Classroom walls that talk: Using online course activity data of successful students to raise self-awareness of underperforming peers John Fritz Div. of Information Technology & the Department of Language, Literacy and Culture, University of Maryland, Baltimore County, USA abstract article info Keywords: Analytics Course management systems Retention Student success Self-efficacy Self-regulated learning Similar to other institutions, the University of Maryland, Baltimore County (UMBC) has determined that a relationship may exist between student performance as defined by grades, and activity in the campus' online course management system (CMS). Specifically, since Fall 2007, UMBC's “Most Active Blackboard Courses” reports show students earning a D or F in a sample of 131 courses used the CMS 39% less than students earning a grade of C or higher. While the sample needs to be expanded and the demographic backgrounds of students need to be studied further, what if this usage pattern holds true throughout the semester? And how might students' awareness, motivation and performance change if they could know this information sooner? This article presents a new tool that UMBC students can (and do) use to check their activity and grades against an anonymous summary of their peers, which might make them more inclined to seek or accept academic support. © 2010 Elsevier Inc. All rights reserved. 1. Introduction With barely one in five Americans over 25 earning a bachelor's degree, retention of students who actually enter college is vitally important to our country's global competitiveness (Educational Attainment in the United States: 2007 Detailed Tables, 2008). Yet, nationally, the six-year graduation rate for all colleges and universities is 63% (Berkner, He, & Cataldi, 2003), and students in their second and third year of college can be among the least likely to persist (Lipka, 2006). Institutions need to do better, but retention experts agree that underperforming students also need to take some responsibility for their own learning (Choi, 2005; Hsieh, Sullivan, & Guerra, 2007; Tinto, 1993). To help, many institutions are turning to information technology in a way known as “academic analytics.” Typically associated with business and marketing—Amazon analyzes other people's past purchases to suggest books you might be interested in buying next—analytics is now being used in higher education to identify and even predict students who may be at risk by studying demographic and performance data of former students in the same course, major, and institution. However, the problem this article attempts to define—and illustrate with preliminary results of a still evolving case study—is how to apply academic analytics into a scalable intervention that motivates underperforming students to seek or accept help, without raising concerns about their privacy or academic profiling. Even if our data models are highly predictive, how do we convey this insight in a way that underperforming students will not dismiss or misunder- stand? Why shouldn't they think they're the exception to our rules? 1.1. Review of literature and practice The earliest attempt to define academic analytics appears in Goldstein and Katz (2005) who called it “an imperfect equivalent term for business intelligence” (p. 2), which essentially describes the use of information technology to support operational and financial decision-making of corporations. Though still evolving, the crossover of analytics from business to higher education can be seen in Goldstein and Katz's survey of 380 higher education institutions and follow up interviews with 27 individuals who reported “exemplary success” with academic analytics. They report that few organizations “have achieved both broad and deep usage” and also provide a useful framework for categorizing key milestones in any institutional application of analytics: • Stage 1—Extraction and reporting of transaction-level data • Stage 2—Analysis and monitoring of operational performance • Stage 3—What-if decision support (such as scenario building) • Stage 4—Predictive modeling and simulation • Stage 5—Automatic triggers of business processes (such as alerts) For the most part, Goldstein and Katz's use of the word “academic” describes a setting where analytics takes place, not necessarily a goal for improvement. They do give a nod to the potential for “improving student learning outcomes,” but the scope of their inquiry includes a Internet and Higher Education 14 (2011) 89–97 E-mail address: fritz@umbc.edu. 1096-7516/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.iheduc.2010.07.007 Contents lists available at ScienceDirect Internet and Higher Education