact of Integrated Project-Based Learning and Flipped Classroom on Students’ Computational Thinking Skills: Embedded Mixed Methods. Education Sciences, 15(4), 448. https://doi.org/10.3390/educsci15040448
Folabit, N. L., C. Jita, L., & Jita, T. (2025). Impact of technology integration on students’ sense of belonging and well-being: a systematic review. International Journal of Evaluation and Research in Education (IJERE), 14(2), 1075. https://doi.org/10.11591/ijere.v14i2.30938
Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
Hayyi, M. (2025). Analysis of Educational Learning Approaches in Improving Digital Literacy in Elementary School Students. Jurnal Penelitian Pendidikan IPA, 11(6), 883–892. https://doi.org/10.29303/jppipa.v11i6.11480
Hsu, T.-C., Chang, S.-C., & Hung, Y.-T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296–310. https://doi.org/10.1016/j.compedu.2018.07.004
Hwang, G.-J., & Chien, S.-Y. (2022). Definition, roles, and potential research issues of the metaverse in education: An artificial intelligence perspective. Computers and Education: Artificial Intelligence, 3, 100082. https://doi.org/10.1016/j.caeai.2022.100082
Kocaman, B. (2023). The Effect of Coding Education on Analytical Thinking of Gifted Students. International Journal of Educational Methodology, 9(1), 95–106. https://doi.org/10.12973/ijem.9.1.95
Lestari, Y. E., Pudin, Y. A., & Wibowo, V. M. (2024). The Impact of Digital Learning Policies on Educational Equity in Rural Indonesian Schools. International Journal of Educational Evaluation and Policy Analysis, 1(2), 13–19. https://doi.org/10.62951/ijeepa.v1i2.74
Lindberg, R. S. N., Laine, T. H., & Haaranen, L. (2019). Gamifying programming education in K‐12: A review of programming curricula in seven countries and programming games. British Journal of Educational Technology, 50(4), 1979–1995. https://doi.org/10.1111/bjet.12685
Mahfud, T., Siswanto, I., Wijayanto, D. S., & Puspitasari, P. F. (2020). Antecedent Factors Of Vocational High School Students’ Readiness For Selecting Careers: A Case In Indonesia. Jurnal Cakrawala Pendidikan, 39(3), 633–644. https://doi.org/10.21831/cp.v39i3.32310
Mills, K. A., Cope, J., Scholes, L., & Rowe, L. (2025). Coding and Computational Thinking Across the Curriculum: A Review of Educational Outcomes. Review of Educational Research, 95(3), 581–618. https://doi.org/10.3102/00346543241241327
Muhammad, F., & Rahmania. (2025). Peningkatan Kemampuan Berpikir Logis dan Problem Solving Siswa SMAN 1 Soppeng melalui Pembelajaran Koding Terstruktur. Abdimas Langkane, 5(2). https://doi.org/10.53769/jpm.v5i2.578
Nabihah, F. K., Arifuddin, A., & Andini, D. W. (2025). The Effect of Project Based Learning (PJBL) Model on Elementary School Students’ Computational Thinking Skills. Dawuh Guru, Jurnal Pendidikan MI/ SD, 5(2), 2962–746. https://doi.org/10.35878/guru.v5i2.1700
Nuryanna, N., Halim, A., Mahzum, E., & Hamid, A. (2021). Acceptance of Technology by Islamic Boarding School Students Based on the TAM Model. Jurnal Penelitian Pendidikan IPA, 7(SpecialIssue), 194–198. https://doi.org/10.29303/jppipa.v7iSpecialIssue.825
Papadakis, S., Kalogiannakis, M., & Zaranis, N. (2016). Developing fundamental programming concepts and computational thinking with ScratchJr in preschool education: a case study. International Journal of Mobile Learning and Organisation, 10(3), 187. https://doi.org/10.1504/IJMLO.2016.077867
Putri, M. E., Ahda, Y., Anhar, A., & Putri, D. H. (2023). Effect of Problem-Based Learning Models and Prior Knowledge toward Critical Thinking Skills on Human Digestive and Circulatory System. Jurnal Penelitian Pendidikan IPA, 9(5), 3905–3914. https://doi.org/10.29303/jppipa.v9i5.3697
