Adapting to analytics

Data analytics is being used by marketing companies to help pitch products to consumers, but how can data collected by systems such as Cengage Learning’s MindTap on the online activity of students, be used to help improve the student experience at university?

Data is already used by universities to predict grades. If a student is achieving around the 60% mark in essays then it is likely they will go on to be awarded a 2:1 degree.

The idea of learning analytics – data collected from students’ online activity showing how or when they study – is to go deeper into the process, collect data in real time and try to predict the outcome on a day-to-day basis. When done well it can be used to improve student retention and results, as well as ensuring courses are better run. 

Universities today can analyse which resources students access in the virtual learning environment (VLE), such as a chapter in a book or video, to get a good idea of which is the most efficient learning path to get the best outcome.

Library services can analyse how content is being used so that they can optimise their budget and processes. Universities can look at data from previous years, revise it, and ensure that in future it is better adapted to student needs. 

This data can also be used to see what is happening on a day-to-day basis. Students who are not accessing online resources or attending lectures can be identified quickly and remedial action taken where possible (with a focus on supporting students rather than penalising them).

The advantage of learning analytics is that you get an instant assessment of student performance and can feedback much faster. Providing students with daily responses like this will help universities keep their students engaged and boost retention.

At Plymouth University, the Student Support System (S³) is helping academics to manage personal tutoring, attendance monitoring and basic analytics to support over 15,000 students (including overseas partnerships). The system collects assessment submissions, attendance records, tutoring records and academic attainment to present a wide-lens on a student’s academic life. This enables tutors to gather a range of information (in a single system) to view the student’s engagement with their learning and assessment, as well as reviewing performance and notes from previous interactions with tutors, module leaders etc. Even simple tools can help – for example, showing a student their marks in a visual manner:

Commercial companies that store and analyse data include Oracle, SAS, Newton and, Cengage Learning’s MindTap. MindTap is a new personal learning experience that combines all of the university’s digital assets – readings, multimedia, activities and assessments,integrates with the university’s VLE and allows tutors to set mock exams using the assessment feature to track student progress and to identify areas where further tuition is required. 

In each of these examples learning analytics is used to illustrate the students’ learning path and present it in a way that lecturers can use to help improve teaching.

Going forward, learning analytics will continue to develop this relationship between data, teaching and learning so that universities can maximise the student experience.

Dr Paul Dowland is a Senior Lecturer at Plymouth University and the architect of the S³ system.