Research Article

Psychological Aspects of Digital Learning: A Self-Determination Theory Perspective

Nailya R. Salikhova 1 * , Martin F. Lynch 1 2 3, Albina B. Salikhova 4
More Detail
1 Kazan (Volga region) Federal University, Russia2 University of Rochester, USA3 Higher School of Economics, Moscow, Russia4 Sechenov First Moscow State Medical University (Sechenov University), Russia* Corresponding Author
Contemporary Educational Technology, 12(2), October 2020, ep280, https://doi.org/10.30935/cedtech/8584
OPEN ACCESS   4948 Views   3354 Downloads
Download Full Text (PDF)

ABSTRACT

The purpose of this article was to compile a general map of existing research on digital education from the Self-determination theory (SDT) perspective, in order to understand SDT’s contribution to the emerging field of research on digital technologies in education, the methods used to advance this research, the gaps in existing research, and the development of the theory itself in this context. Methods include searching in databases or search engines and chaining from known research papers. Papers were classed as relevant if their primary focus was to explore the Self-determination theory perspective for digital education. Articles published over the past twelve years in leading scientific journals were analyzed and synthesized. Results show that this theory is actively used both in studies on digital education and in the development of training programs. It makes a significant contribution to solving the problem of continuing digital learning and its motivation, to predicting the academic success of students, to increasing teachers’ motivation to use digital resources. The ideas of SDT have become an important reference point in various formats of digital education: MOOC, hybrid virtual classes, mobile applications, etc. The study found that digital education technologies provide many opportunities to satisfy the need for autonomy whereas they pose the greatest challenge to the need for relatedness. Research in the context of digital education provides new perspectives for the development of SDT, clarifying the relationships of basic needs among themselves. The materials presented in the article are useful for planning further research from the point of view of SDT, as well as for use in the development of digital educational resources. The scientific novelty of this study is to collate, highlight and generalize the directions of application of Self-determination theory in the rapidly developing field of digital education. As an original result, a new general map of the main areas of such research has been created. The review categorizes the literature into five different areas: predicting motivation and intentions to continue digital learning, predicting student academic success, combining SDT ideas with other theories in digital education research, application of SDT for creating online courses, and teachers’ readiness to use digital education.
Keywords: self-determination theory, internal motivation, online learning, digital education

CITATION (APA)

Salikhova, N. R., Lynch, M. F., & Salikhova, A. B. (2020). Psychological Aspects of Digital Learning: A Self-Determination Theory Perspective. Contemporary Educational Technology, 12(2), ep280. https://doi.org/10.30935/cedtech/8584

REFERENCES

  1. Ahn, S. J., Johnsen, K., & Ball, C. (2019). Points-Based Reward Systems in Gamification Impact Children’s Physical Activity Strategies and Psychological Needs. Health Education and Behavior, 46(3), 417-425. https://doi.org/10.1177/1090198118818241
  2. Bachman, C. M., & Stewart, C. (2011). Self-Determination Theory and Web-Enhanced Course Template Development. Teaching of Psychology, 38(3), 180-188. https://doi.org/10.1177/0098628311411798
  3. Barak, M., Watted, A., & Haick, H. (2016). Motivation to learn in massive open online courses: Examining aspects of language and social engagement. Computers and Education, 94, 49-60. https://doi.org/10.1016/j.compedu.2015.11.010
  4. Baranik, L. E., Wright, N. A., & Reburn, K. L. (2017). Mentoring relationships in online classes. Internet and Higher Education, 34, 65-71. https://doi.org/10.1016/j.iheduc.2017.05.001
  5. Baumeister, R. F., & Leary, M. R. (1995). The Need to Belong: Desire for Interpersonal Attachments as a Fundamental Human Motivation. Psychological Bulletin, 117(3), 497-529. https://doi.org/10.1037/0033-2909.117.3.497
  6. Belkhamza, Z., & Bin Abdullah, M. M. (2019). Trainee characteristics and organizational environment for enhancing individual performance in e-learning involvement. International Journal of Web-Based Learning and Teaching Technologies, 14(2), 88-101. https://doi.org/10.4018/IJWLTT.2019040106
  7. Betoret, F. D., & Artiga, A. G. (2011). The relationship among student basic need satisfaction, approaches to learning, reporting of avoidance strategies and achievement. Electronic Journal of Research in Educational Psychology, 9(2), 463-496.
