From Content Transmission to Deep Learning: Successful Teaching Strategies in Higher Education a Structured Integrative Review of the Evidence and a Proposed Pedagogical Model for University Teaching

Higher education faces a growing tension between the expansion of knowledge, the increasing diversity of the student population, digitalisation, and the persistence of teaching models centred primarily on lectures. This paper examines which teaching strategies have sufficient evidence to be considered successful and under what conditions they promote deep, durable, self-regulated, inclusive, and transferable learning. A structured integrative review with a scoping-review orientation was conducted, drawing on meta-analyses, systematic reviews, influential empirical studies, and documents issued by international organisations that were available through open access or verifiable bibliographic records. Sources were organised according to study design, quality of evidence, outcomes examined, and implementation conditions. The findings show consistent support for active learning, retrieval practice, and spacing; favourable but more design-dependent evidence for problem-based learning, formative assessment, feedback, and the teaching of self-regulation; and a still-emerging evidence base for Universal Design for Learning and generative artificial intelligence when evaluated in terms of causal effects on learning. The paper argues that success does not reside in any single methodology, but in the articulation of five mechanisms: activation of prior knowledge, cognitive elaboration, distributed practice, action-oriented feedback, and transfer. An integrative model for designing university learning experiences is proposed, and its implications for academic development, curriculum, assessment, and the institutional governance of innovation are discussed.

: Higher Education; Teaching Strategies; Active Learning; Formative Assessment; Self-Regulated Learning; Artificial Intelligence; Integrative Review

1. Agarwal, P. K., Nunes, L. D., & Blunt, J. R. (2021). Retrieval practice consistently benefits student learning: A systematic review of applied research in schools and classrooms. Educational Psychology Review, 33, 1409-1453. https://doi.org/10.1007/s10648-021-09595-9 [Google Scholar] [Crossref]

2. Arksey, H., & O'Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19-32. https://doi.org/10.1080/1364557032000119616 [Google Scholar] [Crossref]

3. Biggs, J., & Tang, C. (2011). Teaching for quality learning at university (4th ed.). Open University Press. [Google Scholar] [Crossref]

4. Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education, 5(1), 7-74. https://doi.org/10.1080/0969595980050102 [Google Scholar] [Crossref]

5. Boud, D., & Molloy, E. (2013). Rethinking models of feedback for learning: The challenge of design. Assessment & Evaluation in Higher Education, 38(6), 698-712. https://doi.org/10.1080/02602938.2012.691462 [Google Scholar] [Crossref]

6. CAST. (2024). Universal Design for Learning Guidelines version 3.0. https://udlguidelines.cast.org [Google Scholar] [Crossref]

7. Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219-243. https://doi.org/10.1080/00461520.2014.965823 [Google Scholar] [Crossref]

8. Dochy, F., Segers, M., Van den Bossche, P., & Gijbels, D. (2003). Effects of problem-based learning: A meta-analysis. Learning and Instruction, 13(5), 533-568. https://doi.org/10.1016/S0959-4752(02)00025-7 [Google Scholar] [Crossref]

9. Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410-8415. https://doi.org/10.1073/pnas.1319030111 [Google Scholar] [Crossref]

10. Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81-112. https://doi.org/10.3102/003465430298487 [Google Scholar] [Crossref]

11. Hoidn, S., & Kärkkäinen, K. (2014). Promoting skills for innovation in higher education: A literature review on the effectiveness of problem-based learning and of teaching behaviours. OECD Education Working Papers, No. 100. https://doi.org/10.1787/5k3tsj67l226-en [Google Scholar] [Crossref]

12. Lan, M., & Zhou, X. (2025). A qualitative systematic review on AI empowered self-regulated learning in higher education. npj Science of Learning, 10, 21. https://doi.org/10.1038/s41539-025-00319-0 [Google Scholar] [Crossref]

13. Levac, D., Colquhoun, H., & O'Brien, K. K. (2010). Scoping studies: Advancing the methodology. Implementation Science, 5, 69. https://doi.org/10.1186/1748-5908-5-69 [Google Scholar] [Crossref]

14. Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535 [Google Scholar] [Crossref]

15. Morris, R., Perry, T., & Wardle, L. (2021). Formative assessment and feedback for learning in higher education: A systematic review. Review of Education, 9(3), e3292. https://doi.org/10.1002/rev3.3292 [Google Scholar] [Crossref]

16. Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199-218. https://doi.org/10.1080/03075070600572090 [Google Scholar] [Crossref]

17. OECD. (2010). Learning our lesson: Review of quality teaching in higher education. OECD Publishing. https://doi.org/10.1787/9789264079281-en [Google Scholar] [Crossref]

18. Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 422. https://doi.org/10.3389/fpsyg.2017.00422 [Google Scholar] [Crossref]

19. Peters, M. D. J., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., McInerney, P., Godfrey, C. M., & Khalil, H. (2020). Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Synthesis, 18(10), 2119-2126. https://doi.org/10.11124/JBIES-20-00167 [Google Scholar] [Crossref]

20. Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93(3), 223-231. https://doi.org/10.1002/j.2168-9830.2004.tb00809.x [Google Scholar] [Crossref]

21. Roediger, H. L., III, & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249-255. https://doi.org/10.1111/j.1467-9280.2006.01693.x [Google Scholar] [Crossref]

22. Theobald, E. J., Hill, M. J., Tran, E., Agrawal, S., Arroyo, E. N., Behling, S., Chambwe, N., Cintrón, D. L., Cooper, J. D., Dunster, G., Grummer, J. A., Hennessey, K., Hsiao, J., Iranon, N., Jones, L., II, Jordt, H., Keller, M., Lacey, M. E., Littlefield, C. E., ... Freeman, S. (2020). Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math. Proceedings of the National Academy of Sciences, 117(12), 6476-6483. https://doi.org/10.1073/pnas.1916903117 [Google Scholar] [Crossref]

23. Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., ... Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467-473. https://doi.org/10.7326/M18-0850 [Google Scholar] [Crossref]

24. Trumble, E., Lodge, J. M., Mandrusiak, A., & Forbes, R. (2024). Systematic review of distributed practice and retrieval practice in health professions education. Advances in Health Sciences Education, 29, 689-714. https://doi.org/10.1007/s10459-023-10274-3 [Google Scholar] [Crossref]

25. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70. https://doi.org/10.1207/s15430421tip4102_2 [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet