Learning when we are not there: designing for self-directed learning

Our students’ circumstances have changed considerably, with formal and informal learning now merging and continuing through networks, platforms, and conversations that form what might be called the “other university”—one that is digital, distributed, and deeply dependent on learner agency, skills, and motivation. Alongside this sits a third, more personal digital space of learning, filled with platforms, practices, and experiences that formal education approaches with both curiosity and caution—Generative AI being the latest example. This shift demands new degree models that reflect distributed study and enable students to move flexibly between digital, hybrid, and on-campus learning (Mosley, 2025). And to design for this reality, we must re-examine not only what we teach but how and when students learn.

The paradox of independent learning

Learning science shows that independent learning without guidance is rarely successful (Kirschner & Hendrick, 2020)—a reality sharpened by Generative AI. Students' progress from novice to expert when design scaffolds reflection, metacognition, and gradual responsibility. Hence, the value of graduate attributes, learning outcomes, and frameworks such as the JISC Digital Capabilities Framework (JISC, 2023) as supports for independence.

Universities have attempted to formalise this scaffolding through graduate attributes, notional learning hours and “blended learning”—a term that, as Mosley (2025) notes, now carries almost empty meaning—but the goal remains the same: guided independence. Without clear design, asynchronous study fragments, and students default to weak strategies such as plagiarism, cramming or passive rereading instead of evidence-based ones like distributed practice, elaboration, and collaboration (Kirschner & Hendrick, 2020).

Asynchronous learning is then a looser, less visible space where success hinges on planning, monitoring, and evaluating progress—alongside values like academic integrity. These skills are central to the digital-age curriculum and must be intentionally supported.

What makes a successful online learner?

Research into online learner readiness (Joosten & Cusatis, 2020) shows that the strongest predictor of success is online learning efficacy—belief in one’s ability to learn effectively in digital environments. Other key factors include online work skills and socialisation—the capacity to collaborate through digital tools—which shape both satisfaction and achievement. When interaction is poorly structured, socialisation can lower satisfaction, underscoring the need for purposeful design. Although Joosten and Cusatis did not measure persistence directly, their findings suggest that structured interaction fosters the connectedness that sustains learning online.

Similarly, Rivera (2018) highlights that successful online learners are self-regulated: they plan, monitor, and evaluate their progress. They are disciplined, goal-oriented, and confident in using digital tools—demonstrating strong computer and Internet self-efficacy. These traits can be nurtured through clear expectations, opportunities for practice, and visible instructor presence, which build autonomy and confidence.

Taken together, these studies show that effective online learning depends less on technical mastery than on self-regulation, reflection, and collaboration. Successful learners are organised, adaptable, and resilient, engaging peers and instructors through social and cognitive presence. Educators must design for these dispositions through structure, feedback, and a balance between independence and connection.

Designing for agency and belonging

How, then, do we design learning that develops these skills?

If you want to explore digital approaches, start with timetabling. As Norberg, Dziuban, and Moskal (2011) suggest, time—not space—is the fundamental measure and capital of learning. Learning happens whenever learners, educators, and activities meet, so an effective blended approach requires time to be the primary design dimension. It is not enough to digitise materials or offer flexible access; curricula must be intentionally organised, with clear sequencing of synchronous and asynchronous activities, balanced workload planning, and alignment between study effort and assessment. Müller and Mildenberger (2021) show that when classroom hours are replaced with online components, outcomes remain equivalent only when time and effort are redistributed. Likewise, Mubango and Ngirande (2023) highlight that students’ time-management skills can only develop within structures that make expectations visible. Timetabling is therefore a pedagogical act—shaping continuity and belonging between campus and digital spaces. The virtual environment should mirror the coherence of a well-planned timetable, carrying the same sense of authority, community, and presence (Garrison, Anderson & Archer, 2000).

Secondly, by making learning design intentional, explicit, and transparent. 

