Student retention pattern prediction employing linguistic features extracted from admission application essays

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper investigates the use of linguistic features extracted from the application essays of 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 numbers, (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 processes.

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
EditorsXuewen Chen, Bo Luo, Feng Luo, Vasile Palade, M. Arif Wani
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages532-539
Number of pages8
ISBN (Electronic)9781538614174
DOIs
StatePublished - Jan 1 2017
Event16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 - Cancun, Mexico
Duration: Dec 18 2017Dec 21 2017

Publication series

NameProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Volume2017-December

Other

Other16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
CountryMexico
CityCancun
Period12/18/1712/21/17

Keywords

  • application essay
  • educational data mining
  • linguistic features
  • predictive analysis
  • student retention

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

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