  8. Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. Internet and Higher Education, 27(573) 1-13. https://doi.org/10.1016/j.iheduc.2015.04.007
  9. Bubnova, I. S., Khvatova, M. A., Chernik, V. E., Popova, O. V., Prokopyev, A. I., Naumov, P. Yu., & Babarykin, O. V. (2018). Research of Professional Activity Features of Ecologist at Carrying Out Public Ecological Examination. Ekoloji, 106, 999-1006, Article No: e106183
  10. Buhr, E. E., Daniels, L. M., & Goegan, L. D. (2019). Cognitive appraisals mediate relationships between two basic psychological needs and emotions in a massive open online course. Computers in Human Behavior, 96, 85-94. https://doi.org/10.1016/j.chb.2019.02.009
  11. Butakova, M. M., Sokolova, O. N., Zaitseva, N. A., Larionova, A. A., Ignatova, M. N., Trufanova, S. N., & Yakovlev, A. Y. (2020). Evolution and current development trends of the Russian federation’s higher education system. Opción, 36(Special Edition 27), 1718-1733.
  12. Butz, N. T., & Stupnisky, R. H. (2017). Improving student relatedness through an online discussion intervention: The application of self-determination theory in synchronous hybrid programs. Computers and Education, 114, 117-138. https://doi.org/10.1016/j.compedu.2017.06.006
  13. Chang, H. H., Fu, C. S., & Huang, C. Y. (2017). Willingness to adopt or reuse an e-learning system: The perspectives of self-determination and perceived characteristics of innovation. Innovations in Education and Teaching International, 54(5), 511-520. https://doi.org/10.1080/14703297.2016.1194768
  14. Chen, K. C., & Jang, S. J. (2010). Motivation in online learning: Testing a model of self-determination theory. Computers in Human Behavior, 26, 741-752. https://doi.org/10.1016/j.chb.2010.01.011
  15. Cincinnato, S., Zhu, C., & De Wever, B. (2016). Teacher and institutional characteristics affecting teaching practices in OBL: A self-determination approach. Proceedings of the European Conference on e-Learning, pp. 750-757.
  16. Danowitz, A. (2016). Leveraging the final project to improve student motivation in introductory digital design courses. Proceedings - 46th Annual Frontiers in Education Conference, FIE 2016, Bayfront Convention CenterErie; United States, 7757380. https://doi.org/10.1109/FIE.2016.7757380
  17. De Araujo Guerra Grangeia, T., De Jorge, B., Franci, D., Santos, T. M., Setubal, M. S. V., Schweller, M., & De Carvalho-Filho, M. A. (2016). Cognitive load and self-determination theories applied to e-learning: Impact on students’ participation and academic performance. PLoS ONE, 11(3), e0152462. https://doi.org/10.1371/journal.pone.0152462
  18. De Barba, P. G., Kennedy, G. E., & Ainley, M. D. (2016). The role of students’ motivation and participation in predicting performance in a MOOC. Journal of Computer Assisted Learning, 32(3), 218-231. https://doi.org/10.1111/jcal.12130
  19. Deci, E. L., & Ryan, R. M. (2014). Autonomy and need satisfaction in close relationships: Relationships motivation theory. Human Motivation and Interpersonal Relationships: Theory, Research, and Applications, V.9789401785426, 53-73. https://doi.org/10.1007/978-94-017-8542-6_3
  20. Durksen, T. L, Chu, M. W., Ahmad, Z. F., Radil, A. I., & Daniels, L. M. (2016). Motivation in a MOOC: a probabilistic analysis of online learners’ basic psychological needs. Social Psychology of Education, 19(2), 241-260. https://doi.org/10.1007/s11218-015-9331-9
  21. Ellis, D., & Haugan, M. (1997). Modelling the information seeking patterns of engineers and research scientists in an industrial environment. Journal of Documentation, 53, 384-403. https://doi.org/10.1108/EUM0000000007204
  22. Escobar Fandiño, F. G., Muñoz, L. D., & Silva Velandia, A. J. (2019). Motivation and E-Learning English as a foreign language: A qualitative study. Heliyon, 5(9), e02394. https://doi.org/10.1016/j.heliyon.2019.e02394
  23. Fang, J., Tang, L., Yang, J., & Peng, M. (2019). Social interaction in MOOCs: The mediating effects of immersive experience and psychological needs satisfaction. Telematics and Informatics, 39, 75-91.
