The software that visualizes data trends and helps organizations make informed, key decisions has significantly evolved in recent years. Higher education institutions are finding innovative ways to leverage this data in order to measure how engaged their students are, and to identify ways to help them reach success; however, there is still more they can do, and in different ways, to better measure student engagement.
The Current Climate for Measuring Engagement
As higher education progressively uses more technology to support teaching and learning, institutions have access to data from software like learning management systems (LMS) that indicates how users interact and perform throughout their learning path. Access to this data lets institutions explore new ways of understanding how faculty are teaching and how students are learning.
Pairing edtech data with other variables like demographics or historical performance is often key to providing efficient, personalized support for student engagement and success. While some institutions work with vendors to predict student engagement and success, others are designing their own models and systems that help them identify warning signs that a student will drop out or graduate late based on the unique conditions and systems in use by their campus community.
Yet, access to, and optimization of, data from educational technology is maturing. Institutions need more than just access to raw data or databases; they need optimized data, in a consistent, standardized format, and delivered fast.
In some cases, the data must be delivered in real-time, so institutions can analyze it on-the-fly in order to reach students—or their advisors and coaches—in the moment, and with the context in which, they might need support.
(Next page: The future of using data for engagement, and how to get there)
The Future of Data for Measuring Engagement
To this end, higher education wants to see data used to predict not just if a particular student might drop-out of a program or fail a course, they want to see data used to predict whether or not a particular intervention or engagement will be helpful and welcome to students.
This is where machine learning becomes even more appealing. Machine learning is a general term for a variety of methods of automating the analysis of data in order to understand relationships between variables, identify clusters or patterns and predict outcomes.
The “learning” part comes in because these methods are designed to continuously learn and improve their own effectiveness and accuracy as more data comes in and as results of predictions are tested.
Changing the Way Colleges Look at Engagement
Just as whenever we look at technology as a solution for problems we must also consider the human factor, we should also balance top-down approaches to student success with bottom-up efforts.
Student success is most often discussed as a top-down problem; the challenge of student retention and completion seems most relevant to institutional leaders because these issues tie to institutional funding and accreditation. It makes sense, then, that institutions spend a lot of time and energy on top-down initiatives that create an environment, culture and support system to address the symptoms of student dis-engagement.
And yet, research on student success in higher education and K-12 points very clearly to the benefits of bottom-up approaches to student engagement. Specific teaching and learning methods can increase student engagement in ways that extend beyond a given course or semester.
The kind of engagement that makes a difference in student success is something we can actually affect by understanding the interplay between teaching, learning and technology. For example, one study of student engagement in technology-rich environments found that students reported the lowest levels of engagement when technology was used primarily for “content transmission”. [1] Conversely, another study showed course-level student-to-student interaction, collaborative learning and active learning is linked with “long-term persistence and degree completion.” [2]
Where Analytics Can Fill the Gaps
Our best faculty will strive to create engaging experiences no matter what technology they have. But those practices aren’t always recognized or shared within the institution, and this is another opportunity for data and analytics to be of use — for example, in identifying instructional practices that relate to engagement and predict success for diverse groups of students.
The field of learning analytics is young, but we’re seeing progress and promising results from across the spectrum of higher education.
Creating more sophisticated and impactful support services is important, as is fostering better teaching and learning practices that tie to engagement. In both cases, analytics can help higher education understand not just which students need our support, but also where we are today and what’s most likely to yield positive outcomes.
The opportunity to advance teaching practices, increase student engagement and improve overall student success by leveraging optimized data and technology is one that higher education institutions must take advantage of for their long-term sustainability.
[1] Gebre, E. Saroyan, A, Bracewell, R. (2014). Students’ engagement in technology rich classrooms and its relationship to professors’ conceptions of effective teaching. British Journal of Educational Technology, 45(1), 83-96.
[2] McClenney, Kay, C. Nathan Marti, and Courtney Adkins. “Student Engagement and Student Outcomes: Key Findings from.” Community College Survey of Student Engagement (2012).
Jared Stein is the VP of Product Strategy & Research for Canvas by Instructure.
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