One of the major talking points of the democratic primaries that appears to have been forgotten about during the presidential election proper has been the issue of tuition fees, with US students often leaving college in severe debt. At the lower end of the scale, public colleges charge on average roughly $9,000 per year, while at the upper end, Ivy League schools like Harvard demand as much as $60,000 a year for a degree.
This is, by anyone’s standards, a significant sum of money, and students rightly expect value for it. In order to provide this value, universities are increasingly turning to data analytics, with a KPMG study finding that 41% of colleges are engaged in predictive analytics and other recent surveys yielding similar results. While this number is rising though, it is still some way below the 62.5% adoption rate of predictive analytics in the private sector, and new research may provide some answers as to why.
In the 2016 Inside Higher Ed Survey of Faculty Attitudes on Technology, just 27% of faculty members and 34% of administrators said their data efforts have actually improved the quality of teaching and learning at their institutions. Similar numbers said the same about the impact on degree completion rates. Meanwhile, 65% of faculty members and 46% of administrators said their data initiatives had only been carried out to appease external groups, such as accreditors and politicians. This suggests one of three things: 1) they are not committing to data insights because they resent pressure from parties they see as undermining them and trying to infringe on their duties, 2) data does not provide any ROI in a higher education setting, or 3) data initiatives are not being properly carried out because universities are not approaching them in the correct way due to lack of knowledge or mismanagement.
The idea that faculty members would not want to do the best for their students is patently ridiculous, as is the concept that analytics simply cannot work in universities. It is also easily disproved by universities where it is working. Georgia State University, for example, is a shining example. They analyzed 2.