Russian Psychological Issues
JournalsTopicsAuthorsEditor's Choice For AuthorsAbout PsyJournals.ruContact Us

  Previous issue (2020. Vol. 9, no. 3)

Journal of Modern Foreign Psychology

Publisher: Moscow State University of Psychology and Education

ISSN (online): 2304-4977


License: CC BY-NC 4.0

Started in 2012

Published quarterly

Free of fees
Open Access Journal


Personalization in education: from programmed to adaptive learning 271


Kravchenko D.A.
Master’s in Psychology, , National Research University Higher School of Economics, Moscow, Russia

Bleskina I.A.
Master's in Business Informatics, National Research University Higher School of Economics, Moscow, Russia

Kalyaeva E.N.
Degree in Sociology, National Research University Higher School of Economics, Moscow, Russia

Zemlyakova E.A.
Bachelor's in History, National Research University Higher School of Economics, Moscow, Russia

Abbakumov D.F.
PhD in Educational Sciences, Moscow, Russia

Adaptive learning is a learning service that adapts quickly and continuously to the individual characteristics of students. Our study is a literature review that includes a brief analysis of the history of development, the main modern approaches and methods of implementation, the educational potential of adaptive platforms and the directions of the future development of adaptive learning. The literature review allowed us to describe and analyze the main stages of learning development: from programmable to adaptive. Its results are aimed at helping researchers and developers gain a general and comprehensive understanding of adaptive learning and its development trends.

Keywords: adaptive learning, programmed learning, a review of the literature, adaptive learning platform

Column: Educational psychology


For Reference


The reported study was funded by Russian Foundation for Basic Research (RFBR), project number № 19-113-50415

