Digital media technologies have become central to higher-education delivery, raising important questions about how online learning environments can support inclusion, equity, and sustainability under Sustainable Development Goal 4 (SDG 4). This study examines associations between recorded digital engagement and academic outcomes using the Open University Learning Analytics Dataset, which contains 32,593 enrolments from 28,785 learners across 22 course-presentations and 10,655,280 timestamped learner–VLE records. Descriptive statistics, Pearson correlation, and multivariable logistic regression were used to examine engagement–outcome associations and demographic inequities. Total VLE interactions were aggregated by learner-enrolment, transformed as log (x + 1), and standardised before modelling. Median interactions increased from 89 among withdrawn learners to 1896 among distinction learners, and log-engagement was positively correlated with success (r = 0.477). In the complete-case model (n = 31,482), a one-standard-deviation increase in log-engagement was associated with higher odds of success (OR = 27.074, 95 % CI [24.918, 29.415], p < 0.001) after adjustment for socioeconomic deprivation, gender, disability, age, prior education, previous attempts, and studied credits. Model discrimination was high (ROC-AUC = 0.891; accuracy = 81.0 %), although a sensitivity analysis excluding withdrawn enrolments produced a smaller association (OR = 7.036), indicating that withdrawal-related truncation explains part of the estimate. Success rates also varied from 35.2 % in the most deprived group to 57.5 % in the least deprived group and from 38.1 % among disabled learners to 48.2 % among non-disabled learners. Because the data are observational, institution-specific, and from 2013–2014, the results should be interpreted as historical associations rather than causal or universally contemporary effects. The findings support further prospective evaluation of learning-analytics systems, accessible course design, and equity-focused student support.

1.         UNESCO. Education in a Post-COVID World: Nine Ideas for Public Action. International Commission on the Futures of Education. UNESCO: Paris, France, 2020. Available online: https://unesdoc.unesco.org/ark:/48223/pf0000373717 (accessed on 10 February 2025).

2.         Tlili A, Shehata B, Adarkwah MA, et al. What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments. 2023; 10: 15. doi: 10.1186/s40561-023-00237-x

3.         Mayer RE. Multimedia Learning. Cambridge University Press: Cambridge, UK; 2020. doi: 10.1017/9781316941355

4.         Crompton H, Burke D. Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education. 2023; 20: 22. doi: 10.1186/s41239-023-00392-8

5.         Dutta S, Lanvin B. The Network Readiness Index 2023. Portulans Institute: Washington, DC, USA; 2023. Available online: https://networkreadinessindex.org/nri-2023/ (accessed on 15 January 2025).

6.         UNESCO GEM Report. Technology in Education: A Tool on Whose Terms? UNESCO: Paris, France; 2023. Available online: https://www.unesco.org/gem-report/en/2023 (accessed on 10 March 2025).

7.         World Bank. The Human Capital Index 2020 Update: Human Capital in the Time of COVID-19. World Bank: Washington, DC, USA; 2020. doi: 10.1596/34432

8.         Bates AW. Teaching in a Digital Age: Guidelines for Designing Teaching and Learning for a Digital World. Tony Bates Associates Ltd.: Vancouver, BC, Canada; 2022. Available online: https://teachonline.ca/teaching-in-a-digital-age (accessed on 10 March 2025).

9.         Ifenthaler D, Yau JY-K. Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development. 2020; 68(4): 1961–1990. doi: 10.1007/s11423-020-09788-z

10.      Hilliger I, Ortiz-Rojas M, Pesantez-Cabrera P, et al. Identifying needs for learning analytics adoption in Latin American universities: A mixed-methods approach. The Internet and Higher Education. 2022; 53: 100842. doi: 10.1016/j.iheduc.2020.100726

11.      United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development. United Nations: New York, NY, USA; 2015. Available online: https://sdgs.un.org/2030agenda (accessed on 1 January 2025).

12.      UNESCO. Education 2030: Incheon Declaration and Framework for Action for the Implementation of Sustainable Development Goal 4. UNESCO: Paris, France; 2015. Available online: https://unesdoc.unesco.org/ark:/48223/pf0000245656  (accessed on 1 January 2025).

13.      Leal Filho W, Lange Salvia A, Beynaghi A, et al. Digital transformation and sustainable development in higher education in a post-pandemic world. International Journal of Sustainable Development&World Ecology. 2024; 31(1): 108–123. doi: 10.1080/13504509.2023.2237933

14.      Starkweather J, Moske AK. Multinomial logistic regression. Benchmarks RSS. 2023. Available online: https://it.unt.edu/benchmarks (accessed on 5 April 2025).

