Sponsored: Technology captures data constantly and can be used to generate reports on individual students and groups but also future outcomes
A compelling benefit of technology-based learning is that it provides students, teachers, tutors and administrators with a wealth of information on how students are learning and progressing. This is because learning technology platforms capture data constantly and this can be used to not only generate reports on individual students and groups but also to predict future outcomes.
The sort of outcomes that are really important to predict are whether or not a student or group are going to pass a course, or if they’re at risk of failing and which actions and interventions will provide the greatest chance of success. Armed with this knowledge, tutors can intervene – as necessary – to get (and keep) students on track.
Data can be collected all the time when learning is digital. It can reveal many things about student progress and learning behaviour such as proficiency across course topics, personal areas of interest/engagement, where weak points lie, how much time is spent studying and how much participation there is in discussion groups. The beauty of all this captured data is that it provides many opportunities to build a unique picture of each student, therefore, empowering the student’s learning experience.
It enables educators to craft a personalised, adaptable learning journey tailored towards an individual’s success. If something isn’t working, the data shines a light on it and enables something to be done about it. Let’s say a student is faltering on a particular part of the syllabus, then additional content can be added into their work stream to bring them back on track.
Collectively, data builds a picture of how all students on a course, or across a learning institution, are progressing. From this, deductions can be made on a wider scale that can be used to positively influence the learning path of individuals. For example, if the data shows that students who engage in group discussions perform better in a subject, more participation can be encouraged among the students who tend to work in a more solitary way. More generally, data can help identity patterns of behaviour that can be modelled in (in the case of good behaviour) learning processes or modelled out (in the case of bad behaviours)
Of course, what works for one student may not work for the next. Learning analytics supports this understanding of the importance of individual learning pathways with concrete detail around what does, and does not suit each student. It gives teachers and administrators insight that they can use to change or adapt content or course delivery.
Predicting at-risk students
The wealth of data gathered by a learning platform provides a baseline of how students perform overall on a course. An individual’s progress can be compared against this baseline and automatic deductions made.
If predictive analytics determine that a student is not on track and that their learning progress, studying behaviour and/or output indicates they will not achieve the required outcome, a number of options are available to turn things around. The option – or options – that are presented can be managed according to the needs of the student and the available resources of the learning institution. These can range from connecting the student with external resources, to arranging a tutoring session or revising the learning schedule.
Students themselves also benefit from insight into their progress. They can take ownership of their study approach if predictive analytics forecasts an outcome they don’t want. The analytics helps them to understand what they need to do today in order to achieve the result they want tomorrow.
The impact of these insights on student learning behaviour has been seen in practice. The Higher Education Policy Institute (HEPI) in a 2017 report found that a staggering 81 per cent of students in Nottingham increased their study time after seeing their own engagement data.
Smart data equals better outcomes
Predictive analytics supports informed, action-oriented decisions that can help achieve better learning outcomes. This can translate into higher rates of student retention, completion, and success. Insight through analytics empowers both tutors and students to benefit from personalised and adaptive learning and to take ownership of individual learning pathways.
Over time, continuous course improvement can result from insight through analytics with tweaks or updates made to content and/or delivery to keep study programmes relevant and delivering at the highest level possible. As student needs change over time, this will help educational institutions maintain and improve student engagement, performance and outcomes.