  24. Faye, C., & Sharpe, D. (2008). Academic motivation in university: The role of basic psychological needs and identity formation. Canadian Journal of Behavioural Science, 40(4), 189-199. https://doi.org/10.1037/a0012858
  25. Gao, F., Luo, T., & Zhang, K. (2012). Tweeting for learning: A critical analysis of research on microblogging in education published in 2008-2011. British Journal of Educational Technology, 43(5), 783-801. https://doi.org/10.1111/j.1467-8535.2012.01357.x
  26. Hamidi, H., & Chavoshi, A. (2018). Analysis of the essential factors for the adoption of mobile learning in higher education: A case study of students of the University of Technology. Telematics and Informatics, 35(4), 1053-1070. https://doi.org/10.1016/j.tele.2017.09.016
  27. Hew, K. F., & Cheung, W. S. (2014). Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review, 12, 45-58. https://doi.org/10.1016/j.edurev.2014.05.001
  28. Hew, T. S., & Kadir, S. L. (2016). Predicting the acceptance of cloud-based virtual learning environment: The roles of Self Determination and Channel Expansion Theory. Telematics and Informatics, 33(4), 990-1013. https://doi.org/10.1016/j.tele.2016.01.004
  29. Hsu, H. K., Wang, C. V., & Levesque-Bristol, C. (2019). Reexamining the impact of self-determination theory on learning outcomes in the online learning environment. Education and Information Technologies, 24(3), 2159-2174. https://doi.org/10.1007/s10639-019-09863-w
  30. Jeno, L. M., Grytnes, J. A., & Vandvik, V. (2017). The effect of a mobile-application tool on biology students’ motivation and achievement in species identification: A Self-Determination Theory perspective. Computers and Education, 107, 1-12. https://doi.org/10.1016/j.compedu.2016.12.011
  31. Jeno, L. M., Vandvik, V., Eliassen, S., & Grytnes, J. A. (2019). Testing the novelty effect of an m-learning tool on internalization and achievement: A Self-Determination Theory approach. Computers and Education, 128, 398-413. https://doi.org/10.1016/j.compedu.2018.10.008
  32. Joo, Y. J., So, H. J., & Kim, N. H. (2018). Examination of relationships among students’ self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers and Education, 122, 260-272. https://doi.org/10.1016/j.compedu.2018.01.003
  33. Ke, W., & Zhang, P. (2010). The effects of extrinsic motivations and satisfaction in open source software development. Journal of the Association for Information Systems, 11(12), 784-808.
  34. Kennedy, J. (2014). Characteristics of massive open online courses (MOOCS): A research review, 2009-2012. Journal of Interactive Online Learning, 13(1), 1-16.
