Institutions of higher education aggregate and warehouse many terabytes of student data, aiding routine functions including degree conferral, financial aid eligibility, online course management, and more. In recent years, scholars of teaching and learning have begun exploring new ways to utilize these “big data” resources, extracting information that might directly contribute to the institution’s educational mission. These nascent methodologies have come to be termed learning analytics.1 For the most part, efforts in learning analytics have focused primarily on the development of predictive safety nets, empirically derived early warning systems that deploy flags and interventions for students who are under-performing or otherwise “at risk.” Nonetheless, some believe that learning analytics has largely untapped potential, with far more value to be gained from mining student data, beyond providing diagnostic tools.
Accordingly, this article describes a novel use of institutional data resources in higher education. Rather than providing information about students at risk, we aimed to develop a system that would broadly inform instructors about all their enrolled students, providing summarized institutional data about the aggregate characteristics of the students enrolled in their respective classes. We call it the Student Profile Report (SPR), a short document that summarizes student records, intended to provide a useful snapshot of information about the population of students taking a course prior to the start of a semester. We hypothesized that, when given this report, instructors might be spurred to develop more learner-centered course materials, better accommodating their students’ characteristics. Our current research seeks evidence that instructors would be attentive and responsive to this implementation of learning analytics and, importantly, that the report would not cause bias in letter grade assignment.