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Personalised higher education based on microcourses: Possible ways of implementation

https://doi.org/10.17853/1994-5639-2024-3-40-68

Abstract

Introduction. Educators all over the world agree that human education at any stage of development should be personalised. In order for the training to be personalised, the knowledge, competencies, behaviuoral models and approaches that need to be learned must be presented to a student in such a way that they have a value sense, are relevant and arouse his/her desire to learn even more.

The problem of the study is the lack of sound methods for implementing a personalised approach in higher education. At the same time, it is noted that the learning process based on the introduction of microcourses contains the potential for the implementation of personalisation ideas, since microcourses are aimed at meeting the individual needs of the student.

Aim. The present research aims to substantiate the possibilities of personalised learning based on microcourses at universities (master’s level).

Methodology and research methods. The methodological framework of the research is the theory of the competence-based approach, the paradigm of personality-oriented learning, as well as scientific work in the field of personalised education.

Results. The article presents a model of personalised learning at a university based on the use of microcourses. The principles of its construction are described: the responsibility of the university for the preparation of a graduate; reasonable conservatism; compliance of the initiative topic with the direction of training; systematic and systematic control. The concept of a microcourse of an academic discipline is introduced; examples of students choosing microcourses are given. The model of personalisation of education based on the use of microcourses, in addition to traditional sections, such as planned learning outcomes and a calendar curriculum, also contains new aspects (compared to current models of student learning): diagnostics of student’s capabilities in the implementation of personalisation; training regulations (including personal goals); schemes for the selection of general and additional modules, availability of initiative modules; diagnostics of achievements. The results can be broadcast to various areas of master’s degree programmes.

Scientific novelty. The scientific novelty consists in the description of the possibilities of implementing a personalised approach in education based on the use of microcourses in master’s degree programmes.

Practical significance. The developed model of personalisation of learning based on microcourses can be applied at all levels in the higher education system for various profiles of preparation. It will be especially significant for the profiles of the educational direction.

About the Authors

L. O. Denishcheva
Moscow City Pedagogical University
Russian Federation

Larisa O. Denishcheva – Cand. Sci. (Education), Professor, Department of Mathematics and Physics,

Moscow.

Author ID 730525; Moscow.



I. S. Safuanov
Moscow City Pedagogical University
Russian Federation

Ildar S. Safuanov – Dr. Sci. (Education), Professor, Department of Mathematics and Physics,

Moscow.  Author ID 103864, Scopus Author ID 15731713700, Researcher ID R-9025-2017.



Yu. A. Semenyachenko
Moscow City Pedagogical University
Russian Federation

Yulia A. Semenyachenko – Cand. Sci. (Education), Associate Professor, Department of Mathematics and Physics,

Moscow.

Author ID 721324.



References

1. Denishcheva L. O., Safuanov I. S., Semenyachenko Yu. A. Opportunities of ensuring the personalization of education at the university. Vestnik MGPU. Seriya “Informatika i informatizaciya obrazovaniya” = Bulletin of Moscow City University. Series “Informatics and Informatization of Education”. 2022; 2 (60): 72–85. (In Russ.)

2. Dewey J. Demokratija i obrazovanie = Democracy and education [Internet]. Moscow: Publishing House Pedagogika-Press; 2000 [cited 2023 Mar 24]. 384 p. Available from: https://rusneb.ru/catalog/000199_000009_000654051 (In Russ.)

3. Watters A. Teaching machines: The history of personalized learning [Internet]. Cambridge, Massachusetts: The MIT Press; 2021 [cited 2023 Mar 24]. 316 p. Available from: https://mitpress.mit.edu/9780262363754/teaching-machines

4. Bloom B. S. Learning for mastery. Evaluation Comment (UCLA-CSIEP) [Internet]. 1968 [cited 2023 Mar 24]; 1 (2): 1–12. Available from: https://eric.ed.gov/?id=ED053419

5. Bloom B. S. The 2 sigma problem: The Search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher. 1984; 13 (6): 4–16. DOI: 10.2307/1175554

6. Zeer E. F., Krezhevskikh O. V. Conceptual and theoretical foundations of personalised learning. Obrazovanie i nauka = The Education and Science Journal. 2022; 24 (4): 11–39. DOI: 10.17853/1994-5639-2022-4-11-399 (In Russ.)

