Provide opportunities for institutions at similar stages of learner analytics implementation to work together and share what they are doing through a series of special interest groups. Suggested groups could be around course dashboards, attendance tracking, ethics, BME and gender groups, etc.
Using data and analytics to support students; improving satisfaction, retention and graduation rates.
Universities and colleges don't have enough useful data about students and how they are learning. What they have they don’t analyse and interpret. They are missing opportunities to use technology to provide feedback to students. They need to support staff who could be using analytics and a standard set of tools and technologies to monitor and intervene.
To share and prioritise ideas on how best to address the challenge of learning analytics. The timescale is to collect and prioritse some ideas by the end of August. The ideas are being collated from a series of stakeholder discussions from June-August and will feed into a prioritisation workshop in September.
The consultation has resulted in three priority areas have been chosen to work on over the next two years and further details are available from the Learning Analytics Blog. The ideas list will be captured and reviewed over time.
Jisc provides digital solutions for UK education and research.
Ideascale is being used to support a Jisc project but is not affiliated with or funded by Jisc in any way.
Building on the work of the Library Analytics and Metrics Project (LAMP). Develop further use cases to extend the data set and look at learner analytics in relation to resource usage.
A report to inform institutions on what specific data they need to gather to do effective learner analytics. Combined with the metrics this will avoid all institutions from gather vast amounts of data unnecessarily and wasting time doing their own analytics to determine what correlations are significant for predicting student attainment.
Research existing learner analytics and produce a basic summary of the metrics (what data matters and how to analyse it). This study would also provide some key learning from existing learner analytics that could be used by institutions to make informed decisions about their choice of analytics tools and how to interpret information. The report would also support a more DIY solution to the course/student dashboard.