Introduction As demonstrated throughout this volume, colleges, and universities are increasingly fi nding value in aggregating and analyzing once disparate sources of data, such as a student's admissions records, academic history, and use of campus information technologies-all under the rubric of "learning analytics" (LA) (see also, Campbell et al. 2007 ; Fritz 2011 ; Goldstein and Katz 2005 ) . In this chapter, we describe a design-based research project that developed Student Explorer, an

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... y warning system (EWS) for an undergraduate engineering advising program. This project was organized around identifying user needs, developing the necessary infrastructure for building Student Explorer, and identifying factors affecting advisors' decision-making related to the use of Student Explorer. The EWS described in this chapter represents an application of LA that is gaining popularity across colleges and universities-the near real-time aggregation and analysis of data on students' use of information technologies, such as Learning Management Systems (LMSs), for the purposes of identifying students in need of academic support (e.g., Beck and Davidson 2001 ; Macfadyen and Dawson 2010 ; Morris et al. 2005 ) . One of the many benefi ts of collecting and analyzing LMS data is that these systems are used by a majority of instructors and students on most