The diversity of contemporary online learning cohorts requires learning experiences that are designed for high engagement but are flexible and adaptive to the needs of autonomous learners. Adaptivity in learning design, online teaching and student support has been enhanced by technologies providing timely data on learners’ knowledge, perceptions and study behaviour. Alongside this, flexibility in the timing and mode of engagement with teachers, peers and learning content, and data driven feedback on study approaches can promote agile and personalised learning experiences. This element supports enhanced learner-content, learner-learner, learner-teacher and learner-institutional engagement.
Adaptivity in learning design, online teaching and student support has been enhanced by technologies providing timely data on learners’ knowledge, perceptions and study behaviour. The use of learning analytics and adaptive learning technologies can be key enabling elements for the provision of a tailored learning experience for students (Siemens & Long, 2011). Learning analytics enable personalised support from teachers and learning support staff which recognises students as individuals and ensures that problems encountered are headed off quickly. Another tool available to personalise the learning experience is the use of adaptive technologies that allow students to progress through a course at their own pace with quizzes and other online assessment techniques providing feedback and guidance to allow them to skip over material they have already mastered or engage more deeply with components where they need additional help (Irwin, Hepplestone, Holden, Parkin, & Thorpe, 2013).
Alongside this, flexibility in the timing and mode of engagement with teachers, peers and learning content, and data driven feedback on study approaches can promote agile and personalised learning experiences. Conole (2009) provides a sophisticated interpretation of personalised learning, citing the aspiration of a range of international governing bodies to move beyond a one-size-fits-all view of education. She highlights a focus on the Personal Learning Environments of individuals who learn through social engagement using a range of loosely coupled tools in their unique environments. She points to the changing educational context of open content and open courses and argues that in our increasingly connected society we need to capitalize on the affordances of all of the resources available to us to enable our learners to be part of a global, connected distributed intelligence (Conole, 2009, p. 3). This may eventually require a rethinking of course structures and credit and credentialing processes as we explore the affordances of strategies like the modularisation of content and badging of learning achievements (Gibson, Ostashewski, Flintoff, Grant, & Knight, 2013).
The Flexible and Adaptive Learning element is exemplified by:
- Subject and course design informed by data drawn from student and peer feedback, research and learning analytics.
- Data informed during session adaptation of teaching strategies and resources.
- Data informed recommendations for students to connect with university support services.
- Dashboards that provide feedback to students on their learning strategies.
- Flexible or adaptive lesson, subject or course designs providing individualised pathways based on demonstration of knowledge and competency.
- Flexibility in assessment providing opportunities for students to build on their specific discipline knowledge or professional expertise.
The TOL Learning Experience Framework, while encouraging designers to draw upon the OLM in a way which best meets the learning needs of the particular cohort, also recommends specific strategies to enact the Flexible and Adaptive Learning element, as follows:
- Using conventional study sessions and durations as the norm, provide support along with consistent and transparent processes for students to take up the option to accelerate, towards a specified early completion date (minimum 8 weeks), and changes to Special Consideration policy to enable students to formally request option to work beyond normal session end on a broader range of grounds. [NOTE: ongoing investigations/system work and/or market testing towards greater flexibility in next 3-5 years]
- Using recommended assessment dates as the norm, provide processes and support for students to schedule and renegotiate their planned assessment submission dates in response to their individual life circumstances.
- Shorter marking/feedback return cycles – particularly for early assessment tasks where comprehensive feedback promotes learning and successful progression through the subject. The norm will be a maximum of 5 days. Where more complex assessment items require longer turnaround times, this is clearly communicated in the subject outline.
- Assessment tasks designed to ensure that the integrity of the assessment process is maintained in the context of flexible submission and return of feedback.
Conole, G. (2009). Personalisation through technology-Enhanced learning. In J. O’Donoghue (Ed.), Technology-Supported Environments for Personalized Learning: Methods and Case Studies (pp. 1-15). London: IGI Global. doi:10.4018/978-1-60566-884-0.ch001
Gibson, D., Ostashewski, N., Flintoff, K., Grant, S., & Knight, E. (2013). Digital badges in education. Education and information technologies, 20(2), 403-410. doi:10.1007/s10639-013-9291-7
Irwin, B., Hepplestone, S., Holden, G., Parkin, H. J., & Thorpe, L. (2013). Engaging students with feedback through adaptive release. Innovations in Education & Teaching International, 50(1), 51-61. doi:10.1080/14703297.2012.748333
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), 30.
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