15.      Williamson B, Eynon R, Potter J. Pandemic politics, pedagogies and practices: Digital technologies and distance education during the coronavirus emergency. Learning, media and technology. 2020; 45: 107–114. doi: 10.1080/17439884.2020.1761641

16.      Huang R, Liu D, Tlili A, et al. Handbook on facilitating flexible learning during educational disruption. Smart Learning Institute of Beijing Normal University/UNESCO: Beijing, China; 2020. Available online: https://iite.unesco.org/publications/handbook-on-facilitating-flexible-learning/ (accessed on 12 February 2025).

17.      Kuzilek J, Hlosta M, Zdrahal Z. Open University learning analytics dataset. Sciencetific Data. 2017; 4: 170171. doi: 10.1038/sdata.2017.171

18.      Hosmer DW, Lemeshow S, Sturdivant RX. Applied Logistic Regression. Wiley: Hoboken, NJ, USA; 2013. doi: 10.1002/9781118548387

19.      Committee on Publication Ethics (COPE). Core Practices. COPE: London, UK; 2022. Available online: https://publicationethics.org/core-practices  (accessed on 20 March 2025).

20.      UNESCO GEM Report. Education for People and Planet: Creating Sustainable Futures for All. UNESCO: Paris, France; 2022. Available online: https://www.unesco.org/gem-report/en/2022 (accessed on 15 January 2025).

21.      Rienties B, Olney T, Nichols M, Herodotou C. Effective usage of learning analytics: What do practitioners want and where should distance learning institutions be going? Open Learning. 2019; 35(2): 178–195. doi: 10.1080/02680513.2019.1690441

22.      Marginson S. Limitations of human capital theory. Studies in Higher Education. 2019; 44(3): 1–15. doi: 10.1080/03075079.2017.1359823

23.      Rienties B, Boroowa A, Cross S, et al. Reviewing three case-studies of learning analytics interventions at the Open University UK. The Internet and Higher Education. 2022; 55: 100875. doi:10.1016/j.iheduc.2022.100875

24.      OECD. Education at a Glance 2023: OECD Indicators. OECD Publishing: Paris, France; 2023. doi: 10.1787/e13bef63-en

25.      Tight M. Systematic reviews and meta-analyses of higher education research. European Journal of Higher Education. 2019; 9(2): 133–152. doi: 10.1080/21568235.2018.1541752

26.      Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate Data Analysis, 8th ed. Cengage Learning: Stamford, CT, USA; 2019.

27.      Zhao Y, Watterston J. The changes we need: education post COVID-19. Journal of Educational Change. 2021; 22: 3–12. doi: 10.1007/s10833-021-09417-3

28.      Ahshan R. A framework of implementing strategies for active student engagement in remote/online teaching and learning during the COVID-19 pandemic. Education Sciences. 2021; 11(9): 483. doi: 10.3390/educsci11090483

29.      Jawad K, Shah MA, Tahir M. Students’ academic performance and engagement prediction in a virtual learning environment using random forest with data balancing. Sustainability. 2022; 14(22): 14795. doi:10.3390/su142214795

30.      Sachs JD, Lafortune G, Fuller G, Drumm E. Implementing the SDG stimulus. Sustainable Development Report 2023. Dublin University Press: Dublin, Ireland; 2023. doi: 10.25546/102924

31.      Bañeres D, Rodríguez-González ME, Guerrero-Roldán AE, Cortadas P. An early warning system to identify and intervene online dropout learners. International Journal of Educational Technology in Higher Education. 2023; 20: 3. doi: 10.1186/s41239-022-00371-5

32.      Selwyn N. Education and Technology: Key Issues and Debates. London, UK: Bloomsbury Academic; 2022.

33.      Davis FD, Riedl R, Vom Brocke J, Léger PM, Randolph AB, Mueller B. Information systems and neuroscience: Gmunden Retreat on NeuroIS 2022. Information Systems and Neuroscience. 2023; 64: 1–138. doi: 10.1007/978-3-031-13064-9

34.      Schunk DH, DiBenedetto MK. Chapter four - self-efficacy and human motivation. Advances in Motivation Science. 2021; 8: 153–179. doi: 10.1016/bs.adms.2020.10.001

35.      Khalil M, Prinsloo P, Slade S. The use and application of learning theory in learning analytics: a scoping review. Journal of Computing in Higher Education. 2023; 35: 573–594. doi: 10.1007/s12528-022-09340-3

36.      Lameras P, Arnab S. Power to the teachers: An exploratory review on artificial intelligence in education. Information. 2022; 13(1): 14. doi: 10.3390/info13010014

37.      Kabudi T, Pappas I, Olsen DH. AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence. 2021; 2: 100017. doi: 10.1016/j.caeai.2021.100017

38.      Crompton H, Burke D. Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education. 2023; 20: 22. doi: 10.1186/s41239-023-00392-8