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Can Data-Driven Student Retention Strategies Make an Impact?

Student retention, already a major concern at higher education institutions, is an even greater concern as students are unable to learn in person and on campus. It is now standard for education learning management systems to provide some kind of at-risk warning feature. At the end of July, Blackboard Inc. announced a service called Blackboard Retention Coaching, which uses predictive data from Blackboard Predict and the institution’s student information system to determine when students are struggling or becoming disengaged with their studies. The solution is designed to pull student information from Blackboard Data and from the student information system to determine which students would benefit from additional intervention and support, which is then offered by Blackboard coaches and/or the institution’s own services.

Source: Blackboard Vendor Composite Score

Our Take

Student retention has already been a long-standing concern for higher education: 59% of students complete four-year college/university degrees within a six-year period. Moreover, institutions may be operating within performance-based funding models, which adds to their motivation to improve completion rates. To complicate the matter, as more teaching and learning moves online due to the pandemic, teachers are increasingly cut off from the in-person, interpersonal strategies they may have been accustomed to using to determine if a student is struggling. Predictive data can appear as an attractive solution to helping students before an individual teacher is able to perceive they need help. Blackboard’s introduction of coaching services seems to offer a restoration of the personalized support element to potentially alienated students. Institutions can look at data to see whether retention rates improve; however, equally useful will be qualitative feedback from students who use these call-center coaching services to understand their perspective on what the most useful types of intervention are.

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