  1. Landa L.N. Algoritmizatsiya v obuchenii [Algorithmization in teaching]. Moscow: Prosveshchenie, 1966. 524 p. (In Russ.).
  2. Talyzina N.F. Teoreticheskie problemy programmirovannogo obucheniya [Theoretical problems of programmed learning]. Moscow: MGU, 1969. 132 p. (In Russ.).
  3. Kidzinski L. et al. A tutorial on machine learning in educational science. In Y. Li et al. (eds.), State-of-the-Art and Future Directions of Smart Learning (Lecture Notes in Educational Technology). Springer, Singapore, 2015, pp. 453–459.
  4. Abbakumov D., Desmet P., Van den Noortgate W. Measuring growth in students’ proficiency in MOOCs: Two component dynamic extensions for the Rasch model. Behavior Research Methods, 2019. Vol. 51, no. 1, pp. 332–341. DOI:10.3758/s13428-018-1129-1
  5. Nabizadeh A.H. et al. Adaptive learning path recommender approach using auxiliary learning objects. Computers & Education, 2020. Vol. 147, pp. 1–17. DOI:10.1016/j.compedu.2019.103777
  6. Nabih A.H. et al. Adaptive Social Learning Management System to Develop University Students Achievement. Egyptian Computer Science Journal, 2020. Vol. 44, no. 1, pp. 35–47.
  7. Aghababyan A., Lewkow N., Baker R.S. Enhancing the Clustering of Student Performance Using the Variation in Confidence. Proceedings of International Conference on Intelligent Tutoring Systems. Cham: Springer, 2018, pp. 274–279.
  8. Bingham A.J. et al. Ahead of the curve: Implementation challenges in personalized learning school models. Educational Policy, 2018. Vol. 32, no. 3, pp. 454–489. DOI:10.1177/0895904816637688
  9. Bloom B.S. The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational researcher, 1984. Vol. 13, no. 6, pp. 4–16. DOI:10.2307/1175554
  10. Boyce S., O’Halloran J. Active Learning in Computer-based College Algebra. Primus, 2020. Vol. 30, no. 4, pp. 458–474. DOI:10.1080/10511970.2019.1608487
  11. Brusilovsky P., Pesin L. Adaptive Educational Hypermedia [Elektronnyi resurs]. Proceedings of Tenth International PEG conference: Tampere, Finland, 23-26 June 2001). Tampere, 2001, pp. 8–12. URL: (Accessed 20.07.2020).
  12. Linden K. et al. Can we calm first-year student’s «neuroscience anxiety» with adaptive learning resources? A pilot study [Elektronnyi resurs]. Proceedings of ASCILITE 2018: Open Oceans: Learning without borders. Geelong, 2018, pp. 451–455. URL: (Accessed 20.07.2020).
  13. Carbonell J.R. AI in CAI: An Artificial-intelligence Approach to Computer Assisted Instruction. IEEE Transactions on Man-Machine Systems, 1970. Vol. 11, no. 4, pp. 190–202. DOI:10.1109/TMMS.1970.299942
  14. Chen C.M., Liu C.Y., Chang M.H. Personalized curriculum sequencing utilizing modified item response theory for web-based instruction. Expert Systems with Applications, 2006. Vol. 30, no. 2, pp. 378–396. DOI:10.1016/j.eswa.2005.07.029
  15. Chiu T.K.F., Mok I.A.C. Learner expertise and mathematics different order thinking skills in multimedia learning. Computers & Education, 2017. Vol. 107, pp. 147–164. DOI:10.1016/j.compedu.2017.01.008
  16. Chrysafiadi K., Troussas C., Virvou M. A Framework for Creating Automated Online Adaptive Tests Using Multiple-Criteria Decision Analysis. Proceedings of 2018 IEEE International Conference on Systems, Man, and Cybernetics: Miyazaki, Japan, 7–10 October 2018). Miyazaki: IEEE, 2018, pp. 226–231. DOI:10.1109/SMC.2018.00049
  17. Clark R.M., Kaw A., Delgado E.E. Board 69: Do Adaptive Lessons for Pre-class Experience Improve Flipped Learning? [Elektronnyi resurs]. 2018 ASEE Annual Conference & Exposition (Salt Lake City, Utah, June 2018). Salt Lake City: American Society for Engineering Education, 2018. 13 p. URL: (Accessed 20.07.2020).
  18. Crowder N.A. Automatic tutoring by means of intrinsic programming. In E. Galanter (ed.), Automatic teaching: The state of the art. New York: Wiley, 1959, pp. 109–116.
  19. Al-Mahmood R. et al. Digital identity and e-reputation: Showcasing an adaptive eLearning module to develop students’ digital literacies. Proceedings of 35th International Conference of Innovation, Practice and Research in the use of Educational Technologies in Tertiary Education: Deakin University, Geelong, Australia, 25-28 November 2018. Geelong: Deakin University, 2018, pp. 25–34.
  20. Farmer E.C., Catalano A.J., Halpern A.J. Exploring Student Preference between Textbook Chapters and Adaptive Learning Lessons in an Introductory Environmental Geology Course. TechTrends, 2020. Vol. 64, pp. 150–157. DOI:10.1007/s11528-019-00435-w
  21. Fautch J.M. Adaptive Learning Technology in General Chemistry: Does It Support Student Success? In S.K. Hartwell, T. Gupta (eds.), Enhancing Retention in Introductory Chemistry Courses: Teaching Practices and Assessments. American Chemical Society, 2019, pp. 91–104. DOI:10.1021/bk-2019-1330.ch006
  22. Galperin P.I. Stage-by-stage formation as a method of psychological investigation. Journal of Russian and East European Psychology, 1992. Vol. 30, no. 4, pp. 60–80. DOI:10.2753/RPO1061-0405300460