Richardo, R., Dwiningrum, S. I. A., Wijaya, A., Retnawati, H., Wahyudi, A., Sholihah, D. A., & Hidayah, K. N. (2023). The impact of STEM attitudes and computational thinking on 21st-century via structural equation modelling. International Journal of Evaluation and Research in Education (IJERE), 12(2), 571. https://doi.org/10.11591/ijere.v12i2.24232
Safitri, W., Suyanto, S., & Prasetya, W. A. (2024). The Influence of the STEM-Based Engineering Design Process Model on High School Students’ Creative and Critical Thinking Abilities. Jurnal Penelitian Pendidikan IPA, 10(2), 662–673. https://doi.org/10.29303/jppipa.v10i2.4765
Soliman, M. (2024). Predicting Continuous Intention to Use e-Learning Platforms among University Students: An Integrated Model. International Journal of Information and Education Technology, 14(12), 1724–1733. https://doi.org/10.18178/ijiet.2024.14.12.2203
Su, J., Zhong, Y., & Ng, D. T. K. (2022). A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers and Education: Artificial Intelligence, 3, 100065. https://doi.org/10.1016/j.caeai.2022.100065
Sutiyono, A., Bakhri, S., Ulyanti, Ningsih, D. R., & Syukur, F. (2024). Assessing E-learning Systems Using the DeLone & McLean and Technology Acceptance Models. Jurnal Penelitian Pendidikan IPA, 10(12), 10001–10012. https://doi.org/10.29303/jppipa.v10i12.8790
Sweller, J., Ayres, P., & Kalyuga, S. (2011). The Expertise Reversal Effect. In Cognitive Load Theory (pp. 155–170). Springer New York. https://doi.org/10.1007/978-1-4419-8126-4_12
Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive Architecture and Instructional Design: 20 Years Later. Educational Psychology Review, 31(2), 261–292. https://doi.org/10.1007/s10648-019-09465-5
Syaflita, D., Efendi, R., Muslim, & Azhar. (2024). Implementation of Design Thinking to Support Creativity-Oriented Learning: A Literature Review. Jurnal Penelitian Pendidikan IPA, 10(4), 188–197. https://doi.org/10.29303/jppipa.v10i4.6788
Teo, T. (2011). Modeling the determinants of pre‐service teachers’ perceived usefulness of e‐learning. Campus-Wide Information Systems, 28(2), 124–140. https://doi.org/10.1108/10650741111117824
Timotheou, S., Miliou, O., Dimitriadis, Y., Sobrino, S. V., Giannoutsou, N., Cachia, R., Monés, A. M., & Ioannou, A. (2023). Impacts of digital technologies on education and factors influencing schools’ digital capacity and transformation: A literature review. Education and Information Technologies, 28(6), 6695–6726. https://doi.org/10.1007/s10639-022-11431-8
UNESCO. (2024). AI competency framework for students. UNESCO. https://doi.org/10.54675/JKJB9835
Wijaya, E. (2023). Improving Elementary School Science Innovation through Visionary Leadership and Information Computer Technology Awareness. Jurnal Penelitian Pendidikan IPA, 9(3), 1558–1562. https://doi.org/10.29303/jppipa.v9i3.3289
Yusuf, Badrun, Suhirman, & Fatimah, G. E. R. (2024). Analysis of the Need for Development of Science Learning Tools Based on Religious Moderation in Junior High Schools in Conflict Prone Areas in West Nusa Tenggara. Jurnal Penelitian Pendidikan IPA, 10(5), 2220–2229. https://doi.org/10.29303/jppipa.v10i5.6893
Zhang, W., & Liu, X. (2025). Artificial Intelligence-Generated Content Empowers College Students’ Critical Thinking Skills: What, How, and Why. Education Sciences, 15(8), 977. https://doi.org/10.3390/educsci15080977
License
Copyright (c) 2026 Bagus Sulasmono, Yuliana Friska, Hendro Prasetyono
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with Jurnal Penelitian Pendidikan IPA, agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in Jurnal Penelitian Pendidikan IPA.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
