This article scientifically and methodologically examines the issue of improving the criteria and indicators for assessing students’ programming competence based on an adaptive system. The study analyzes the process of assessing programming competence through cognitive, algorithmic, practical, and reflective components, and develops a clear system of indicators for each component.

The article substantiates the limitations of traditional assessment systems, particularly their primary focus on final outcomes, while insufficiently considering students’ algorithmic thinking, code quality, and ability to analyze errors. On this basis, an adaptive assessment mechanism based on learning analytics and artificial intelligence technologies is proposed.

The research findings demonstrate that the adaptive assessment system makes it possible to identify students’ individual learning trajectories, analyze their strengths and weaknesses, and monitor the dynamics of programming competence development. The improved system of indicators contributes to increasing the objectivity, accuracy, and developmental function of the assessment process. This approach has significant practical importance for improving the teaching and assessment system of programming disciplines in higher education institutions.

programming competence, adaptive assessment, artificial intelligence, learning analytics, assessment criteria, indicator system, cognitive component, algorithmic component, practical component, reflective component, individual learning trajectory, digital education.

Yao, D., & Lin, J. (2025). Cognitive enhancement through competency-based programming education: a 12-year longitudinal study. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13582-w

du Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10, e39630. https://doi.org/10.1016/j.heliyon.2024.e39630

Cipriano, B. P., & Alves, P. (2022). An automatic assessment tool for programming assignments. SoftwareX, 18, 101079. https://doi.org/10.1016/j.softx.2022.101079

Fernández-Morante, C., Cebreiro-López, B., Rodríguez-Malmierca, M. J., & Casal-Otero, L. (2022). Adaptive learning supported by learning analytics for student teachers’ personalized training during in-school practices. Sustainability, 14(1), 124. https://doi.org/10.3390/su14010124

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.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education — where are the educators? International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0

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. https://doi.org/10.1002/widm.1355

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x.