  35. Khan, I. U., Hameed, Z., Yu, Y., Islam, T., Sheikh, Z., & Khan, S. U. (2018). Predicting the acceptance of MOOCs in a developing country: Application of task-technology fit model, social motivation, and self-determination theory. Telematics and Informatics, 35(4), 964-978. https://doi.org/10.1016/j.tele.2017.09.009
  36. Klassen, R. M., Perry, N. E., & Frenzel, A. C. (2012). Teachers’ relatedness with students: An underemphasized component of teachers’ basic psychological needs. Journal of Educational Psychology, 104(1), 150-165. https://doi.org/10.1037/a0026253
  37. Kop, R. (2011). The challenges to connectivist learning on open online networks: Learning experiences during a massive open online course. International Review of Research in Open and Distance Learning, 12(3), 19-38. https://doi.org/10.19173/irrodl.v12i3.882
  38. Kramer, I. M., & Kusurkar, R. A. (2017). Science-writing in the blogosphere as a tool to promote autonomous motivation in education. Internet and Higher Education, 35, 48-62. https://doi.org/10.1016/j.iheduc.2017.08.001
  39. Kreijns, K., Vermeulen, M., Van Acker, F., & van Buuren, H. (2014). Predicting teachers’ use of digital learning materials: combining self-determination theory and the integrative model of behaviour prediction. European Journal of Teacher Education, 37(4), 465-478. https://doi.org/10.1080/02619768.2014.882308
  40. Larionova, A. A., Zaitseva, N. A., Anoshina, Y. F., Gaidarenko, L. V., & Ostroukhov, V. M. (2018). The modern paradigm of transforming the vocational education system. Astra Salvensis, 6, 436-448.
  41. Liyanagunawardena, T. R., Adams, A. A., & Williams, S. A. (2013). MOOCs: A systematic study of the published literature 2008-2012. International Review of Research in Open and Distance Learning, 14(3), 202-227. https://doi.org/10.19173/irrodl.v14i3.1455
  42. Loizzo, J., Ertmer, P. A., Watson, W. R., & Watson, S. L. (2017). Adult MOOC learners as self-directed: Perceptions of motivation, success, and completion. Online Learning Journal, 21(2). https://doi.org/10.24059/olj.v21i2.889
  43. Martin, N. I., Kelly, N., & Terry, P. C. (2018). A framework for self-determination in massive open online courses: Design for autonomy, competence, and relatedness. Australasian Journal of Educational Technology, 34(2), 35-55. https://doi.org/10.14742/ajet.3722
  44. Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, competence, and relatedness in the classroom: Applying self-determination theory to educational practice. Theory and Research in Education, 7(2), 133-144. https://doi.org/10.1177/1477878509104318
  45. Pedrotti, M., & Nistor, N. (2016). User motivation and technology acceptance in online learning environments. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11th European Conference on Technology Enhanced Learning, Lyon; 9891, 472-477. https://doi.org/10.1007/978-3-319-45153-4_45
  46. Proulx, J. N., Romero, M., & Arnab, S. (2017). Learning Mechanics and Game Mechanics Under the Perspective of Self-Determination Theory to Foster Motivation in Digital Game Based Learning. Simulation and Gaming, 48(1), 81-97. https://doi.org/10.1177/1046878116674399
  47. Raes, A., Vanneste, P., Pieters, M., Windey, I., Van Den Noortgate, W., & Depaepe, F. (2020). Learning and instruction in the hybrid virtual classroom: An investigation of students’ engagement and the effect of quizzes. Computers and Education, 143, 103682. https://doi.org/10.1016/j.compedu.2019.103682
  48. Razumovskaya, M., Zaitseva N. A., Larionova, A. A., Chudnovskiy, A. D., & Breusova, E. A. (2018). Prospects for applying various forms of organizational integration to improve the quality of education. Astra Salvensis, 6, 348-362.
  49. Robertson, S. (2018). Exploring the efficacy of training and development for liaison librarians at Deakin University, Australia. Journal of Higher Education Policy and Management, 40(2), 107-120. https://doi.org/10.1080/1360080X.2018.1426370
  50. Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24(4), 1585-1604. https://doi.org/10.1016/j.chb.2007.06.001
  51. Rogers, R. (2017). The motivational pull of video game feedback, rules, and social interaction: Another self-determination theory approach. Computers in Human Behavior, 73, 446-450. https://doi.org/10.1016/j.chb.2017.03.048
  52. Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. New York: Guilford Publications.
