Home Releases № 1 (67)

ASSESSING THE EFFECTIVENESS OF AN ADAPTIVE LEARNING SYSTEM IN PREPARING HIGH SCHOOL STUDENTS FOR STATE EXAMINATIONS IN MATHEMATICS

Innovative Pedagogical Technologies in Education , UDC: 373 DOI: 10.25688/2072-9014.2024.67.1.12

Authors

  • Yarmakhov Boris B. Candidate of Philosophical Science, Associate Professor
  • Zaitsev Alexey Ivanovich Doctor of Philosophy

Annotation

The article presents the results of a study focussing the effectiveness of adaptive learning systems in preparing high school students for State Examinations in mathematics. The sample included 4012 students from 19 regions of the Russian Federation who took part in the project “Preparing for the State Examination with Artificial Intelligence” in 2023. Cohen’s Kappa coefficient was used as the main tool for assessing effectiveness for the control and experimental samples, calculated on the digital footprint data collected on the adaptive learning platform. The analysis showed that the systematic use of the adaptive learning platform in preparing students for taking the State Examination in Mathematics gives a stable increase in scores, calculated by the difference between the results of the trial and final State Examination. The effect size of using the adaptive learning system for students when preparing to take the Unified State Examination was 0.21 points, for the basic Unified State Examination — 0.25, and for the specialized Unified State Examination — 0.15 points. Thus, the assumption that the effectiveness of the adaptive learning system can be assessed based on the Cohen’s Kappa coefficient based on the material of final state exams was confirmed.

How to link insert

Yarmakhov, B. B. & Zaitsev, A. I. (2024). ASSESSING THE EFFECTIVENESS OF AN ADAPTIVE LEARNING SYSTEM IN PREPARING HIGH SCHOOL STUDENTS FOR STATE EXAMINATIONS IN MATHEMATICS Bulletin of the Moscow City Pedagogical University. Series "Pedagogy and Psychology", № 1 (67), 124. https://doi.org/10.25688/2072-9014.2024.67.1.12
References
1. 1. Paladines J., Ramirez J. A systematic literature review of intelligent tutoring systems with dialogue in natural language // IEEE Access. 2020 Vol. 8. P. 246–267.
2. 2. Taylor D. L. Personalized and adaptive learning. / D. L. Taylor, M. Yeung, A. Z. Bashet // Innovative Learning Environments in STEM Higher Education: Opportunities, Challenges, and Looking Forward. 2021. P. 17–34.
3. 3. Katsaris I. Adaptive e-learning systems through learning styles. A review of the literature / I. Katsaris, N. Vidakis // Advances in Mobile Learning Educational Research. 2021. Vol. 1 (2). P. 124–145.
4. 4. Shute V. J. Adaptive e-learning / V. J. Shute, B. Towle // Educational Psychology. American Psychological Association. 2018. Р. 105–114.
5. 5. Galperin P. Ya. On the theory of programmed learning / P. Ya. Galperin. M.: Znanie, 1967. 44 p.
6. 6. Weinstein Yu. V. Pedagogical design of personalized adaptive subject-based education for university students in the context of digitalization: diss. ... Doctor of Pedagogical Sciences / Yu. V. Weinstein. Krasnoyarsk, 2021. 425 p.
7. 7. Barria-Pineda J. Augmenting digital textbooks with reusable smart learning content: Solutions and challenges / J. Barria-Pineda, A. B.Narayanan, P. Brusilovsky // Proceedings of the Fourth International Workshop on Intelligent Textbooks. 2022. Vol. 3192. P. 77–91.
8. 8. Brusilovsky P. AI in education, learner control and human-AI collaboration / P. Brusilovsky // International Journal of Artificial Intelligence in Education. 2023. P. 1–4.
9. 9. Implementation of adaptive learning at higher education institutions by means of Moodle LMS / N. Morze [et al.] // Journal of physics: Conference series. 2021. Vol. 1840. № 1. P. 12–62.
10. 10. Howard L. Adaptive blended learning environments / L. Howard, Z. Remenyi, G. Pap // International Conference on Engineering Education. 2006. P. 23–28.
11. 11. Yarmakhov B. B. Parameters of teachers’ readiness to use adaptive digital textbooks in teaching / B. B. Yarmakhov // Pedagogical Innovation and Continuing Education in the XXI Century: a Collection of Scientific Papers of the I All-Russian Scientific and Practical Conference with International Participation. Kirov: Vyatka GATU, 2023. P. 224–227.
12. 12. Mitrovic A. Intelligent tutors for all: constraint-based modeling methodology, systems and authoring / A. Mitrovic, B. Martin, P. Suraweera // IEEE Intelligent Systems. 2007. Vol. 22 (4). P. 38–45.
13. 13. Shmukler A. I. Parameters for evaluating the effectiveness of the use of adaptive systems in education / A. I. Shmukler, B. B. Yarmakhov // Pedagogical Innovation and Continuing Education in the XXI Century: a Collection of Scientific Papers of the I All-Russian Scientific and Practical Conference with International Participation. Kirov: Vyatka GATU, 2023. P. 214–217.
14. 14. VanLehn K. The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems / K. VanLehn // Educational Psychologist. 2011. Vol. 46 (4). P. 197–221.
15. 15. Hattie J. Visible Learning for Teachers: Maximizing Impact on Learning / J. Hattie. London: Routledge, 2012. 267 p.
16. 16. Hubalovsky S. Assessment of the influence of adaptive e-learning on learning effectiveness of primary school pupils / S. Hubalovsky, M. Hubalovska, M. Musilek // Computers in Human Behavior. 2019. P. 691–705.
17. 17. Ghergulescu I. Learning effectiveness of adaptive learning in real world context / I. Ghergulescu, C. Flynn, C. O. Sullivan // EdMedia: World Conference on Educational Media and Technology. 2016. P. 1385–1390.
18. 18. Kulik J. A. Effectiveness of intelligent tutoring systems: a meta-analytic review / J. A. Kulik, J. D. Fletcher // Review of Educational Research. 2016. Vol. 86 (1). P. 42–78.
19. 19. VanLehn K. The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems / K. VanLehn // Educational Psychologist. 2011. Vol. 46 (4). P. 197–221.
Download file .pdf 473.33 kb