Authors :
Khushvaktov Umar Norkobilovich
Volume/Issue :
Volume 11 - 2026, Issue 4 - April
Google Scholar :
https://tinyurl.com/vupfurcv
DOI :
https://doi.org/10.38124/ijisrt/26apr1875
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
In the context of digital transformation in education, formative assessment, along with acquiring new qualities
through technological tools, contributes to improving the quality of education. This work is dedicated to studying the
characteristics of formative assessment in the digital educational environment, focusing on the role of algorithmic feedback
systems in developing students' self-management skills. The evolution of feedback from one-time expert reviews to a
continuous, individualized, and data-driven process provided by algorithms is analyzed. It examines how automated, timely,
and diagnostically enriched feedback contributes to the formation of metacognitive skills in students: goal setting, selfcontrol, correction of learning directions, and reflection. Particular attention is paid to the dialectics of interaction between
external, technologically organized assessment processes and internal self-regulation processes. The study is theoreticalmethodological in nature and involves a combination of pedagogical, digital didactics, and cognitive psychology rules. It was
concluded that the effectiveness of digital feedback algorithms is determined not by their technical complexity, but by their
ability to initiate and maintain conscious learning activities, transitioning formative assessment into a subject-subject
mutually regulated process, which is a key factor in the development of academic independence in the digital age.
Keywords :
Formative Algorithmic Assessment, Digital Educational Environment, Algorithmic Feedback, Basic Algorithmic Thinking, Cognitive Development of Students, Self-Regulation of Learning, Metacognitive Skills, Learning Personalization, Adaptive Educational Technologies, Learning Process Analysis, Digital Assessment Tools, Individual Educational Trajectory.
References :
- Anoshina, O. V., & Shumikhina, K. A. (2021). Advantages of using a virtual physics laboratory in pandemic conditions. Modern Problems of Science and Education, (3), 101-110. (in Russian).
- Anoshina, O. V., & Shumikhina, K. A. (2022). Hybrid technologies in teaching physics at universities in pandemic conditions. New Information Technologies in Education and Science, (6), 5-10. DOI: 10.17853/2587-6910-2022-06-5-10 (in Russian).
- Khushvaktov, U. N. (2023). Competence-based approach to developing imperative thinking in students. Bulletin of the National University of Uzbekistan, 1(12), 278-280. (in Uzbek).
- Khushvaktov, U. N. (2025). Analysis of methodological approaches to the development of imperative algorithmic thinking. Pedagogical Skill (Bukhara State University), 1(6), 146-151. (in Uzbek).
- Khushvaktov, U. N. (2025). Cognitive features of the development of imperative algorithmic thinking. Preschool and School Education, 358-362. (in Uzbek).
- Khushvaktov, U. N. (2025). Monitoring imperative algorithmic thinking through an activity-based approach in the educational process. International Scientific-Methodological Journal "Promising Research in Education", (9), 246-250. (in Uzbek).
- Khushvaktov, U. N. (2023). The essence of the concept of imperative algorithmic thinking as a basic component of algorithmic thinking. Journal of Innovations in Social Sciences, 3, 56-60.
- Khushvaktov, U. N. (2023). Diagnostic models for determining the level of imperative algorithmic thinking formation. Analytical Journal of Education and Development, 3, 202-207.
- Anoshina, O. V., & Shumikhina, K. A. (2024). Advantages of using practice-oriented tasks in the educational process on the example of "Physics" discipline. Modern High Technologies, (7), 112-117. DOI: 10.17513/snt.40094 (in Russian).
- Belchik, T. A. (2009). On the problems of organizing students' independent work. Bulletin of Kemerovo State University, (1), 49-54. (in Russian).
- Bulanova-Toporkova, M. V. (2002). Pedagogy and psychology of higher education: textbook. Rostov-on-Don: Phoenix, 544 p. (in Russian).
- Merenkov, A. V., Kunshchikov, S. V., Grechukhina, T. I., Usacheva, A. V., & Vorotkova, I. Y. (2016). Independent work of students: types, forms, assessment criteria (Ed. by T. I. Grechukhina & A. V. Merenkov). Ekaterinburg: Ural Federal University, 80 p. (in Russian).
In the context of digital transformation in education, formative assessment, along with acquiring new qualities
through technological tools, contributes to improving the quality of education. This work is dedicated to studying the
characteristics of formative assessment in the digital educational environment, focusing on the role of algorithmic feedback
systems in developing students' self-management skills. The evolution of feedback from one-time expert reviews to a
continuous, individualized, and data-driven process provided by algorithms is analyzed. It examines how automated, timely,
and diagnostically enriched feedback contributes to the formation of metacognitive skills in students: goal setting, selfcontrol, correction of learning directions, and reflection. Particular attention is paid to the dialectics of interaction between
external, technologically organized assessment processes and internal self-regulation processes. The study is theoreticalmethodological in nature and involves a combination of pedagogical, digital didactics, and cognitive psychology rules. It was
concluded that the effectiveness of digital feedback algorithms is determined not by their technical complexity, but by their
ability to initiate and maintain conscious learning activities, transitioning formative assessment into a subject-subject
mutually regulated process, which is a key factor in the development of academic independence in the digital age.
Keywords :
Formative Algorithmic Assessment, Digital Educational Environment, Algorithmic Feedback, Basic Algorithmic Thinking, Cognitive Development of Students, Self-Regulation of Learning, Metacognitive Skills, Learning Personalization, Adaptive Educational Technologies, Learning Process Analysis, Digital Assessment Tools, Individual Educational Trajectory.