  23. Lura D.J. et al. Homework Methods in Engineering Mechanics: Part 3 [Elektronnyi resurs]. ASEE Annual Conference & Exposition, 2017. 7 p. URL: (Accessed 20.07.2020).
  24. Hssina B., Erritali M. A Personalized Pedagogical Objectives Based on a Genetic Algorithm in an Adaptive Learning System. Procedia Computer Science, 2019. Vol. 151, pp. 1152–1157. DOI:10.1016/j.procs.2019.04.164
  25. Normadhi N.B.A. et al. Identification of personal traits in adaptive learning environment: Systematic literature review. Computers & Education, 2019. Vol. 130, pp. 168–190. DOI:10.1016/j.compedu.2018.11.005
  26. Kerr P. Adaptive learning. ETL Journal, 2016. Vol. 70, no. 1, pp. 88–93. DOI:10.1093/elt/ccv055
  27. Lowendahl J.M., Thayer T.L.B., Morgan G. Top 10 strategic technologies impacting higher education in 2016 [Elektronnyi resurs]. Gartner Research, 2016. URL: (Accessed 20.07.2020).
  28. Martin F., Markant D. Adaptive learning modules. In M.E. David, M.J. Amey (eds.), The SAGE encyclopedia of higher education. London: Sage, 2020, pp. 2–4.
  29. Matayoshi J., Cosyn E. Identifying Student Learning Patterns with Semi-Supervised Machine Learning Models. In J.C. Yang et al. (eds.), Proceedings of the 26th International Conference on Computers in Education. 2018, pp. 11–20.
  30. Forsyth B. et al. Maximizing the Adaptive Learning Technology Experience [Elektronnyi resurs]. Journal of Higher Education Theory and Practice, 2016. Vol. 16, no. 4, pp. 80–88. URL: (Accessed 20.07.2020).
  31. Natriello G. The Adaptive Learning Landscape [Elektronnyi resurs]. Teachers College Record, 2017. Vol. 119, no. 3, 46 p. URL: (Accessed 20.07.2020).
  32. Fang Y. et al. Online Learning Persistence and Academic Achievement [Elektronnyi resurs]. Proceedings of the 10th International Conference on Educational Data Mining: Wuhan, China, 25-28 June 2017. Wuhan: ERIC, 2017, pp. 312–317. URL: (Accessed 20.07.2020).
  33. Pask G. Electronic keyboard teaching machines. Education and Commerce, 1958. Vol. 24, pp. 16–26.
  34. Reddy S., Labutov I., Joachims T. Learning student and content embeddings for personalized lesson sequence recommendation. Proceedings of the Third (2016) ACM Conference on Learning: Edinburgh, Scotland UK, April 25–26. Edinburgh, 2016, pp. 93–96. DOI:10.1145/2876034.2893375
  35. Skinner B.F. Teaching Machines. Science, 1958. Vol. 128, no. 3330, pp. 969–977. DOI:10.1126/science.128.3330.969
  36. Nye B.D. et al. SKOPE-IT (Shareable Knowledge Objects as Portable Intelligent Tutors): overlaying natural language tutoring on an adaptive learning system for mathematics. International journal of STEM education, 2018. Vol. 5, no. 12, pp. 1–20. DOI:10.1186/s40594-018-0109-4
  37. Xie J. et al. Student learning strategies and behaviors to predict success in an online adaptive mathematics tutoring system. Proceedings of the 10th International Conference on Educational Data Mining. 2017, pp. 460–465.
  38. Sun Q., Norman T.J., Abdourazakou Y. Perceived value of interactive digital textbook and adaptive learning: Implications on student learning effectiveness. Journal of Education for Business, 2018. Vol. 93, no. 7, pp. 323–331. DOI:10.1080/08832323.2018.1493422
  39. Samulski T.D. et al. The utility of adaptive eLearning in cervical cytopathology education. Cancer cytopathology, 2018. Vol. 126, no. 2, pp. 129–135. DOI:10.1002/cncy.21942
  40. Wang Y., Liao H.C. Adaptive learning for ESL based on computation. British Journal of Educational Technology, 2011. Vol. 42, no. 1, pp. 66–87. DOI:10.1111/j.1467-8535.2009.00981.x
  41. Wauters K., Desmet P., Van Den Noortgate W. Item difficulty estimation: An auspicious collaboration between data and judgment. Computers & Education, 2012. Vol. 58, no. 4, pp. 1183–1193. DOI:10.1016/j.compedu.2011.11.020
  42. Wenger E. Artificial Intelligence and Tutoring Systems. Los Altos, CA: Morgan Kaufmann Publisher, 1987. 485 p.
  43. Yudelson M., Koedinger K.R., Gordon G.J. Individualized bayesian knowledge tracing models. In Proceedings of 16th International Conference on Artificial Intelligence in Education (AIED 2013). Springer, 2013, pp. 171–180. DOI:10.1007/978-3-642-39112-5_18
  44. Zhong J. Actively Engage Students with Diverse Background Using a More Personalized Approach. IEEE Frontiers in Education Conference (FIE): San Jose, CA, USA, 3–6 Oct. 2018. San Jose: IEEE, 2018, pp. 1–5. DOI:10.1109/FIE.2018.8658485

© 2007–2021 Portal of Russian Psychological Publications. All rights reserved in Russian

Publisher: Moscow State University of Psychology and Education

Catalogue of academic journals in psychology & education MSUPE

Creative Commons License Open Access Repository     Webometrics Ranking of Repositories

RSS Psyjournals at facebook Psyjournals at Twitter Psyjournals at Youtube ??????.???????