7. Yermakov D. S., Kirillov P. N., Koryakina N. I., Yankevich S. A. Personalizirovannaya model obrazovaniya s ispolzovaniem cifrovoy platformy = Personalized education model using a digital platform [Internet]. Moscow: Publishing House Vklad v Buduschee. Blagotvoritelnyj fond Sberbanka; 2020 [cited 2023 Mar 24]. Available from: https://vbudushee.ru/upload/lib/%D0%9F%D0%9C%D0%9E.pdf (In Russ.)

8. Vygotsky L. S. Psikhologiya razvitiya cheloveka = Psychology of human development [Internet]. Moscow: Publishing Houses Smysl, Eksmo; 2005 [cited 2023 Mar 24]. 1136 p. Available from: https://archive.org/details/2006_20210417 (In Russ.)

9. Savina N. V. Methodologival foundations of personalized learning. Nauka o cheloveke: gumanitarnye issledovaniya = Russian Journal of Social Sciences and Humanities. 2020; 14 (4): 82–90. DOI: 10.17238/issn1998-5320.2020.14.4.10 (In Russ.)

10. Chatti M. A. Personalization in technology enhanced learning: A social software perspective [Internet]. Aachen: Shaker Verlag; 2010 [cited 2023 Mar 24]. 249 p. Available from: https://www.semanticscholar.org/paper/Personalization-in-technology-enhanced-learning%3A-a-Chatti/521acf72f5f6f8738921ddde03bf0212a6281ddc

11. Pontual Falcão T., e Peres F. M. A., Sales de Morais D. C., da Silva Oliveira G. Participatory methodologies to promote student engagement in the development of educational digital games. Computers & Education. 2018; 116: 161–175. DOI: 10.1016/j.compedu.2017.09.006

12. Spector J. M. The potential of smart technologies for learning and instruction. International Journal of Smart Technology & Learning. 2018; 1 (1): 21–32. DOI: 10.1504/IJSMARTTL.2016.078163

13. Lockspeiser T. M., Kaul P. Using individualized learning plans to facilitate learner-centered teaching. Journal of Pediatric and Adolescent Gynecology. 2016; 29 (3): 214–217. DOI: 10.1016/j.jpag.2015.10.020

14. FitzGerald E., Kucirkova N., Jones A., Cross S., Ferguson R., Herodotou C. Hillaire G., Scanlon E. Dimensions of personalisation in technology-enhanced learning: A framework and implications for design. British Journal of Educational Technology. 2018; 49 (1): 165–181. DOI: 10.1111/bjet.12534

15. Niknam M., Thulasiraman P. LPR: a bio-inspired intelligent learning path recommendation system based on meaningful learning theory. Education and Information Technologies. 2020; 25: 3797–3819. DOI: 10.1007/s10639-020-10133-3

16. Schmid R., Petko D. Does the use of educational technology in personalized learning environments correlate with self-reported digital skills and beliefs of secondary-school students? Computers & Education. 2019; 136: 75–86. DOI: 10.1016/j.compedu.2019.03.006

17. Liu M., McKelroy E., Corliss S. B., Carrigan J. Investigating the effect of an adaptive learning intervention on students’ learning. Educational Technology Research and Development. 2017; 65 (6): 1605–1625. DOI: 10.1007/s11423-017-9542-1

18. Scheiter K., Schubert C., Schüler A., Schmidt H., Zimmermann G., Wassermann B., Krebs M., Eder T. Adaptive multimedia: Using gaze-contingent instructional guidance to provide personalized processing support. Computers & Education. 2019; 139: 31–47. DOI: 10.1016/j.compedu.2019.05.005

19. Afini Normadhi N. B., Shuib L., Md Nasir H. N., Bimba A., Idris N., Balakrishnan V. Identification of personal traits in adaptive learning environment: Systematic literature review. Computers & Education. 2019; 130: 168–190. DOI: 10.1016/j.compedu.2018.11.005

20. Xie H., Chu H. C., Hwang G. J., Wang C. C. Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education. 2019; 140: 103599 DOI: 10.1016/j.compedu.2019.103599

21. Bahçeci F., Gürol M. The effect of individualized instruction system on the academic achievement scores of students. Education Research International. 2016; 2016: 1–9. DOI: 10.1155/2016/7392125

22. Lee D., Huh Y., Lin C. Y., Reigeluth C. M. Technology functions for personalized learning in learner-centered schools. Educational Technology Research and Development. 2018; 6 (5): 1269–1302. DOI: 10.1007/s11423-018-9615-9

