Decoding the Alphabet Soup of Degrees in the United States Postsecondary
  Education System Through Hybrid Method: Database and Text Mining
Sahar Voghoei James Byars John A Miller Khaled Rasheed Hamid A Arabnia
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.