This paper investigates the use of linguistic features extracted from the application essay so students enrolled in a university academic program for their retention pattern prediction. Three sets of linguistic features are generated from text analysis: (1) latent Dirichlet allocation (LDA) based topic modeling with a variety of topic number, (2) Linguistic Inquiry and Word Count (LIWC), and (3) part-of-speech (POS) distribution. Various classification experiments are implemented to evaluate the prediction performance of student retention patterns from these three feature sets and their combinations. The results show that the POS distribution features yield the best prediction performance among these three, while neither the LDA features nor ensemble methods improves predictive performance, which is contrary to admission experts’ manual analysis methods in the conventional admission process.
Presented at International Conference on Machine Learning and Applications IEEE ICMLA 2017 Paper ID #379 | Cancun, Mexico, December 18-21, 2017
M. Ogihara and G. Ren, Student Retention Pattern Prediction Employing Linguistic Features Extracted from Admission Application Essays, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, 2017, pp. 532-539, doi: 10.1109/ICMLA.2017.0-106.https://ieeexplore.ieee.org/document/8260686
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