Blended Learning Using E-Courses in the Assessments of University Students: Decision Tree

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Abstract

The digital transformation of education is a sustainable international trend. Modern digital technologies allow universities to improve the quality of education through the development of digital competencies of students, individualization of learning, the introduction of e-learning courses and new learning formats. At the same time, there are a number of common opinions and prejudices about online learning in the digital environment of the university. The aim of the study is to compare students’ attitude to learning in the university digital environment at different levels of education and to identify their characteristic predictive opinions. We compared the opinions of students of master’s and second higher education programs (N = 161) and students of bachelor’s and specialty programs of the first higher education (N = 183) using a questionnaire that assesses students’ attitudes towards e-learning. For the questionnaire, using the CHAID analysis method, a decision tree was built and three predictors were identified: “Face-to-face meetings or webinars with a teacher are not needed at all, video recordings and contacts through forums are quite enough”, “It’s hard to get used to a new form of training in the EUL format” and “It’s high time to introduce e-learning”. Graduate and second-degree students are less likely to refuse face-to-face meetings with teachers and are less likely to agree that e-learning should be introduced, but more often they report that e-learning is easy to get used to. However, the overall percentage of correct predictions of the model was 65 % (52 % for graduate and second graduate students and 78 % for undergraduate and specialty students), which suggests little predictive power of the model and indicates that the results contradict the bias that older graduate students find it more difficult to get used to e-learning, that they are more challenged and more critical.

General Information

Keywords: digital educational environment, digital transformation of education, blended learning, e-learning course, scaffolding, flipped classroom model, CHAID analysis, decision tree

Publication rubric: Modeling and Data Analysis for Digital Education

Article type: scientific article

Funding. The reported study was funded by the Moscow State University of Psychology and Education (MSUPE) in the framework of the research project “Digital Technologies in Higher Education: Development of Technology for Individualizing Education Using E-Courses.

For citation: Sorokova M.G., Radchikova N.P. Blended Learning Using E-Courses in the Assessments of University Students: Decision Tree. Digital Humanities and Technology in Education (DHTE 2021): Collection of Articles of the II All-Russian Scientific and Practical Conference with International Participation. November 11-12, 2021 / V.V. Rubtsov, M.G. Sorokova, N.P. Radchikova (Eds). Moscow: Publishing house MSUPE, 2021., pp. 571–588.