  53. Shi, L., & Cristea, A. I. (2016). Motivational gamification strategies rooted in self-determination theory for social adaptive e-learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13th International Conference on Intelligent Tutoring Systems, Croatia, pp. 294-300. https://doi.org/10.1007/978-3-319-39583-8_32
  54. Sørebø, O., Halvari, H., Gulli, V. F., & Kristiansen, R. (2009). The role of self-determination theory in explaining teachers’ motivation to continue to use e-learning technology. Computers and Education, 53(4), 1177-1187. https://doi.org/10.1016/j.compedu.2009.06.001
  55. Summers, J. J., Waigandt, A., & Whittaker, T. A. (2005). A comparison of student achievement and satisfaction in an online versus a traditional face-to-face statistics class. Innovative Higher Education, 29(3), 233-250. https://doi.org/10.1007/s10755-005-1938-x
  56. Sun, Y., Ni, L., Zhao, Y., Shen, X. L., & Wang, N. (2019). Understanding students’ engagement in MOOCs: An integration of self-determination theory and theory of relationship quality. British Journal of Educational Technology, 50(6), 3156-3174. https://doi.org/10.1111/bjet.12724
  57. Tan, Y. L. (2018). Meaningful gamification and students’ motivation: A strategy for scaffolding reading material. Online Learning Journal, 22(2), 141-156. https://doi.org/10.24059/olj.v22i2.1167
  58. Tschofen, C., & Mackness, J. (2012). Connectivism and dimensions of individual experience. International Review of Research in Open and Distance Learning, 13(1), 124-143.
  59. Vanslambrouck, S., Zhu, C., Lombaerts, K., Philipsen, B., & Tondeur, J. (2018). Students’ motivation and subjective task value of participating in online and blended learning environments. Internet and Higher Education, 36, 33-40. https://doi.org/10.1016/j.iheduc.2017.09.002
  60. Vanslambrouck, S., Zhu, C., Tondeur, J., Phillipsen, B., & Lombaerts, K. (2016). Adult learners’ motivation to participate and perception of online and blended environments. Proceedings of the European Conference on e-Learning, 14th European Conference on e-Learning, ECEL 2015, University of HertfordshireHatfield; United Kingdom, pp. 790-793.
  61. Veletsianos, G., & Shepherdson, P. (2016). A systematic analysis and synthesis of the empirical MOOC literature published in 2013-2015. International Review of Research in Open and Distance Learning, 17(2), 198-221.
  62. Wang, C. Hsu, H. K., Bonem, E. M., Moss, J. D., Yu, S., Nelson, D. B., & Levesque-Bristol, C. (2019). Need satisfaction and need dissatisfaction: A comparative study of online and face-to-face learning contexts. Computers in Human Behavior, 95, 114-125. https://doi.org/10.1016/j.chb.2019.01.034
  63. Yang, S., Zhou, S., & Cheng, X. (2019). Why do college students continue to use mobile learning? Learning involvement and self-determination theory. British Journal of Educational Technology, 50(2), 626-637. https://doi.org/10.1111/bjet.12634
  64. Zaitseva, N. A., Larionova, A. A., Nikolay, I. Shapovalov, N. I., Povorina, E. V., Takhumova, O. V., Zhukova, M. A., & Dvornikova, T.A. (2020). Regulatory aspects and problems of personnel certification taking into account the requirements of professional standards. International Journal of Psychosocial Rehabilitation, 24(03), 2179-2188. https://doi.org/10.37200/IJPR/V24I3/PR200966
  65. Zhou, M. (2016). Chinese university students’ acceptance of MOOCs: A self-determination perspective. Computers and Education, 92, 194-203. https://doi.org/10.1016/j.compedu.2015.10.012