Using Multilabel Neural Network to Score High-Dimensional Assessments for Different Use Foci: An Example with College Major Preference Assessment

Abstract

Scoring high-dimensional assessments (e.g., > 15 traits) can be a challenging task. This paper introduces the multilabel neural network (MNN) as a scoring method for high-dimensional assessments. Additionally, it demonstrates how MNN can score the same test responses to maximize different performance metrics, such as accuracy, recall, or precision, to suit users' varying needs. These two objectives are illustrated with an example of scoring the short version of the College Majors Preference assessment (Short CMPA) to match the results of whether the 50 college majors would be in one's top three, as determined by the Long CMPA. The results reveal that MNN significantly outperforms the simple-sum ranking method (i.e., ranking the 50 majors' subscale scores) in targeting recall (.95 vs. .68) and precision (.53 vs. .38), while gaining an additional 3% in accuracy (.94 vs. .91). These findings suggest that, when executed properly, MNN can be a flexible and practical tool for scoring numerous traits and addressing various use foci.