Interaction, Practical Creativity, and Academic Performance in Digital Higher Education Environments: A Multivariate Analysis from Learning Analytics
DOI:
https://doi.org/10.46842/ipn.cien.v30n1a10Keywords:
learning analytics, digital interaction, creativity in action, higher education, academic performance, multivariate analysisAbstract
The growth of digital learning environments has expanded the possibilities for empirically analyzing student participation dynamics through data generated by educational platforms. In the field of learning analytics, digital interaction has become a key indicator of academic performance; however, its qualitative dimension—particularly in terms of practical creativity—has been less explored. This study examines the relationship between digital interaction, creativity in action, and academic performance in higher education using a quantitative approach. To this end, an observational design based on activity logs from virtual platforms is employed, analyzed through descriptive statistics, correlation matrices, regression models, and multilevel models. The results show that student interaction is positively associated with academic performance, especially when participation dynamics involve active forms of engagement in discussions. Practical creativity also shows significant effects, suggesting that the quality of participation is as relevant as its frequency. The multilevel model confirms the stability of these effects when considering variations across courses. These findings highlight the importance of designing digital environments that promote not only participation, but also forms of interaction that foster more active and collaborative learning processes.
References
[1] D. Gašević, S. Joksimović, B. Eagan, D. W. Shaffer, “SENS: Network analytics to combine social and cognitive perspectives of collaborative learning,” Computers in Human Behavior, vol. 92, pp. 562–577, 2019, doi: https://doi.org/10.1016/j.chb.2018.07.003
[2] D. Ifenthaler, J. Yau, “Utilising learning analytics for study success: Reflections on current empirical findings,” Research and Practice in Technology Enhanced Learning, vol. 15, no. 1, pp. 1–17, 2020, doi: https://doi.org/10.1007/978-3-319-64792-0_2
[3] R. Luckin, AI for Schoolteachers. London, UK: Routledge, 2022.
[4] A. Wise, D. W. Shaffer, “Why theory matters more than ever in the age of big data,” Journal of Learning Analytics, vol. 8, no. 2, pp. 1–5, 2021, doi: https://doi.org/10.18608/jla.2015.22.2
[5] F. Martin, T. Sun, C. D. Westine, “A systematic review of research on online teaching and learning from 2009 to 2018,” Computers & Education, vol. 159, 2020, doi: https://doi.org/10.1016/j.compedu.2020.104009
[6] B. Rienties, D. Tempelaar, Q. Nguyen, A. Littlejohn, “Unpacking the intertemporal impact of self-regulation in blended learning environments,” The Internet and Higher Education, vol. 45, 2020, doi: https://doi.org/10.1016/j.chb.2019.07.007
[7] V. P. Glăveanu, “A sociocultural theory of creativity: Bridging the social, the material and the psychological,” Creativity Research Journal, vol. 32, no. 1, pp. 1–10, 2020, doi: https://doi.org/10.1177/1089268020961763
[8] Z. Papamitsiou, A. Economides, “Learning analytics and educational data mining in practice: A systematic literature review,” Educational Technology & Society, vol. 24, no. 2, pp. 49–64, 2014, https://eric.ed.gov/?id=EJ1045537
[9] OECD, PISA 2022 Assessment and Analytical Framework: Creative Thinking. Paris, France: OECD Publishing, 2023, available: https://www.oecd.org/en/publications/pisa-2022-assessment-and-analytical-framework_dfe0bf9c-en.html
[10] O. Zawacki-Richter, V. Marín, M. Bond, F. Gouverneur, “Systematic review of research on artificial intelligence applications in higher education,” International Journal of Educational Technology in Higher Education, vol. 16, no. 39, 2019, doi: https://doi.org/10.1186/s41239-019-0171-0
[11] D. Tempelaar, B. Rienties, Q. Nguyen, “Subjective data, objective data and the role of bias in predictive modelling,” Computers in Human Behavior, vol. 104, 2020, doi: https://doi.org/10.1371/journal.pone.0233977
[12] R. A. Beghetto, “How Times of Crisis Serve as a Catalyst for Creative Action: An Agentic Perspective,” Frontiers in Psychology, vol. 12, 2021, doi: https://doi.org/10.3389/fpsyg.2020.600685
[13] D. Henriksen, E. Creely, M. Henderson, P. Mishra, “Creativity and technology in teaching and learning: a literature review of the uneasy space of implementation,” Educational Technology Research and Development, 2021, doi: https://doi.org/10.1007/s11423-020-09912-z
[14] S. Joksimović, V. Kovanović, S. Dawson, D. Gašević, G. Siemens, “The journey of learning analytics,” HERDSA Review of Higher Education, vol. 6, pp. 37–63, 2019, available: https://hdl.handle.net/11541.2/142171
[15] N. Selwyn, Education and Technology: Key Issues and Debates, 3rd ed. London, U.K.: Bloomsbury Academic, 2023.
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