23. Jung E., Kim D., Yoon M., Park S., Oakley B. The influence of instructional design on learner control, sense of achievement, and perceived effectiveness in a supersize MOOC course. Computers & Education. 2019; 128: 377–388. DOI: 10.1016/j.compedu.2018.10.001

24. Shute V. J., Rahimi S. Review of computer-based assessment for learning in elementary and secondary education. Journal of Computer Assisted Learning. 2017; 33 (1): 1–19. DOI: 10.1111/jcal.12172

25. Fatahi S. An experimental study on an adaptive e-learning environment based on learner’s personality and emotion. Education and Information Technologies. 2019; 24 (4): 2225–2241. DOI: 10.1007/s10639-019-09868-5

26. Junokas M. J., Lindgren R., Kang J., Morphew J. W. Enhancing multimodal learning through personalized gesture recognition. Journal of Computer Assisted Learning. 2018; 34 (4): 350–357. DOI: 10.1111/jcal.12262

27. Chen S. Y., Huang P. R., Shih Y. C., Chang L. P. Investigation of multiple human factors in personalized learning. Interactive Learning Environments [Internet]. 2016 [cited 2023 Mar 24]; 24 (1): 119–141. Available from: https://www.learntechlib.org/p/194316/

28. Rastegarmoghadam M., Ziarati K. Improved modeling of intelligent tutoring systems using ant colony optimization. Education and Information Technologies. 2017; 22 (3): 1067–1087. DOI: 10.1007/s10639-016-9472-2

29. Ennouamani S., Mahani Z., Akharraz L. A context-aware mobile learning system for adapting learning content and format of presentation: Design, validation, and evaluation. Education and Information Technologies. 2020; 25: 3919–3955. DOI: 10.1007/s10639-020-10149-9

30. Pliakos K., Joo S. H., Park J. Y., Cornillie F., Vens C., Van den Noortgate W. Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems. Computers & Education. 2019; 137: 91–103. DOI: 10.1016/j.compedu.2019.04.009

31. Alamri H., Watson S., Watson W. Learning technology models that support personalization within blended learning environments in higher education. TechTrends. 2020; 65 (3): 62–68. DOI: 10.1007/s11528-020-00530-3

32. Walkington C., Bernacki M. L. Appraising research on personalized learning: Definitions, theoretical alignment, advancements, and future directions. Journal of Research on Technology in Education. 2020; 52 (3): 235–252. DOI: 10.1080/15391523.2020.1747757

33. Zha Y., Zhu Q. Research on vocational student personalized learning recommended model. In: Proceedings of the 2nd International Conference on Education, Management and Information Technology. Amsterdam: Atlantis Press; 2015. p. 800–805. DOI: 10.2991/icemit-15.2015.166

34. Leong K., Sung A., Au D., Blanchard C. A review of the trend of microlearning. Journal of Work-Applied Management. 2021; 13 (1): 88–102. DOI: 10.1108/jwam-10-2020-0044

35. Giurgiu L. Microlearning an evolving elearning trend. Scientific Bulletin. 2017; 22 (1): 18–23. DOI: 10.1515/bsaft-2017-0003

36. Aldosemani T. Microlearning for macro-outcomes: Students’ perceptions of telegram as a microlearning tool. In: Väljataga T., Laanpere M. (Eds.). Digital turn in schools – research, policy, practice, lecture notes in educational technology. 2019. p. 189–191. DOI: 10.1007/978-981-13-7361-9_13

37. Zhou Ping. Research on the application of micro-courses in advanced mathematics teaching. Frontiers in Educational Research [Internet]. 2019 [cited 2023 Mar 24]; 2 (11): 113–118. Available from: https://francis-press.com/papers/1104

38. Tianmei M. Research on the application of micro-course in the teaching of higher vocational mathematics. In: 5th International Workshop on Education, Development and Social Sciences (IWEDSS 2019). Tokyo; 2019. p. 323–326. DOI: 10.25236/iwedss.2019.069

39. Zhang R. Research and practice of microcourse teaching in college mathematics under the mode of flipped classroom teaching. In: IOP Conference Series: Materials Science and Engineering. 2018; 439 (3): 2–6. DOI: 10.1088/1757-899x/439/3/032062


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For citations:


Denishcheva L.O., Safuanov I.S., Semenyachenko Yu.A. Personalised higher education based on microcourses: Possible ways of implementation. The Education and science journal. 2024;26(3):40-68. (In Russ.) https://doi.org/10.17853/1994-5639-2024-3-40-68

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ISSN 1994-5639 (Print)
ISSN 2310-5828 (Online)