This year’s Horizon Report suggests that over the next three to five years one of the key trends in accelerating technology adoption in HE will be a growing focus on measuring learning.
There is an increasing interest in using new sources of data for personalizing the learning experience, for ongoing formative assessment of learning, and for performance measurement; this interest is spurring the development of a relatively new field — data-driven learning and assessment.
The intention for measuring learning may differ, but together they are spurring the development of learning analytics. The goals are likewise varied and include building better pedagogies, empowering students to understand and manage their own learning journeys, identifying at-risk students for targeted intervention, and assessing factors that affect retention/completion.
Institutions may already have a lot of data on their students. One of the challenges is understanding and analysing this data. As some organisations transition to make greater use of online learning they will only increase the available data for analysis. This does prompt a concern regarding the ethical and privacy considerations when analysing data, especially where existing data is analysed for new purposes.
The Open University in the UK produced policy on the ethical use of student data for learning analytics, grounded on eight key principles that are linked to particular facets of collecting and analyzing student data. Progress is also being made in the US. In 2014, educators, scientists, and legal/ethical scholars gathered at the Asilomar Conference in California to develop a framework that will inform the ethical use of data and technology in learning research. Six principles emerged: respect for the rights of learners, beneficence, justice, openness, the humanity of learning, and continuous consideration.
At UoLIA we’re very interested in taking advantage of the wealth of data we have on students to help us understand more about who is studying with us. We are currently initiating a project to use predictive analytics to look at issues of retention and progression.
We hope that over time learning analytics will provide just-in-time information to staff to help us:
- identify students with difficulties so we may be better able to support and improve their outcomes
- inform and improve our learning design year-on-year
- provide up-to-date information to students on their progress
Our proposed schedule is as follows:
- Summer 2015 – UoLIA pilot predictive analytics project developed with ULCC.
- September 2015 – Pilot student retention project begins.
- December 2015 – Pilot ends. Evaluation of outcomes.
We’ll continue to update this blog with progress on UoLIA’s work with learning and predictive analytics.