Effective learning analytics Search Ideas

Produce a course/module dashboard using student information to present tutors and managers with a view of individuals at risk. To do this we will explore existing institutional and commercial solutions and look to provide a possible solution (or set of tools) that can be adopted by institutions. The aim will be to find a basic tool (or set of tools) that benefit the maximum number of institutions. These tools draw in... more »

Produce a personalised dashboard for students that shows their engagement and attainment. Use analytics to provide information to help the student to achieve better results and/or chose the best pathway/module choices. To do this we will explore existing institutional and commercial solutions.

Explore an analytics solution to suggest the best pathways/modules choices, the required grades and other skills a learner might require to follow a particular career pathway, by looking at attainment and next destination information from previous learners on similar courses. The purpose will be to explore existing solutions or the viability of providing such a tool.

Examine existing tools around improving student learning through assessment feedback, feed forward or diagnostic tools. The approach will be to look at existing institutional examples and see which could be made available as generic tools to others.

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.

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.

The most expensive resources for most institution are the estates and infrastructure. The approach would be to develop a set of tools that allow institutions to examine use of estates for learning (i.e. teaching rooms) to ensure efficient use of rooms; explore staff workloads to help manage and costs courses.

Undertake a short study (3 months) to review existing institutional and commercial learner analytics tools to allow institutions to make informed choices on the best solutions for them.

Survey institutions to benchmark what they are already doing with learner analytics. This would benchmark the sector and also help to determine the maturity of institutions in relation to learner analytics i.e. data warehousing, tracking, traffic lighting, predictive analytics, personalised data

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.

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.

Investigate what roles institutions needs to support different learner analytics approaches (and what it would cost them).

A network and guide to support approaches used by institutions to a) support learners identified as at risk b) to modify the institutional behaviour to improve learning attainment. This could be a blog/online resource and series of workshops/network meetings (which could be led by membership bodies?)

Produce a student owned data dashboard that gathers any data related to the student, which may include learning attainment, engagement activity and personal data (e.g. sleep patterns, etc) and provides a view for them of how they are coping with learning/student life.

'Human readiness' for learning analytics isn't a given. It's important to ask under what circumstances learning analytics might be welcomed by teaching staff, students, and other decision-makers - in principle and practice.

The work of Anna Lea Dyckhoff advances a view of learning analytics as answering action research questions posed by teaching staff. Carrying out similar studies for other stakeholders - particularly... more »