Thursday 06-08-2015 - 14:05
Learner analytics is about using the increasing potential of data insight to improve students’ learning.
As IT infrastructures and processing power develops, it is now possible to record and store data relating to many aspects of the student learning experience, and many higher and further education providers are developing new data systems to more effectively support students and educators. Data models can identify trends and patterns to assist educators in designing personalised support and assistance for students, and to arrange interventions if there is evidence of a student struggling.
This has great power and potential to tackle some of the problems and challenges that currently exist in UK higher education, such as avoiding unnecessary drop-outs, student demotivation, reducing the number of exam resits, enabling more reflective learning and engagement, and reducing inequalities such as the BME attainment gap.
Despite all the exciting potential of learner analytics there are a number of issues that could prove problematic if the appropriate checks and balances are not in place to defend students’ rights and interests. Jisc, with input from NUS, has developed a Code of Practice to guide institutions when developing learner analytics. We have also produced our own briefing for students’ unions who are working with institutions developing new analytics systems.
You can find the Jisc Code of Practice here
You can find NUS’s briefing here