Studying and enhancing the methods for distance teaching of computer science in Kazakh secondary school students during the pandemic

Introduction. In 2020, the first two weeks of the enforced transformation of all the levels of school education, which was initiated as one of the measures against the Coronavirus disease (COVID-19), revealed a range of issues hampering the appropriate distance education. The absence of the methodological basis for conducting online classes in the Kazakh pedagogical community defined the need to study and enhance forms and technologies that would be efficient to use to interact with students during the transition of the national education system to distance education.

Aim. The aim of this research lies in defining efficient methods for distance teaching of computer science in the Kazakh secondary school students in terms of ensuring the maintenance of the quality of knowledge and the academic progress of students at the sufficient level corresponding to that of the traditional in-person education.

Methodology and research methods. A total of five educators and 320 students of three Kazakh schools took part in the study. At the moment of the experiment, the students were aged 12 to 18 years old. The participants were divided into seven groups according to the educational level (5th–11th forms) in order to make it more convenient to trace qualitative changes in the academic progress depending on the selected method for distance teaching of computer science. The authors conducted three control evaluations of the quality of knowledge in each of the 320 participants. The t-test for unpaired samples for every group was conducted to prove the statistical certainty of the calculated average reference values, which were required to confirm the viability of the conducted research. The analysis of the data obtained at the concluding stage of the experiment allowed to compare them with the reference values calculated at the preliminary stage of the research in question. For the comparison, The authors applied the Mann–Whitney U test for independent samples.

Results. The preliminary analysis of the quality of knowledge related to the discipline of computer science in the participants revealed generally high and average level of both acquisition of theoretical information and development of the subject-related skills, which was registered based of the results of in-person education. The leading experience of the specialists composing the authors’ initiative research group allowed developing a structural scheme for an online lesson. The lessons applying this scheme were conducted up to the end of the academic quarter. By conducting the Mann–Whitney U test, we discovered that the obtained average values of the quality of teaching computer science to the participants statistically increased (I group – Uemp = 6.49 (p ≤ 0.05), II group – Uemp = 7.46 (p ≤ 0.05), III group – Uemp = 6.05 (p ≤ 0.01), IV group – Uemp = 6.71 (p ≤ 0.05), V group – Uemp = 6.91 (p ≤ 0.01), VI group – Uemp = 6.65 (p ≤ 0.05), VII group – Uemp = 6.21 (p ≤ 0.05)). Despite temporary fall in the efficiency of teaching computer science registered during the transition to the distance model, it was managed to achieve the level of academic progress and acquisition of knowledge corresponding to that of in-person education.

Scientific novelty. TThe significance of the collected and analysed data was statistically proved. The data confirmed the efficiency of the use of defined and adapted approaches and teaching techniques, which were able to compensate the absence of traditional in-person lessons, while preventing the fall in the academic progress and the quality of knowledge in students.

Practical significance. The obtained results evidence the success of the arrangements aimed at the enhancement of the methods for distance teaching of computer science in the Kazakh secondary school students during the transition to distance education enforced due to the pandemic.

1. Akhunov A. M. The COVID-19 pandemic as a challenge for post-Soviet Central Asia countries. Mezhdunarodnaya analitika = Journal of International Analytics. 2020; 11 (1): 114–128. DOI: 10.46272/2587-8476-2020-11-1-114-128 (In Russ.)

2. Zhiltsov S. S. Coronavirus hits former-Soviet countries. Problemy postsovetskogo prostranstva = Post-Soviet Issues. 2020; 7 (1): 8–17. DOI: 10.24975/2313-8920-2020-7-1-8-17 (In Russ.)

3. Aimkulov R. A., Aubakirov F. M., Kazezova M. K. Impact of COVID-pandemic on the economy of Kazakhstan. Vestnik Kazakhskogo gumanitarno-yuridicheskogo innovatsionnogo universiteta = Bulletin of the Kazakh Humanitarian and Legal Innovative University. 2020; 4: 28–31. (In Russ.)

4. Glas O. Corona in Kazakhstan – An authoritarian transparency offensive. New Eastern Europe. 2020; 42 (4): 114–119.

5. Romero C., Ventura S. Educational data science in massive open online courses. WIREs Data Mining and Knowledge Discovery. 2017; 7: 1187. DOI: 10.1002/widm.1187

6. Pardala A. Informatization of mathematics education: Didactic opportunities, experience and foreign trends. Informatics and Education. 2019; 6: 49–55. DOI: 10.32517/0234-0453-2019-34-6-49-55

7. Picciano A. G. Theories and frameworks for online education: Seeking an integrated model. Online Learning. 2017; 21 (3): 166–190. DOI: 10.24059/olj.v21i3.1225

8. Kaderkeyeva Z., Bekmanova G., Sharipbay A., Omarbekova A. A model and a method for assessing students’ competencies in e-learning system. In: DATA ‘19: Proceedings of the Second International Conference on Data Science, E-Learning and Information. New York: Association for Computing Machinery; 2019. p. 1–5. DOI: 10.1145/3368691.3372391

