Data Informed Decision Making — LILAC 2016
By Emma Warren-Jones
Data-informed decision making: Research data that can inform library procurement and investment in the student learning experience
Evidence is growing to support the view that learner analytics and other data sets could be better utilised within the Higher Education sector than they are at present (Kuh, 2003). Engagement data concerned with physical spaces (eg. libraries) and virtual learning environments could all become more valid proxies for teaching and learning excellence, in addition to more established academic league tables.
Despite recent publications espousing the best principles and practices with regards to learner analytics in higher education (Sclater, 2014), adoption of those principles and the monitoring of staff and student engagement is still relatively low across institutions throughout the UK. Disruptive new platforms and software emerging in this space are shifting the status quo and demonstrating the real impact of decisions based on learner outcomes data. The HE sector is starting to place more emphasis on the value of this information and the impact students’ research and learning data can have on their academic experience.
Initial engagement with our Technology Innovation Partners in the HE sector suggests that data relating to how and what a student is referencing during their research activity, can provide valuable indicators for future library investment decisions. It also provides the student and teaching staff a clear and traceable digital research footprint, which could be linked to an individual’s performance.
RefME is sponsoring LILAC 2016 at University College Dublin from 21–23rd March.
Come and visit us at our exhibition stand and find out more about how RefME Institute is revolutionising the student learning experience!
RefME workshop at LILAC 2016 — What’s in it for you?
By attending RefME’s Data Informed Decision Making workshop, you can expect to discuss some thought provoking concepts around the potential of student research data for informing library procurement decisions, as well as the impact this data can have on learning and teaching environments. You’ll be also be collecting some raw data through the RefME app and online platforms which we’ll use to illustrate some of the insights you can draw from the research activities of your student community. Participants can expect to leave with a clear picture of the power that student research data can afford them in their current and future decision making.
0–10 mins — Introduction and presentation of key concepts around learner analytics, big data and ethics.
10–30 mins — The group will be asked to participate in the collection of some raw data in order to consider ways in which currently uncollected and underutilised data sets may facilitate future decisions within the library and other departments. What may be defined as engagement, as opposed to monitoring, will also be discussed in relation to the JISC publication (Sclater, 2014).
30–45 mins — Discussion and concluding statements.
Data intelligence notes — estates management (2011), Available from: https://www.hesa.ac.uk/intel?name=bds_emr [Accessed: 1.02.16].
King, J.H. & Richards, N.M. (2014) Big data ethics, Available from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2384174 [Accessed: 1.02.16].
Kuh, G.D. (2003) What we’re learning about student engagement from NSSE: Benchmarks for effective educational practices, Change: The Magazine of Higher Learning, 35, (2), 24–32.
Sclater, N. (2014) Code of practice for learning analytics A literature review of the ethical and legal issues, http://repository.jisc.ac.uk/5661/1/Learning_Analytics_A-_Literature_Review.pdf.
This post originally appeared on RefME blog.