Personalised learning is premised on the recognition of the diversity and differentiation of learners, with the aim of fostering autonomy for learners to shape individualised learning journeys (Li & Wong, 2021). Upon closer inspection, however, personalised learning remains broad, subjective and elusive to define, and the student archetype as inherently self-regulated is problematic.
Online learning must contend with how to deliver on two competing premises: to make learning more individualised while also allowing for learning at scale. Massive open online courses (MOOCs) provide a seemingly limitless menu of study options to fulfil each learner’s personalised learning plan, address skills gaps and remove barriers to entry through ease of enrolment and free options. Multiple pathways to learning – where the delineation between informal and formal learning blurs – is one of the biggest selling points for education technology.
Yet, the abundance of choice facilitated by technology mounts increased pressure on the learner to discern their own best journey among the endless and exponentially growing possibilities; the proliferation of formal and informal qualifications increases the complexity of choice and increases pressure on learners to discern an individual learning path (de Freitas, 2005).
Self-regulated learners – who have the ability to identify goals and to proactively investigate clear pathways to reach those goals (Zimmerman & Schunk, 2011) – are best placed to navigate massification of choice. However, not all learners come readily equipped to formulate their own pathways. At course level, personalisation does not translate into the removal of limits. Instructors play a pivotal role in identifying when and how to remove parameters for their students through scaffolding and through increased reliance on data.
A significant subsection of research focuses on data-driven facilitation of personalisation in online learning (Nguyen et al., 2021). Learning analytics is a widely recognised and increasingly implemented tool for capturing student data to inform pedagogy and to create adaptive and individualised learning interventions, but research reveals issues around lack of consensus in its interpretation (Greller & Drachsler, 2012). Ill-informed decisions on student data could lead to prejudicial practices – such as where learner demographics are collected in advance in order to segment at-risk learners. This is just one possible harmful consequence of the datafication of learners (Tsai et al., 2019).
‘Ill-informed decisions on student data could lead to prejudicial practices – such as where learner demographics are collected in advance in order to segment at-risk learners.’
The use of analytics coalesces with a different composition of teaching strategies for online instructors, including an emphasis on timely and targeted feedback to evidence instructor presence (Anderson et al., 2001; Richardson et al., 2015). Technology provides multiple methods of feedback at both formative and summative levels, facilitating more frequent touchpoints between students and lecturers. In this way, personalisation can foster a dynamic and productive relationship between the learner and teacher.
Although analytics may be viewed as a natural affordance of technology-enhanced learning, the link between learning analytics and learning theory is not well evidenced (Gašević et al., 2015). Analytics supply instructors with potentially rich data but with no clear rubric for interpretation. Without a clear understanding of how to extract and analyse data, particularly large amounts of data, educators face challenges with providing effective and personalised feedback at scale.
Positioning personalisation within these debates remains contentious, but it is crucial as new technology and the proliferation of delivery models reconfigures the role of both the learner and the educator, and even the definition of education itself. Tensions persist around the ethical and pedagogical implications of applying data, and the recognition that learner agency does not always translate into complete self-regulation.
Anderson, T., Rourke, L., Garrison, D. R., & Archer, W. (2001). Assessing teacher presence in a computer conferencing context. Journal of Asynchronous Learning Networks, 5(2), 1–17. http://dx.doi.org/10.24059/olj.v5i2.1875
de Freitas, S. (2005) The paradox of choice and personalization. In S. de Freitas & C. Yapp (Eds.), Personalizing Learning in the 21st Century (pp. 13–16). Stafford: Network Educational Press.
Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x
Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Journal of Educational Technology & Society, 15(3), 42–57. http://www.jstor.org/stable/jeductechsoci.15.3.42
Li, K. C., & Wong, B. T. M. (2021). Features and trends of personalised learning: A review of journal publications from 2001 to 2018. Interactive Learning Environments, 29(2), 182–195. https://doi.org/10.1080/10494820.2020.1811735
Nguyen, A., Tuunanen, T., Gardner, L., & Sheridan, D. (2021). Design principles for learning analytics information systems in higher education. European Journal of Information Systems, 30(5), 541–568. https://doi.org/10.1080/0960085X.2020.1816144
Richardson, J. C., Koehler, A. A., Besser, E. D., Caskurlu, S., Lim, J., & Mueller, C. M. (2015). Conceptualizing and investigating instructor presence in online learning environments. The International Review of Research in Open and Distributed Learning, 16(3). https://doi.org/10.19173/irrodl.v16i3.2123
Tsai, Y. S., Perrotta, C., & Gašević, D. (2019). Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment & Evaluation in Higher Education, 45(4), 554–567. https://doi.org/10.1080/02602938.2019.1676396
Zimmerman, B. J., & Schunk, D. H. (2011). Self-regulated learning and performance: An introduction and an overview. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 1–12). Routledge.