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| Dr Ellen Wagner, Chief Research and Strategy Officer for the Predictive Analytics Reporting (PAR) Framework. Photo: OEB Newsportal |
Students failing to complete their education represent a problem globally for already financially challenged higher education institutions. Every student ‘lost’ is seen as a financial loss, as universities miss out on fees, government funding and potential future alumni contributions. And of course for students, dropping out can mean losing initial investments and valuable time, as well as the ignominy of being labelled a ‘drop out’.
As higher education provision continues to grow, so too does concern regarding student ‘attrition’ (the reduction in numbers of students attending courses as time goes by). According to UNESCO, the percentage of adults worldwide who have received tertiary education rose from 19% to 29% between 2000 and 2010. This growth has continued in the first half of the decade, albeit at a slower pace. Such growth is subject to what Philip Altbach, Director of the Centre for International Higher Education at Boston College, has called the ‘law of expansion’. Apart from a small number of elite institutions, Altbach claims that any large expansion in a country’s tertiary education sector will be matched by a decline in quality of both education and students – as students of a wider range of ability are being taught often by less qualified staff under conditions of stretched public funding.
Does the growth of higher education mean then, by necessity, that student attrition rates will grow in turn? Not necessarily. Using the analytical power of big data, the Predictive Analytics Reporting (PAR) Framework is offering an innovative way of understanding student loss, allowing institutions to identify causes and thus halt the flow of students dropping out. With more than 351 unique member campuses, over 2.6 million anonymous student records and 24 million institutionally de-identified course level records, PAR seeks to provide a holistic perspective in order to improve student outcomes on a broad scale across the US.
Dr Ellen Wagner, Chief Research and Strategy Officer for the framework explains: “PAR uses predictive analytics to find students likely to be at risk of dropping out, finds the variables /reasons they are likely to drop out, which then helps target interventions most likely to provide support for that student at the point of need.”
Using predictive techniques usually found in business intelligence settings to aid educational decision-making, PAR does not only seek to predict who is at risk, but also provides analytical data which measures the effectiveness of interventions. According to Dr Wagner, “We spend as much of our energy on intervention measurement as we do on the predictive models and descriptive benchmarks we have developed.”
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Source: OEB Newsportal
