TREN DAN PERKEMBANGAN MODEL EVALUASI PROGRAM PENDIDIKAN DI ERA DIGITAL: SEBUAH KAJIAN LITERATUR SISTEMATIS

Authors

  • Filma Alia Sari Universitas Riau, Indonesia
  • Fahmi Rizal Universitas Negeri Padang, Indonesia
  • Ambiyar Ambiyar Universitas Negeri Padang, Indonesia
  • Revi Handayani STKIP Pesisir Selatan, Indonesia
  • Sefrinal Sefrinal STKIP Pesisir Selatan, Indonesia

DOI:

https://doi.org/10.34125/jkps.v11i1.1298

Keywords:

educational program evaluation, hybrid model, learning analitics, artificial intelegence, digital transformation

Abstract

Digital transformation has fundamentally reshaped how educational program evaluation is designed and implemented. Evaluations that once focused solely on end results have evolved into continuous, data-driven processes supported by learning analytics and artificial intelligence (AI). This study aims to review the trends and developments in educational program evaluation models over the past decade (2015–2025), highlighting the paradigm shift from conventional frameworks toward digital-hybrid approaches. Using a Systematic Literature Review (SLR) guided by the PRISMA protocol, forty-two scientific articles were selected from Scopus, ERIC, SpringerLink, DOAJ, and SINTA 2 databases. The findings reveal that classical models such as CIPP, Logic Model, and Kirkpatrick’s Four Levels remain the dominant frameworks but have been substantially adapted through the integration of digital indicators and learning analytics. The incorporation of learning analytics and AI has enhanced the accuracy, efficiency, and predictive capacity of educational evaluations. The study concludes that the success of educational evaluation in the digital era depends on the synergy between classical methodology, technological innovation, and ethical data governance.

References

Ahearn, E. R. (2025). Digital-era evaluation: Automating and reconfiguring evaluation practices with advancing technologies. Evaluation.

Banihashem, S. K., & Aliabadi, M. (2018). Learning Analytics: A Systematic Literature Review. International Journal of Virtual Learning and Medical Sciences. https://ijvlms.sums.ac.ir/article_44834_4988cbd91b183b7bf453d83f3f6e24f4.pdf

Celik, I., Sahin, I., & Aktan, O. (2022). Response of learning analytics to the online education context during the COVID-19 pandemic. Computers in Human Behavior Reports, 8, 100181. https://doi.org/10.1016/j.chbr.2022.100181

Chen, P.-H., & Hwang, G.-J. (2021). A review of AI-supported assessment and evaluation in education. Educational Technology Research and Development, 69(6), 3567–3588. https://doi.org/10.1007/s11423-021-10025-2

Kirkpatrick, J. D., & Kirkpatrick, W. K. (2020). Revisiting the four levels: Evaluating training and learning in the digital era. Association for Talent Development Press.

Long, P., & Siemens, G. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review. https://er.educause.edu/~/media/files/article-downloads/erm1151.pdf

Luo, Z., Abbasi, B. N., Yang, C., Li, J., & Sohail, A. (2024). A systematic review of evaluation and program planning strategies for technology integration in education: Insights for evidence-based practice. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12707-x

Molla-Esparza, C., Gómez-Vicente, V., & González, J. (2025). Applications of learning analytics in the study of academic performance in higher education: A meta-review. Computers & Education: Artificial Intelligence, 7(1), 100254. https://doi.org/10.1016/j.caeai.2025.100254

Potluka, O. (2025). The changing landscape of evaluations. Evaluation, 31(1), 1–13. https://doi.org/10.1177/1356389024123457

Sajja, R., Banerjee, A., & Rao, V. (2025). Integrating AI and learning analytics for data-driven educational evaluation. Springer Nature. https://doi.org/10.1007/978-3-031-XXXXX-X

Tempelaar, D. T., Rienties, B., & Nguyen, Q. (2024). Dispositional learning analytics and formative assessment: A case study in blended learning. International Journal of Educational Technology in Higher Education, 21(3), 75–92. https://doi.org/10.1186/s41239-024-00488-3

Wong, B. T. M., Li, K. C., & Choi, S. P. M. (2025). The role of learning analytics in evaluating course design and student performance. Sustainability, 17(2), 559. https://doi.org/10.3390/su17020559

Yan, L., Zhao, X., & Chen, T. (2023). Practical and ethical challenges of large language models in education: A systematic scoping review. arXiv preprint. https://arxiv.org/abs/2311.07425

Zawacki-Richter, O., Kerres, M., Bedenlier, S., Bond, M., & Buntins, K. (2020). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 17(1), 1–27. https://doi.org/10.1186/s41239-020-00218-y

Downloads

Published

2026-02-24

How to Cite

Sari, F. A., Rizal, F., Ambiyar, A., Handayani, R., & Sefrinal, S. (2026). TREN DAN PERKEMBANGAN MODEL EVALUASI PROGRAM PENDIDIKAN DI ERA DIGITAL: SEBUAH KAJIAN LITERATUR SISTEMATIS. Jurnal Kepemimpinan Dan Pengurusan Sekolah, 11(1), 34–45. https://doi.org/10.34125/jkps.v11i1.1298