Students should see why activities exist, how they link to outcomes, and how responsibility gradually shifts from educator to learner. This transparency allows them to “reverse-engineer” effective learning approaches and transfer them to new contexts. Sharing learning outcomes, connecting them with goals, and promoting feedback literacy are practical ways of achieving this.

Thirdly, embedding opportunities for reflection, feedback, and social connection (Garrison, Anderson & Archer, 2000). 

Presence—social, cognitive, and teaching—is critical. Students thrive when they feel their educator is there in the course: through timely communication, consistent tone, and responsiveness. A visible teaching presence humanises the environment and builds shared purpose.

Fourthly, by scaffolding the transition from formal to informal learning spaces. 

The Virtual Learning Environment should be a launchpad into students’ Personal Learning Environments—their networks of tools, communities, and practices (Dabbagh & Kitsantas, 2012). When students use Generative AI, citation managers, social media, collaborative documents, or reflective portfolios, they are constructing personal infrastructures for lifelong learning.

Finally, by designing for social learning.

Discussion, iteration, and elaboration transform asynchronous forums into spaces of shared meaning. These are not merely social interactions but metacognitive spaces where students articulate and refine understanding together.

Learning design as a bridge

Intentional learning design is the bridge between ambition and lived experience and connects learning outcomes to learner realities. By designing courses that scaffold independence, promote metacognition, and nurture community, we do more than teach content—we cultivate capability.

As learning continues across digital and physical spaces, our task is to ensure students enter that world with the skills, confidence, and curiosity to learn well when we are not there.

 

References

Dabbagh, N. and Kitsantas, A. (2012). Personal Learning Environments, social media, and self-regulated learning: A natural formula for connecting formal and informal learning. The Internet and Higher Education, [online] 15(1), pp.3–8. doi:https://doi.org/10.1016/j.iheduc.2011.06.002.

Garrison, D.R., Anderson, T. and Archer, W. (2000) ‘Critical inquiry in a text-based environment: Computer conferencing in higher education’, The Internet and Higher Education, 2(2–3), pp. 87–105.

Jisc (2023) Digital capabilities framework. Available at: https://www.jisc.ac.uk/rd/projects/building-digital-capability (Accessed: 15 October 2025).

Jisc (2024) Beyond blended: rethinking curriculum and learning design. Available at: https://www.jisc.ac.uk/reports/beyond-blended (Accessed: 15 October 2025).

Joosten, T. & Cusatis, R. (2020) ‘Online Learning Readiness Scale (OLR): Implications for student success and satisfaction’, Online Learning, 24(1), pp. 59–75.

Kirschner, P.A. and Hendrick, C. (2020) How learning happens: Seminal works in educational psychology. London: Routledge.

Mosley, N. (2025) ‘Is it time for the UK to develop split-mode undergraduate degrees?’, Neil Mosley Consulting, 8 October. Available at: https://www.neilmosley.com/blog/is-it-time-for-the-uk-to-develop-split-mode-undergraduate-degrees (Accessed: 15 October 2025).

Mubango, H. and Ngirande, H.N. (2023) ‘Self-regulated learning: Time management in a blended learning environment for student academic performance’, in Self-regulated learning – insights and innovations. London: IntechOpen. doi: 10.5772/intechopen.1006068

Müller, C. and Mildenberger, T. (2021) ‘Facilitating flexible learning by replacing classroom time with an online learning environment: A systematic review of blended learning in higher education’, Educational Research Review, 34, Article 100394. doi: https://10.1016/j.edurev.2021.100394

Norberg, A., Dziuban, C. and Moskal, P.D. (2011) ‘A time-based blended learning model’, On the Horizon, 19(3), pp. 207–216. doi: https://10.1108/10748121111163913

Rivera, J.H. (2018) ‘Online learner readiness: Strategies for success’, Kappa Delta Pi Record, 54(2), pp. 52–55. doi: https://10.1080/00228958.2018.1443679