References

  1. Margolis A.A. Zone of Proximal Development, Scaffolding and Teaching Practice. Kul’turno-istoricheskaya psikhologiya = Cultur­al-Historical Psychology. 2020, Vol. 16, no. 3, pp. 15–26. doi:10.17759/ chp.2020160303.
  2. Margolis A.A. Chto smeshivaet smeshannoe obuchenie? [What Kind of Blending Makes Blended Learning?] Psikhologicheskaya nauka i obrazovanie = Psychological Science and Education, 2018. Vol. 23, no. 3, pp. 5–19. doi:10.17759/pse.2018230301. (In Russ., аbstr. in Engl.)
  3. Postanovlenie Pravitel’stva Rossiiskoi Federatsii ot 16.11.2020 № 1836 “O gosudarstvennoi informatsionnoi sisteme “Sovremennaya tsifrovaya obrazovatel’naya sreda” URL: http://publication.pravo. gov.ru/Document/View/0001202011190005 (дата обращения 22.08.2021) (In Russ.)
  4. Sorokova, M.G. (2020). E-Course as Blended Learning Digital Educational Resource in University. Psikhologicheskaya nauka i obrazovanie=Psychological Science and Education, 25(1), 36–50. https://doi.org/10.17759/pse.2020250104.
  5. Sorokova M.G., Odintsova M.A., Radchikova N.P. Obrazovatel’nye rezul’taty studentov v elektronnykh kursakh pri smeshannom i onlain-obuchenii [Students Educational Results in Blended and Online E-Courses]. Modelirovanie i analiz dannykh = Modelling and Data Analysis, 2021. Vol. 11, no. 1, pp. 61–77. doi:10.17759/ mda.2021110105 (In Russ., аbstr. in Engl.)
  6. Sorokova M.G., Odintsova M.A., Radchikova N.P. Scale for As­sessing University Digital Educational Environment (AUDEE Scale). Psikhologicheskaya nauka i obrazovanie = Psychological Sci­ence and Education. 2021, Vol. 26, no. 2, pp. 52–65. doi:10.17759/ pse.2021260205.
  7. Arıf S., Omar İ. Effectiveness of Flipped Classroom in Teaching Basic English Courses. Yükseköğretim Dergisi. 2019, no. 9(3), pp. 279–289. doi:10.2399/yod.19.003.
  8. Awidi I.T., Paynter M. The impact of a flipped classroom approach on student learning experience. Computers & Education. 2019, no. 128, pp. 269–283. doi:10.1016/j.compedu.2018.09.013.
  9. Baldwin S.J. Assimilation in online course design. American Journal of Distance Education. 2019, no.33(3), pp. 195–211. doi:10.1080/089 23647.2019.1610304.
  10. Chesser S., Murrah W., Forbes S.A. Impact of personality on choice of instructional delivery and students’ performance. American Journal of Distance Education. 2020, no. 34(3), pp. 1–13 doi:10.1080/08923647.2019.1705116.
  11. Gulnaz F., Althomali A.D.A., Alzeer D.H. An Investigation of the perceptions and experiences of the EFL teachers and learners about the effectiveness of blended learning at Taif university. Interna­tional Journal of English Linguistics. 2020, no. 10(1), pp. 329–344. doi:10.5539/ijel.v10n1p329.
  12. Islam A.Y.M.., Sheikh A. A study of the determinants of post­graduate students’ satisfaction of using online research databas­es. Journal of Information Science. 2020, no. 46(2), pp. 273–287. doi:10.1177/0165551519834714.
  13. Kuhn S., Frankenhauser S., Tolks D. Digitale Lehr- und Lernange­bote in der medizinischen Ausbildung. Bundesgesundheitsbl. 2018. no. 61, pp. 201–209. doi:10.1007/s00103–017–2673-z.
  14. Li K., Canelas D. Learners’ perceptions and experiences of two chem­istry MOOCs: Implications for teaching and design. American Jour­nal of Distance Education. 2019, no. 33(4), pp. 245–261. doi:10.1080/08923647.2019.1639469.
  15. Pardo A., Jovanovic J., Dawson S., Gašević D., Mirriahi N. Using learning analytics to scale the provision of personalised feedback. British Journal of Educational Technology. 2019, no. 50(1), pp. 128–138. doi:abs/10.1111/bjet.12592.
  16. Rajaram K. Flipped classrooms: Scaffolding support system with real-time learning interventions. Asian Journal of the Scholarship of Teaching and Learning. 2019, no. 9(1), pp. 30–58.
  17. Røe Y., Rowe M., Ødegaard N.B., Sylliaas H., Dahl-Michelsen T. Learning with technology in physiotherapy education: design, imple­mentation and evaluation of a flipped classroom teaching approach. BMC Medical Education. 2019, no. 19, p. 291. doi:10.1186/s12909–019–1728–2.
  18. Shearer R.L., Aldemir T., Hitchcock J., Resig J., Driver J., Kohler M. What students want: A vision of a future online learning experience grounded in distance education theory. American Journal of Distance Education. 2020, no. 34(1), pp. 36–52. doi:10.1080/08923647.2019.1706019.
  19. Sorokova M.G. Skepticism and learning difficulties in a digital en­vironment at the Bachelor’s and Master’s levels: are preconceptions valid? Heliyon. 2020, vol. 6, issue 11, E05335. doi:10.1016/j.heli­yon.2020.e05335
  20. Sorokova M., Odintsova M., Radchikova N. (2021): Digital technol­ogies in higher education: development of technology for individu­alizing education using e-courses. Research project data. Psychologi­cal Research Data & Tools Repository. Dataset. 2021, doi:10.25449/ruspsydata.14783226.v2
  21. Sukmawati R., Pramita M., Purba H., Utami B. The use of blended cooperative learning model in introduction to digital systems learn­ ing. Indonesian Journal on Learning and Advanced Education. 2020, no. 2(2), pp. 75–81. doi:10.23917/ijolae.v2i2.9263.
  22. Williamson B. Digital education governance: data visualization, pre­dictive analytics, and ‘real-time’ policy instruments. Journal of Edu­cation Policy. 2016, no. 31(2), pp. 123–141. https://doi.org/10.1080/ 02680939.2015.1035758.
  23. Wu B., Chen X. Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior. 2017, no. 67, pp. 221– 232. doi:10.1016/j.chb.2016.10.028.

Information About the Authors

Marina G. Sorokova, Doctor of Education, PhD in Physics and Matematics, docent, Head of Scientific and Practical Center for Comprehensive Support of Psychological Research "PsyDATA", Head of the Department of Digital Education, Moscow State University of Psychology and Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-1000-6487, e-mail: sorokovamg@mgppu.ru

Nataly P. Radchikova, PhD in Psychology, Leading Researcher of Scientific and Practical Center for Comprehensive Support of Psychological Research «PsyDATA», Moscow State University of Psychology & Education, Chief Specialist of the Laboratory of Biophysics of Excitable Media, Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, Pushchino;, Moscow, Russia, ORCID: https://orcid.org/0000-0002-5139-8288, e-mail: nataly.radchikova@gmail.com

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