As the old saying goes, “knowledge is power.” The more we know, the more control we have and the more we can change things. In education, when tutors and learning providers have insight into the progress of their students, they are able to take action if things aren’t progressing as well as they could. Technology-based learning provides data that, when analysed can provide this insight, revealing what works and what doesn’t so that learning outcomes can be improved through informed intervention.
A cloud-based learning platform is an efficient way to deliver content, house applications and manage student interactions. Furthermore, it is ideal for gathering and managing data to provide the kind of insights that educational institutions need to ensure successful outcomes.
What’s so big about data?
We all know what we mean by data but why is it sometimes called ‘big data’ and why is there so much fuss about it? There are many definitions out there but all tend to agree that ‘big data’ refers to very large data sets that can be analysed for a purpose.
In education, as elsewhere, this purpose is to meet objectives and deliver results. Data analytics helps achieve this by correlating characteristics of learning behaviour with outcomes. The kind of characteristics that can be analysed include student time spent studying, levels of interaction with digital tools such as discussion groups and content usage.
Cloud computing gives students and tutors always-on access to learning, course content, data and applications. It’s scalable, meaning that institutions can add capacity as needs dictate.
Each time a student interacts with a digital cloud-based learning platform they leave a digital footprint. This creates a vast pool of data points that can be correlated and analysed to match learning behaviour to successful outcomes. This isn’t just on an individual student basis but across the whole data bank within a course or learning institution.
Ultimately, insight from this analysis helps tutors steer students along the learning path that is right for them and that is statistically more likely to help them succeed.
Data analysis in education supports today’s increasingly popular view that better outcomes are achieved from a ‘one size fits me’ approach to learning. Data mining reveals what works collectively and individually so that course content and delivery can be adapted to each learner for a personalised learning experience.
While learning analytics and the use of big data in education is still in its infancy in many areas, there is evidence of its success in a variety of institutions. In Australia, for example, as the Higher Education Policy Institute reports, the University of New England saw student attrition rates fall from 18 per cent to 12 per cent during a learning analytics pilot project.
Big data gives schools and colleges the ability to capture student success data and to use it to improve engagement, retention and course completion rates. Data analysis makes sense of the past – what has worked – while predictive analytics uses the insight to forecast outcomes. Armed with this knowledge, tutors (and students themselves) can take action to achieve the results they desire.