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a member of Purdue's Teaching Academy. Since 1999, she has been a faculty member within the FirstYear Engineering Program, teaching and guiding the design of one of the required first-year engineering courses that engages students in open-ended problem solving and design. Her research focuses on the development, implementation, and assessment of modeling and design activities with authentic engineering contexts. She is currently a member of the educational team for the Network for Computational Nanotechnology (NCN). Building Course-Specific Regression-Based Models to Identify At-Risk Students AbstractThe first step in helping students who may fail a course is to identify them as early in the semester as possible. Predictive modeling techniques can be used to create an early warning system which predicts students' success in courses and informs both the instructor and the students of their performance. One common problem with existing early warning systems is that they typically employ a general model that cannot address the complexity of all courses. In this study, we built three models to identify at-risk students in a specific large first-year engineering course at three important times of the semester according to the academic calendar. Then the models were optimized for identifying at-risk students. The models were able to identify 79% of at-risk students at week 2, 90% at week 4, and 98% at week 9. This high accuracy illustrates the value of creating course specific prediction models instead of generic ones.

Sixty-seven undergraduates taking either a Blended Business Course (BBC) or an Online Education Course (OEC) were surveyed about factors influencing their "listening" behaviors in asynchronous online course discussions. These are the ways they attend to the posts made by others: which posts they open, how they engage with open posts, and which posts they choose to respond to. Goal-orientations were also assessed. Results indicate that student decisions about which posts to open relied strongly on discussion replystructure and message timing; authorship was important only to BBC students. Once open, OEC students often scanned posts to decide whether to read in-depth. In the BBC, similar triage strategies were used by workavoidant students, while mastery students read posts thoroughly. In deciding which posts to reply to, BBC students favored posts that agreed with them while OEC students favored those that disagreed. Course and student characteristics that may account for these differences are discussed and implications for research and practice are presented.