4 research outputs found
Decoding the Alphabet Soup of Degrees in the United States Postsecondary Education System Through Hybrid Method: Database and Text Mining
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.Comment: 18 Pages, 8 figure
Students Success Modeling: Most Important Factors
The importance of retention rate for higher education institutions has
encouraged data analysts to present various methods to predict at-risk
students. The present study, motivated by the same encouragement, proposes a
deep learning model trained with 121 features of diverse categories extracted
or engineered out of the records of 60,822 postsecondary students. The model
undertakes to identify students likely to graduate, the ones likely to transfer
to a different school, and the ones likely to drop out and leave their higher
education unfinished. This study undertakes to adjust its predictive methods
for different stages of curricular progress of students. The temporal aspects
introduced for this purpose are accounted for by incorporating layers of LSTM
in the model. Our experiments demonstrate that distinguishing between
to-be-graduate and at-risk students is reasonably achievable in the earliest
stages, and then it rapidly improves, but the resolution within the latter
category (dropout vs. transfer) depends on data accumulated over time. However,
the model remarkably foresees the fate of students who stay in the school for
three years. The model is also assigned to present the weightiest features in
the procedure of prediction, both on institutional and student levels. A large,
diverse sample size along with the investigation of more than one hundred
extracted or engineered features in our study provide new insights into
variables that affect students success, predict dropouts with reasonable
accuracy, and shed light on the less investigated issue of transfer between
colleges. More importantly, by providing individual-level predictions (as
opposed to school-level predictions) and addressing the outcomes of transfers,
this study improves the use of ML in the prediction of educational outcomes.Comment: 15 pages, 17 figures, 1 apendi