The emerging fields of learning analytics and educational data science have seen rapid change in recent years. As adoption increases and we in higher education grow in our understanding of how data collected by colleges and universities can be used to increase student success, it is helpful to look back at how far we have come in the past year as we also look forward to the year ahead. We recently asked three of our analytics experts to reflect on the field of learning analytics at the end of 2017. This is what they said.
I’ve done a lot of traveling for work this year, speaking to people about their aspirations for using analytics and sharing the findings from our data science team. I think that the field of educational analytics is rapidly making progress — and the idea that we will analyze data is becoming accepted as “status quo” and a “must do” for many institutions. At the same time, we’re making enough progress with our analysis and algorithmic approaches to know what works, and what doesn’t, in predicting student success.
It’s quite clear, for example, that simple click counts or other tallies of student use of the LMS (or other technology) are shallow indicators of student success at best, and at worst can be quite misleading. This is something that many faculty and administrators have intuited for some time, and we’ve begun identifying deeper proxy indicators and cross-cutting analyses that provide deeper insights into what really matters.
I am very excited for the year ahead, as I think the momentum is building to do better analytics, and the application of deep learning and other advanced techniques is showing a lot of promise in early studies. We are also thinking carefully about the ethical implications of this work, ensuring that we’re on a long-term path that is technically robust and sensitive to the wide range of factors that must be considered in order to move the needle on student success.
2017 has been a year of surprises for higher education here in the United States. In January, we posted a blog entry on what a Trump presidency might mean for US higher education. At the time, no one was very certain of what a Trump administration higher education policy would look like. And although much of the federal conversation about education this last year has focused on K-12 issues, we’re starting to see some very important higher education conversations emerge, especially around reauthorizing the Higher Education Act.
When I think about what the next year might hold, I’m excited about the national conversations between practitioners and national policymakers that will be a part of reauthorizing the Higher Education Act. Regardless of what the finished legislation looks like, it is clear that robust conversations about student success are going to be driving the move to reauthorize the Higher Education Act. And that means there will be lots of opportunities to talk about what analytics is telling us about student success, and about the role of analytics in higher education policy on a national level. I think 2018 will be the year we use analytics to drive higher education policy development in a way that will influence higher education for years to come.
What has struck me time and time again over the past year is that we, in higher education, continue to struggle with analytics adoption. When we consider that organizations like NMC had projected that we would have seen widespread adoption by now, it is no surprise that by 2016 we had entered a kind of trough of disillusionment. For me, 2017 really marked the beginning of our ascent up the slope of enlightenment. After several years of rolling around in data muck without really knowing what questions to ask, or what kinds of practices we need to put in place to see significant impact, our analytics imagination is only now beginning to really ignite thanks to the network effects we see from the formation of strong communities of practice.
Over the past year, it has been my great pleasure to share the success stories of Blackboard partners like Coppin State University, which democratized its data and saw a 50% increase in freshman enrollment. Or like Concordia University – Wisconsin, which adopted a data-informed approach to student advising and saw a 10% increase in student retention. Or like Indian River State College which eliminated its achievement gap on the basis on course modality. I have been thrilled to see universities like University of Missouri – Kansas City and University of Arkansas – Monticello use Analytics for Learn in support of faculty development. And I have been delighted to see institutions like University College of Estate Management use data to discover just how impactful their approach to supporting students in high enrollment online courses really was. These are not Blackboard success stories. These are the stories of our clients, and I am pleased that, through products and services, Blackboard has been able to play a supporting role as a partner in change.
In 2018, I expect us in higher education to continue up the slope of enlightenment. Acknowledging that transformative change is always a function of people and practices, I look forward to a year in which we at Blackboard continue to support community as much as we are committed to learning analytics research and market driving product innovation.