#LTHEchat 225: Supporting and Humanising Behavioural Change without Behaviourism – Nudges & Digital Footprints. Led by @ameenalpayne @mart_compton @soapykennedy on 2nd Feb, 8pm GMT

3D rendered cubes in a holographic wave pattern – Nathan Watson via Unsplash

Hosted by an early career researcher, educator and incoming doctoral student (Payne), a disabled, undergraduate psychology student (Kennedy) and senior educator (Compton), our collaborative tweet chat aims to explore how behavioural change in online, higher education can be supported without behaviouristic approaches. Specifically, we will engage in discussion on how nudges and digital footprints may be deployed effectively to empower marginalised students – and the potential pitfalls of such data-driven pedagogy.

When students engage in online learning, they leave behind digital footprints, artefacts that trace their activities such as contributions, page views and communications. Digital learning management systems (LMS) generate data from these footprints that can provide insight into student progress and engagement as it relates to student success. These data are called learner analytics (LAs). LAs encompass the broad data mining, collection, analysis, and sharing/reporting/disseminating of students’ digital footprints. LAs are shaping the role of online instruction and student self-regulated learning by promoting ‘actionable intelligence’ (Bayne et al., 2020, p. 71), allowing instructors to orient students and empowering students to orient themselves. 

The growing adoption and interest in LAs has supported a strategic commitment to transparency regarding key drivers for improved student engagement, retention and success. At the same time, concerns are increasingly voiced around the extent to which students are informed about, supported (or hindered by), and tracked and surveilled as they engage online. It is important to acknowledge that making pedagogical conclusions based on delimited dimensions creates a context for stereotyping and discrimination, and profiling can result in hindering students’ potential and may hurt self-efficacy.

Nudge theory, coined by behaviour economist Richard Thaler, connects persuasion with design principles (Thaler, 2015). A nudge is an approach that focuses not on punishment and reward (behaviourism) but encourages positive choices and decisions – fundamental is understanding the context.

We’d like to share a few assumptions as we engage in this discussion:

  • Academic staff have a responsibility to support our increasingly diverse body of students and need to be open to new tools and techniques such as data generated by our students’ digital footprints and opportunities offered by behavioural psychology.
  • Achievement differentials and attainment gaps exist for marginalised students. Disabled students, or students with executive dysfunction, may struggle with skills vital to independent study and content learning e.g., initiation, planning, organisation, etc. For disabled students, a product of being under-served by higher education institutions (HEIs) is that they often demonstrate lower levels of engagement which leads to disproportionate completion rates and, subsequently, employment rates and other outcomes. 
  • Behaviouristic approaches (rewards and sanctions) are at the heart of much of what we still do in education but there have been movements and trends challenging manifestations of this – from banning of corporal punishment in schools to rapid growth in interest in ungrading. 
  • LMS data are not indicators of students’ potential and merit. LAs are not impartial; they are creations of human design. By giving a voice to the data, we’re defining their meaning through our interpretations.

It is valuable to build in periodic or persistent nudges of and toward ‘both the goal and its value’ to empower all students to sustain their efforts (CAST, 2018). We advocate the implementation of nudges as something that can be useful for everyone using an LMS, as compared to a tool aimed directly at disabled students, who may feel singled out. We hold that nudging is less of an evolution of behaviourism but more of a challenge to its ubiquity and all the common assumptions about its effectiveness. We propose the employment of empathy, human connection (in contrast with carrot and stick approaches of education) and understanding to help effect small changes in student’s learning / behaviour through supportive nudges. Nudging, prompted by LAs, is one way to approach improving achievement, narrowing gaps and offering connection and support for all students.

Join Us 🕗

The live tweet-chat will take place via https://twitter.com/LTHEchat on Wednesday 2nd February 2022, 8:00 to 9:00 pm GMT. During this time 6 questions will be posed (one every 10 minutes) – everyone is welcome to contribute (as much or as little as they like) or just to read. Conversation is also welcomed at any time post 9:00pm GMT. A compilation Wakelet capturing the conversations is below.

Questions

Q1 If nudging students is less about coercive practises (punishments and rewards) and more about soft, small-step connections towards positive change, what examples can you offer from practice?

Q2 What role does/could learning analytics play in shaping our in-course interactions with students, particularly those from marginalised groups? 

Q3 Learning analytics (LAs) risks profiling students and driving inequality. How might we address these weaknesses (such as the cognitive biases we may bring to its interpretation and/or some students being advantaged by extra guidance)?

Q4 What role might nudging and/or learning analytics play in personalising/adaptive learning?

