At the start of this year the UK Higher Education Academy (HEA) released its report ‘[from bricks to clicks]1‘ which included (on p.7, copied below) a set of recommendations. Some of these are pretty UK specific (there may be Australian comparisons), but others are more general. When it was released I thought it might be interesting for us to consider how UTS was addressing the recommendations (particularly as, per recommendation 8 and 9, my department falls within a strategic investment into university data adoption). Actually looking through, the bullets, I did have immediate responses to each, but there are key recommendations I think are more interesting, and that I think would need implementing as pre-requisites to other recommendations (e.g., data literacy should be built before any use of data in accountability structures). I might come back to this. * * * * Recommendation 1 HESA, Jisc and Universities UK should work together to develop a sector-wide strategy for excellent and innovative data management. This strategy will support and enable sharing and collaboration between institutions. There’s a research data infrastructure strategy here [https://docs.education.gov.au/system/files/doc/other/the_australian_research_data_infrastructure_strategy.pdf]2 With some surveys and reviews of institutional strategies (but no shared strategy I think) published in the last year and * Recommendation 2 HESA should take responsibility for rationalising the data collection process across the sector, working in partnership with others. It’s not actually clear what’s intended by this in the original document. If the intent is that a central organisation should gather data, that seems a little absurd (beyond the data HESA already gathers), I suspect it’s just that an organisation should support HEIs in rationalising their data collection (in which case, see above). * Recommendation 3 All HEIs should consider introducing an appropriate learning analytics system to improve student support / performance at their institution. Any such decision should be fully informed by an analysis of the benefits, limitations and risks attached. This particularly focuses on large scale systems, presumably focussing on those around attendance and drop out rates. My view is that, instead, HEIs should consider introducing appropriate learning analytic systems, or moreover, empowering staff to introduce them, and integrate them with their current and developing teaching and learning strategies. That is exactly what we’re trying to implement at UTS in our human-centred approach to learning analytics (and data more broadly). * Recommendation 4 Institutions should put in place clear ethical policies and codes of practices that govern the use of student data in analytics and other digital systems. These policies should, at a minimum, address student privacy, security of data and consent. At UTS the university is developing a set of Data Governance tools, to guide the use of all our data (including student data). As established tools are being used in new ways (with old log data suddenly taking on new meaning), we will need to revise how we think about data security and consent. * Recommendation 5 In particular, when introducing learning analytics, HEIs should seek fully informed consent from students to the use of their personal and learning data in analytics. This should be sought again if new data is incorporated into the system, or existing data is used in new ways. This one is an interesting one, and the nature of ‘fully informed consent’ is complex. There’s been a bit written about this, and I collated some of that in a [blog post here]3. * Recommendation 6 Learning analytics should be driven by improvement of learning and teaching processes and student engagement. At the present early stage of maturity in learning analytics, we recommend that learning analytics is used for formative purposes, not summative purposes. I totally agree with this, although I think it is potentially in conflict with recommendation 3 – where 3 implies imposed systems regarding retention models, while 6 implies development of analytic tools at the fine-grain pedagogic level * Recommendation 7 Many internal and external systems rely upon good quality data being in place. Institutions should immediately review their internal data management approaches and put in place action to ensure that their data is fit for purpose. This may include development of a roadmap for cleaning up data. Many universities are undertaking this (including, as far as I’m aware, us) – one of the key considerations here, and I know it’s something UTS and other HEIs are grappling with, is working out what data is available and what might be done with it * Recommendation 8 To be equipped for the future of higher education, institutions should ensure that digital literacy, capability and good data management strategies are an integral part of their strategic plans. [Data is a key strategic thread at UTS]4, with research strengths and teaching (e.g., our Masters in Data Science and Innovation, and the undergraduate subject Arguments, Evidence and Intuition). An increasing interest for me is around ‘analytics literacy’ – which I’d broadly construe as involving assessment literacy (how do we understand assessment data, how do we design assessment tasks), learning design (how do we design tasks, and support student learning towards learning outcomes), and data literacy (how do we actually deal with the data and do analysis). Our [upcoming writing analytics]5 workshop in part takes this line. * Recommendation 9 HEIs should ensure that the digital agenda is being led at an appropriate level within their institution. See above 🙂 * Recommendation 10 University teaching and administrative staff need to be equipped with the necessary skills to perform their roles in a digital, data-driven world. Staff should be provided with appropriate training and support to improve their digital capability and data management skills. Again see above, I think this is an interesting need. Here the eResearch group, library, and other centres are hosting some training on digital capabilities and data management, but this is largely targeted at research needs. * Recommendation 11 The government should not exempt higher education institutions from the requirements of the FOI Act, and any new providers who enter into the he market and receive public funds should be brought within its scope. Very much from the UK context, nonetheless the equivalent [applies here]6. * Recommendation 12 Institutions should be encouraged to use the information from learning analytics systems to identify and foster excellent teaching within their institutions, and to consider using this information in their submissions to the TEF Again, no TEF here (yet?) but I agree – dependent on the other recommendations being achieved.
Footnotes
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http://www.policyconnect.org.uk/hec/research/report-bricks-clicks-potential-data-and-analytics-higher-education ↩
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https://docs.education.gov.au/system/files/doc/other/the_australian_research_data_infrastructure_strategy.pdf ↩
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http://sjgknight.com/finding-knowledge/2016/07/ethics-and-privacy-in-learning-analytics/ ↩
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https://www.uts.edu.au/research-and-teaching/our-research/data-science ↩
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http://www.uts.edu.au/about-uts/uts-governance/right-to-information-gipa ↩