For our latest reading group, following Sian Bayne’s fascinating Near Future Teaching seminar for BILT, we wanted to look in more depth at the project materials and related reading.
Michael read ‘Using learning analytics to scale the provision of personalized feedback,’ a paper by Abelardo Pardo, Jelena Jovanovic, Shane Dawson, Dragan Gasevic and Negin Mirriahi. Responding to the need to be able to provide individual feedback to large classes of students, this study presented and tested a novel system for utilizing learning analytic data generated by student activity within a learning management system in order to deliver what the authors called ‘personalized’ feedback to students. As it was designed, the system allowed instructors to create small, one or two sentence pieces of feedback for each activity within a course. Based on these, each week students would be able to receive a set of ‘personalized’ feedback that responded to their level of participation. In the study, the authors found an improvement in student satisfaction with the feedback they received, but only a marginal improvement in performance, as compared to previous years. There were limits to the methodology — the study only made use of at most three years of student data for comparison — and the author’s definition of ‘personalized feedback’ seemed in practice to be little more than a kind of customized boilerplate feedback, but nevertheless the study did have a few interesting points. First, it was admirable in the way that it sought to use learning analytics techniques to improve feedback in large courses. Second, the authors took the well thought out step to not make the feedback given to be about the content of the course, but instead it focused on providing feedback on student study habits. That is, the feedback might encourage students to make sure they did all the reading that week if they weren’t doing well, or might encourage them to be sure to review the material if they had already reviewed it all once. Third, the article offered an interesting recounting of the history of the concept of feedback as it moved from focusing only on addressing the gap between targets and actual performance to a more wholistic and continuous relationship between mentor and student.
Suzi read Higher education, unbundling, and the end of the university as we know it by Tristran McCowan. This paper starts with a thorough guide to the language of unbundling and the kinds of things that we talk about when we talk about unbundling, followed by an extensive discussion of what this means for higher education. My impression from the article was that “unbundling” may be slightly unhelpful terminology, partly because it covers a very wide range of things, and partly because – if the article is to be believed – it’s a fairly neutral term for activities which seem to include asset-stripping and declawing universities. As an exploration of the (possible) changing face of universities it’s well worth a read. You can decide for yourself whether students are better off buying an album than creating their own educational mixtape.
Roger read “Future practices”. For world 1 , human led and closed, I was concerned that lots was only available to “higher paying students” and there was no mention at all of collaborative learning. For world 2, human led and open, I liked the the idea of the new field of “compassion analytics”, which would be good to explore further, lots of challenge based learning and open content. World 3, tech led and closed, was appealing in its emphasis on wellbeing in relation to technology, and a move away from traditional assessment, with failure recognised more as an opportunity to learn, and reflection and the ability to analyse and synthesise prioritised. From world 4 I liked the emphasis on lifelong learning and individual flexibility for students eg to choose their own blocks of learning.
Chrysanthi read Future Teaching trends: Science and Technology. The review analyzes 5 trends:
- datafication – e.g. monitoring students’ attendance, location, engagement, real-time attention levels,
- artificial intelligence – e.g. AI tutoring, giving feedback, summarizing discussions and scanning for misconceptions, identifying human emotions and generating its own responses rather than relying only on past experience and data,
- neuroscience and cognitive enhancement – e.g. brain-computer interfaces, enhancement tools like tech that sends currents to the brain to help with reading and memory or drugs that improve creativity and motivation,
- virtual and augmented realities – e.g. that help to acquire medical skills for high-risk scenarios without real risk, or explore life as someone else to develop empathy, and
- new forms of value – enabling e.g. the recording and verification of all educational achievements and accumulation of credit over one’s lifetime, or the creation of direct contracts between student-academic.
I liked it because it gave both pros and cons in a concise way. It allows you to understand why these trends would be useful and could be adopted widely, at the same time as you are getting a glimpse of the dystopian learning environment they could create if used before ethical and other implications have been considered.
- Future Teaching trends: Science and Technology
- Future Teaching Trends: Education and Society
- NFT project methods: community scoping and crafting worlds
- NFT project methods: values within worlds
- Future universities
- Future practices
- Following up references from the Science and Technology and Education and Society reports, including:
- Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., & Mirriahi, N. (2017). Using learning analytics to scale the provision of personalised feedback. British Journal of Educational Technology.
- McCowan, T. (2017). Higher education, unbundling, and the end of the university as we know it. Oxford Review of Education, 43(6), 733-748.