This week I wanted to get away from a very linear approach to data visualisation by creating an image which encapsulated a more networked approach in an attempt to capture the messier nature of data flows. I fear the image is somewhat messier than I hoped. But perhaps this serves to illustrate the point. I followed the approach used by David McCandless in using images that convey the subject matter and altering the size according to the magnitude of each variable (2012). In this case I wanted to measure, over the week, just how personalised or student-centered learning analytics data were. Reading through the articles on personalised learning (Tsai and Gašević 2020, Friesen 2019, Thompson and Cook 2017, Bulger 2016) I was struck by that although these articles engage with a critical debate about personalised learning, the data at my disposal does not claim to create individualised learning assessments. I am currently teaching 109 post-graduate business students using a simulation game. The simulation platform (Cesim) collects data about time on the platform as does the accompanying Moodle site. The students also communicate on MS Teams. Additionally I receive emails and talk to students on Zoom. My analysis shows how each of these platforms are connected and uses a traffic light system to indicate how personalised these platforms are. Unsurprisingly, the face to face Zoom calls are the only opportunity to provide personalised feedback.
References
Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society working paper
Friesen, N. 2019. “The technological imaginary in education, or: Myth and enlightenment in ‘Personalised Learning.” In M. Stocchetti (Ed.), The digital age and its discontents. University of Helsinki Press.
McCandless, D., 2012. Information is beautiful (pp. 978-0007294664). London: Collins.
Thompson, G. and Cook, I. 2017. The logic of data-sense: thinking through learning personalisation. Discourse: Studies in the Cultural Politics of Education. 38(5), pp. 740-754
Tsai, Y-S. Perrotta, C. & Gašević, D. 2020. 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
I like your attempt to represent the networked, messy nature of data flows, and the intensities of personalization across each platform. As platforms are increasingly joined up, e.g. through APIs or single sign-ons, it’s likely that learning- or learner-related data will get even messier and hard to access unless you possess significant technical expertise and the relevant equipment.
It’s interesting of course that genuinely personalized feedback and interaction remains confined to in-person (albeit online) communication. I keep hearing that simulations with a virtual or augmented reality component are booming in enterprise education, with some AI-type applications for personalized or adaptive feedback built-in. This would be a really interesting area of development to explore further, as it suggests very different forms of simulated learning experiences than students might encounter in an educational institution such as a university or school.
Thanks Ben, our simulation does not include any feedback beyond the results of the simulation. The students run a high tech international company. Each week they lock in financial decisions and the simulation returns the company performance and that of its competitors. This is fairly cold as feedback goes, but you could say no more so than actual business. I find the simulations are quite addictive and immersive, better than lectures and knowledge “banking” in many respects. However, they can be overwhelming for students to, so they do need a reasonable amount of support.