R. Zhilmagambetova, A. Mubarakov, A. Alimagambetova


The article considers the tasks and features of mathematics training for students of secondary vocational education. Special attention is paid to the need to solve the problem of adaptation of students to the conditions of study in college and the organization of independent work. In this regard, the authors propose to make wider use of the practice of adaptive learning as innovative pedagogical tools. The article considers the concept of the effectiveness of adaptive personalized learning and suggests the directions by which it can be evaluated. As an example, the experience of implementing an adaptive educational course “Mathematics”, designed in the Articulate Storyline platform, is analyzed. The module is designed to organize and support adaptive learning of students of the Department of Information Systems by means of adaptive educational technologies. The results of the training are analyzed, and the possibilities of the Articulate Storyline platform in ensuring the independent work of students are presented. The main part of the article is devoted to evaluating the effectiveness of e-learning using an adaptive educational platform. With the help of questionnaires and tools of the Articulate Storyline platform, an assessment of the educational result achieved was made, the degree of motivation of students to master the discipline of mathematics was analyzed, and the attitude of students to the process of e-learning using an adaptive educational platform was investigated.

Ключевые слова

mathematics, engineering specialties, student, results of training, adaptive learning platforms, educational process.

Полный текст:



Abdelhai, R., Yassin, S., Ahmad, M. F., & Fors, U. G. (2012). An e-learning reproductive health module to support improved student learning and interaction: a prospective interventional study at a medical school in Egypt. BMC medical education, 12(1), 1-9.

Noroozi, O., Alikhani, I., Järvelä, S., Kirschner, P. A., Juuso, I., & Seppänen, T. (2019). Multimodal data to design visual learning analytics for understanding regulation of learning. Computers in Human Behavior, 100, 298-304.

Fontaine, G., Cossette, S., Maheu-Cadotte, M. A., Mailhot, T., Deschênes, M. F., Mathieu-Dupuis, G., ... & Dubé, V. (2019). Efficacy of adaptive e-learning for health professionals and students: a systematic review and meta-analysis. BMJ open, 9(8), e025252.

Mavroudi, A., Giannakos, M., & Krogstie, J. (2018). Supporting adaptive learning pathways through the use of learning analytics: developments, challenges and future opportunities. Interactive Learning Environments, 26(2), 206-220.

Dahlmann, J. C. (2021). Guidelines for Effective Adaptive Learning: A Meta Meta-Analysis.

Koedinger, K. R., & Anderson, J. R. (1998). Illustrating principled design: The early evolution of a cognitive tutor for algebra symbolization. Interactive Learning Environments, 5(1), 161-179.

Razzaq, L., Feng, M., Nuzzo-Jones, G., Heffernan, N. T., Koedinger, K. R., Junker, B., ... & Rasmussen, K. P. (2005, July). The Assistment project: Blending assessment and assisting. In Proceedings of the 12th annual conference on artificial intelligence in education, (pp.555-562).

Anderson, J. R., & Reiser, B. J. (1985). The LISP tutor. Byte, 10(4), 159-175.

Figueiredo, J., & García-Peñalvo, F. J. (2020, October). Intelligent Tutoring Systems approach to Introductory Programming Courses. In Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality, 34-39.

Mitrovic, A. (2003). An intelligent SQL tutor on the web. International Journal of Artificial Intelligence in Education, 13(2-4), 173-197.

Sykes, E. R., & Franek, F. (2003, June). A Prototype for an Intelligent Tutoring System for Students Learning to Program in JavaTM. In Proceedings of the IASTED International Conference on Computers and Advanced Technology in Education, (pp. 78-83).

Weragama, D., & Reye, J. (2013, July). The PHP intelligent tutoring system. In International Conference on Artificial Intelligence in Education (pp. 583-586). Springer, Berlin, Heidelberg.

Evens, M., Chang, R. C., Lee, Y. H., Shim, L. S., Woo, C. W., & Zbang, Y. (1997, March). CIRCSIM-Tutor: An intelligent tutoring system using natural language dialogue. In Fifth Conference on Applied Natural Language Processing: Descriptions of System Demonstrations and Videos (pp. 13-14).

Ferreira, A., & Atkinson, J. (2008, December). Designing a feedback component of an intelligent tutoring system for foreign language. In International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 277-290). Springer, London.

Slavuj, V., Kovačić, B., & Jugo, I. (2015, May). Intelligent tutoring systems for language learning. In 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (pp. 814-819). IEEE.

Minn, S. (2022). AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence, 100050.

Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M. (2020). When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments, 1–11.

Alamri, H. A., Watson, S., & Watson, W. (2021). Learning technology models that support personalization within blended learning environments in higher education. TechTrends, 65(1), 62–78.



  • Ссылки не определены.

(P): 2707-9031
(E): 2707-904X

Бизнес-центр EXPO, блок C.1.
Казахстан, 010000