9. Fayanto S., Kawuri M., Jufriansyah A., Setiamukti D., Sulisworo D. Implementation e-learning based Moodle on physics learning in senior high school. Indonesian Journal of Science and Education. 2019; 3 (2): 93–102. DOI: 10.31002/ijose.v3i2.1178

10. Kaya M., Ozel S. A. Integrating an online compiler and a plagiarism detection tool into the Moodle distance education system for easy assessment of programming assignments. Computer Applications in Engineering Education. 2015; 23: 363–373. DOI: 10.1002/cae.21606

11. Oyelere S. S., Suhonen J., Wajiga G. M., Sutinen E. Design, development, and evaluation of a mobile learning application for computing education. Education and Information Technologies. 2018; 23: 467–495. DOI: 10.1007/s10639-017-9613-2

12. Konig J., Jager-Biela D. J., Glutsch N. Adapting to online teaching during COVID-19 school closure: Teacher education and teacher competence effects among early career teachers in Germany. European Journal of Teacher Education. 2020; 43 (4): 608–622. DOI: 10.1080/02619768.2020.1809650

13. Riese E., Kann V. Computer science majors’ experiences of their distance education caused by the COVID-19 pandemic. In: 2021 IEEE Global Engineering Education Conference (EDUCON). Vienna: IEEE; 2021. p. 393–397.

14. Fujita N. Transforming online teaching and learning: Towards learning design informed by information science and learning sciences. Information and Learning Sciences. 2020; 121 (7/8): 503–511. DOI: 10.1108/ILS-04-2020-0124

15. Danilchuk E. V., Kulikova N. Y., Chernyshova M. V., Volkov D. V. Teaching computer science in the context of virtualization of the educational space. Sovremennye problemy nauki i obrazovaniya = Modern Problems of Science and Education. 2019; 6: 28–28. (In Russ.)

16. Vajndorf-Sysoeva M. E., Gryaznova T. S., Shitova V. A. Metodika distancionnogo obucheniya = Distance learning methodology. Moscow: Publishing House Jurajt; 2018. 194 p. (In Russ.)

17. Cope B., Kalantzis M. Big data comes to school: Implications for learning, assessment, and research. AERA Open. 2016; 2 (2): 1–19. DOI: 10.1177/2332858416641907

18. Koval N. N. Modern information and communication technologies in analytical management activities: Problems and prospects. Karel’skij nauchnyj zhurnal = Karelian Scientific Journal. 2015; 1 (10): 39–44. (In Russ.)

19. Serikbaeva A. R., Omarov A. M. Avtomatizaciya dokumentooborota v sfere obrazovaniya = Automation of Document Circulation in the Sphere of Education. Aktual’nye nauchnye issledovaniya v sovremennom mire = Actual Scientific Research in the Modern World. 2019; 47 (3-7): 93–95. (In Russ.)

20. Morgan F., Cawley S., Coffey A., Callaly F., Lyons D., O’Loughlin D., Killoran P. ViciLogic: Online learning and prototyping platform for digital logic and computer architecture. In: eChallenges e-2014 Conference Proceedings. Belfast; 2014. p. 1–9.

21. Petac E. New challenges of supportive technologies for education. In: Proceedings: International Conference on Creative Collaboration through Supportive Technologies (ICCCST 2015). Bucharest: MatrixRom Publishing House; 2015. Vol. 24. p. 3–8.

22. Sivinskiy A. M. Development of an effective model for teaching hearing-impaired children within the scope of modern approaches to education. Azimut nauchnyh issledovanij: pedagogika i psihologiya = Azimuth of Scientific Research: Pedagogy and Psychology. 2020; 9 (2(31)): 241–244. DOI: 10.26140/anip-2020-0902-0055 (In Russ.)

23. Sivinskiy A. M., Kulambayeva K. K. Learning effectiveness and evaluation technology in school for hearing-impaired children. Education and Self Development. 2019; 14 (2): 92–104. DOI: 10.26907/esd14.2.08

24. Nadeak B. The effectiveness of distance learning using social media during the pandemic period of COVID-19: A case in Universitas Kristen Indonesia. International Journal of Advanced Science and Technology. 2020; 29 (7): 1764–1772.

25. Lassoued Z., Alhendawi M., Bashitialshaaer R. An exploratory study of the obstacles for achieving quality in distance learning during the COVID-19 pandemic. Education Sciences. 2020; 10 (9): 232. DOI: 10.3390/educsci10090232

26. Marek M. W., Chew C. S., Wu W. C. V. Teacher experiences in converting classes to distance learning in the COVID-19 pandemic. International Journal of Distance Education Technologies. 2021; 19 (1): 40–60. DOI: 10.4018/IJDET.20210101.oa3

27. Klein P., Ivanjek L., Dahlkemper M. N., Jelicic K., Geyer M. A., Kuchemann S., Susac A. Studying physics during the COVID-19 pandemic: Student assessments of learning achievement, perceived effectiveness of online recitations, and online laboratories. Physical Review Physics Education Research. 2021; 17 (1): 1–11. DOI: 10.1103/PhysRevPhysEducRes.17.010117