There were days when the term ‘measurement’ attracted huge focus in all our discussions and quotes such as ‘you can’t manage what you can’t measure’ gained much popularity. However, we have come a long way from there. In the education industry, with online systems such as Learning Management Systems and Student Information Systems now driving functions in higher education institutes, there is humongous amount of data that gets stored as part of each and every transaction. Some of these data are used for creating reports such as ‘how many students have passed with grade A’, ‘what is the overall satisfaction score of the newly launched eLearning course’, ‘how many students have paid fees on time’, etc. As is the case, institutes today, make informed decisions and actions based on these reports (that are generated based on events that have already occurred) and wait for another term or so to go by to see whether the actions resulted in an improvement of what was measured.
Predictive analysis solves this issue by providing insights before an event occurs so that you can influence the outcome of the event. To give you an example, if you need to reach a destination on time, information about any blocks on the way should be available to you before you start the journey so that an alternative route could be taken. Exactly the same way, anything that derails a student’s progress should be known at the beginning so that necessary personalized interventions can take place in a proactive manner to ensure success ratio.
Given this, why should an education institute just go for normal reports and dashboards which carry information and insights based on what has happened already? Shouldn’t there be actionable insights at each and every step so as to change the path beforehand and meet the end objective?
Higher education institutes are jumping into the analytics space to see how they can use insights to make the student experience better. Analytics is now widely used to improve prospect to student conversions, identify the right channels to attract international students, identify at-risk students for retention, provide personalized learning and also support financial decisions.
And we see that institutes are gaining benefits. For instance, Arizona State University improved the retention rate by 8% by using predictive analytics to identify students at risk of failure and by providing them support. Seton Hall University increased their enrolments by 13% via social analytics and reporting.
Recruitment, Retention and Personalized learning seem to be the primary areas of focus where an analytics intervention is used to improve outcomes. Still, institutes are sometimes wary of taking this route and are interested in testing the waters first. With as-a-service models gaining popularity in higher education, customers now look for solutions which do not need any capital investment. The newer engagement models help the customer discover what to measure and through which data sets. They also get the benefit of the latest technologies and access to data scientists and industry experts for quicker and deeper insights.
I believe that this is the right time, if not late, for institutions to move ahead from just reflecting on the past towards predicting and shaping the future as well as providing the best to their students, faculty and staff.