Q5 Regarding the complex issues in the nexus of student agency & subjectivity, privacy, consent, & vulnerability, how might we differentiate between learning analytics & surveillance in online HE?

Q6 Can nudges assist students in overcoming ‘learned helplessness’? If so, how might nudges support students in taking control of their educational experiences?

Wakelet

Here’s the link to the Wakelet of this TweetChat: https://wke.lt/w/s/bZ3xuW

References and Further Reading 📗 

Bayne, S., Evans, P., Ewins, R., Knox, J., Lamb, J., Mcleod, H., et al. (2020). The Manifesto for Teaching Online. Cambridge, MA: MIT Press.

CAST (2018). Universal Design for Learning Guidelines version 2.2. http://udlguidelines.cast.org

Commissioner for Fair Access. (2019). Disabled students at university: discussion paper. Scottish Government. Available at: https://www.gov.scot/publications/commissioner-fair-access-discussion-paper-disabled-students-university/

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71. https://link.springer.com/content/pdf/10.1007/s11528-014-0822-x.pdf 

Lim, L. A., Gentili, S., Pardo, A., Kovanović, V., Whitelock-Wainwright, A., Gašević, D., & Dawson, S. (2021). What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course. Learning and Instruction, 72, 101202.

Payne, A. L., Compton, M. & Kennedy, S. (In Progress). ‘Supporting and humanising behavioural change without the behaviorism: nudges and digital footprints.’ Human Data Interaction, Disadvantage and Skills in the Community: Enabling Cross-Sector Environments For Postdigital Inclusion. Springer.

Prinsloo, P. (2016). “Decolonising the Collection, Analyses and Use of Student Data: A Tentative Exploration/Proposal.” Open Distance Teaching and Learning (blog). https://opendistanceteachingandlearning.wordpress.com/2016/11/14/decolonising-the-collection-analyses-and-use-of-student-data-a-tentative-explorationproposal/.

Prinsloo, P., & Slade, S.(2015). Student privacy self-management: implications for learning analytics. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (LAK ’15). Association for Computing Machinery, New York, NY, USA, 83–92. https://doi.org/10.1145/2723576.2723585

Prinsloo, P., & Slade, S. (2016). Student Vulnerability, Agency and Learning Analytics: An Exploration. Journal of Learning Analytics, 3(1), 159–182. https://doi.org/10.18608/jla.2016.31.10

Roberts, L. D., Howell, J. A., Seaman, K., & Gibson, D. C. (2016). Student Attitudes toward Learning Analytics in Higher Education: “The Fitbit Version of the Learning World”. Frontiers in psychology, 7, 1959. https://doi.org/10.3389/fpsyg.2016.01959

Thaler, R. (2015). The Power of Nudges, for Good and Bad. The New York Times. Available at: https://faculty.chicagobooth.edu/-/media/faculty/richard-thaler/assets/files/goodandbad.pdf

Weijers, R.J., de Koning, B.B. & Paas, F. Nudging in education: from theory towards guidelines for successful implementation. Eur J Psychol Educ 36, 883–902 (2021). https://doi.org/10.1007/s10212-020-00495-0 

Your #LTHEchat 225 Hosts Profiles 📷 

Ameena Payne

Ameena Payne works as an educator in the social sciences and business. She has earned a Master of Education, Graduate Certificate in Learning and Teaching (Higher Education), Bachelor of Science in Business Administration and a Certificate IV in Training & Assessment. She is a Fellow of Advance Higher Education Academy (AdvanceHE) and the Higher Education Research and Development Society of Australasia (HERDSA).

Dr Martin Compton

Martin Compton is an Associate Professor working in the Arena Centre for Research-Based Education at University College London. He worked for many years on access programmes in further education colleges, teaching History and English amongst many other things, some of which he still finds barely credible. For the last 20 years or so he has worked primarily in teacher and academic development including 5 years running an online PGCert HE. His current remit includes a specific digital education brief but prior to this his relationship with technology was very much one of an inquisitive innovating and experimenting practitioner. His research interests and publications include overt consideration of the affordances of and impediments to successful digital education as well as tangential investigations in the contexts of transnational education and the observation of teaching and learning.

Sophie Kennedy

Sophie Kennedy is a Scottish student working towards a BSc (Hons) in Psychology & Counselling. She also works as a student consultant for Abertay University’s Learning Enhancement Academy and was elected the Abertay Student Association’s Disability Officer from 20-21. Her research interests include improving the accessibility of education for disabled and neurodivergent students and researching alternative definitions of student success. 

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