This article examines a learning analytics-based assessment model for evaluating students' programming skills in adaptive learning environments. Programming education requires assessment procedures that capture not only final answers but also learning processes, code quality, debugging behavior, problem-solving strategies and reflective improvement. The study proposes an integrated model that combines learning analytics, automated programming assessment, adaptive feedback and competence-based rubrics. A quasi-experimental design is presented with control and experimental groups, where the experimental group uses an adaptive environment that collects learning traces and transforms them into diagnostic indicators. The model assesses five components: cognitive understanding, algorithmic thinking, practical coding, debugging and reflective learning. Model-based experimental results show that students in the adaptive environment achieved higher growth in programming skills than students taught through conventional assessment. The findings indicate that learning analytics can improve the objectivity, timeliness and developmental function of programming assessment. The article contributes a structural-functional model, an indicator system and methodological recommendations for higher education programming courses.
Learning Analytics-Based Assessment of Students' Programming Skills in Adaptive Environments
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Abouelenein, Y. A. M. (2025). Impact of an adaptive environment based on learning analytics dashboards on students' self-regulated learning and academic performance. Smart Learning Environments. https://doi.org/10.1186/s40561-024-00340-7
Cavalcanti, A. P., Barbosa, A., Carvalho, R., Freitas, F., Tsai, Y.-S., Gašević, D., & Mello, R. F. (2021). Automatic feedback in online learning environments: A systematic literature review. Computers and Education: Artificial Intelligence, 2, 100027.
Cipriano, B. P., Fachada, N., & Alves, P. (2022). Drop Project: An automatic assessment tool for programming assignments. SoftwareX, 18, 101079. https://doi.org/10.1016/j.softx.2022.101079
Gašević, D., Dawson, S., & Siemens, G. (2015). Let's not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.
Horváth, M., Kormaník, T., & Porubän, J. (2024). Adaptation of automated assessment system for large programming courses. OASIcs ICPEC 2024.
Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68, 1961-1990. https://doi.org/10.1007/s11423-020-09788-z
Kayumov Oybek Achilovich, Data-driven yondashuv asosida talabalar kompetentligini baholash metodikasi, Ta’limda istiqbolli izlanishlar - xalqaro ilmiy-metodik jurnal, № 4/2026 ISSN: 3060-527X, 55-63 b.
Kayumov Oybek Achilovich, Kompetentligini baholash mezonlari va indikatorlarini ishlab chiqish, “Maktabgacha va maktab ta’limi” jurnali, ISSN: 3060-4613, 2026-yil, aprel, 4(1)-son, 203-208 b.
Kayumov Oybek Achilovich, Web-loyihalash kompetentligini shakllantirishda interaktiv va loyihaviy (PBL) metodlardan foydalanish, Zamonaviy jamiyat va innovatsiyalar ilmiy-nazariy jurnаl, ISSN: 3060-544x, № 4-sоn, 2026-yil, 63-69 b.
Kayumov Oybek Achilovich, Evaluating the Effectiveness of Adaptive Learning Systems in Developing Web Design Competence: An Empirical Study, International journal of pedagogics, ISSN: 2771-2281, Volume 06 Issue 03 (March) 2026, 362-367 b.
Kayumov Oybek Achilovich, Human–Ai Collaboration in Adaptive Learning Systems: A New Paradigm for Competence Development, American journal of social sciences and humanity research, ISSN: 2771-2141, Volume 06 Issue 03 (March) 2026, 126-131 b.
Kayumov Oybek Achilovich, Data-Driven Approaches to Enhancing Students’ Web Design Skills Using Multimodal Learning Analytics, Current research journal of pedagogics, ISSN: 2767-3278, Vol.07 Issue03 2026, 46-52 b.
Kayumov Oybek Achilovich, AI Capital As A Predictor of Students’ Digital and Web Design Competence in Higher Education, Journal of Social Sciences and Humanities Research Fundamentals, ISSN: - 2748-9345, Volume 06 Issue 03 (March) 2026, 94-99 b.
Kayumov Oybek Achilovich, A Conceptual Framework for Developing Web Design Competence Based on Multimodal Learning Analytics in Adaptive Digital Environments, European International Journal Of Pedagogics, ISSN: 2751-000X, Volume 06 Issue 03 (MARCH) 2026, 155-161 b.
Keuning, H., Jeuring, J., & Heeren, B. (2019). A systematic literature review of automated feedback generation for programming exercises. ACM Transactions on Computing Education, 19(1), Article 3, 1-43. https://doi.org/10.1145/3231711
Lin, K.-Y., Li, M.-H., Lo, F.-Y., Huang, H.-C., Matsuno, K., & Watanabe, R. (2025). Adaptive learning with human factors and artificial intelligence: Associations with training effectiveness in programming education. International Journal of Industrial Ergonomics.
Luxton-Reilly, A., Simon, Albluwi, I., Becker, B. A., Giannakos, M., Kumar, A. N., Ott, L., Paterson, J., Scott, M. J., Sheard, J., & Szabo, C. (2018). Introductory programming: A systematic literature review. ITiCSE 2018 Companion, 55-106. https://doi.org/10.1145/3293881.3295779
Medeiros, R. P., Ramalho, G. L., & Falcão, T. P. (2019). A systematic literature review on teaching and learning introductory programming in higher education. IEEE Transactions on Education, 62(2), 77-90.
Paiva, J. C., Leal, J. P., & Figueira, A. (2025). Incremental repair feedback on automated assessment of programming assignments. Electronics, 14(4), 819.
Qushem, U. B., et al. (2025). Technology-enhanced learning and learning analytics for personalized STEM learning: A scoping review. Computers & Education.
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355.