Decoding the Alphabet Soup of Degrees in the United States Postsecondary Education System Through Hybrid Method: Database and Text Mining

TL;DR

This paper introduces a hybrid model combining database referencing and advanced text mining techniques to accurately classify ambiguous postsecondary degree levels in student reports, improving understanding of student success and mobility.

Contribution

It presents a novel hybrid approach integrating database lookup and CNN-BiLSTM text classification for degree level prediction, achieving high accuracy.

Findings

Achieved 97.83% accuracy with complex datasets

First automated method for classifying ambiguous degree levels

Enhanced insights into student success and mobility patterns

Abstract

This paper proposes a model to predict the levels (e.g., Bachelor, Master, etc.) of postsecondary degree awards that have been ambiguously expressed in the student tracking reports of the National Student Clearinghouse (NSC). The model will be the hybrid of two modules. The first module interprets the relevant abbreviatory elements embedded in NSC reports by referring to a comprehensive database that we have made of nearly 950 abbreviations for degree titles used by American postsecondary educators. The second module is a combination of feature classification and text mining modeled with CNN-BiLSTM, which is preceded by several steps of heavy pre-processing. The model proposed in this paper was trained with four multi-label datasets of different grades of resolution and returned 97.83\% accuracy with the most sophisticated dataset. Such a thorough classification of degree levels will provide insights into the modeling patterns of student success and mobility. To date, such a classification strategy has not been attempted except using manual methods and simple text parsing logic.