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Abstract

The Early Identification of At-Risk Students in an Undergraduate Marketing Metrics Course

Abstract

This research describes the development of a diagnostic tool to permit the early identification of at-risk students in an undergraduate marketing metrics course. Using multiple discriminant analysis, students were classified into performance categories by drawing on a set of predictor variables conceptually linked to student performance in math-based courses. The discriminant model included math ability, perceived self-efficacy, math anxiety and overconfidence as potential discriminators of student performance. The model successfully identifies at-risk students at three times the chance probability. The early identification of at-risk students is a critical first step in the process to improve student performance.

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