Comparison of Machine Learning Methods for Multi-label Classification of Nursing Education and Licensure Exam Questions

John Langton, Krishna Srihasam, Junlin Jiang


Abstract
In this paper, we evaluate several machine learning methods for multi-label classification of text questions. Every nursing student in the United States must pass the National Council Licensure Examination (NCLEX) to begin professional practice. NCLEX defines a number of competencies on which students are evaluated. By labeling test questions with NCLEX competencies, we can score students according to their performance in each competency. This information helps instructors measure how prepared students are for the NCLEX, as well as which competencies they may need help with. A key challenge is that questions may be related to more than one competency. Labeling questions with NCLEX competencies, therefore, equates to a multi-label, text classification problem where each competency is a label. Here we present an evaluation of several methods to support this use case along with a proposed approach. While our work is grounded in the nursing education domain, the methods described here can be used for any multi-label, text classification use case.
Anthology ID:
2020.clinicalnlp-1.10
Volume:
Proceedings of the 3rd Clinical Natural Language Processing Workshop
Month:
November
Year:
2020
Address:
Online
Venues:
ClinicalNLP | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
85–93
Language:
URL:
https://www.aclweb.org/anthology/2020.clinicalnlp-1.10
DOI:
10.18653/v1/2020.clinicalnlp-1.10
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.clinicalnlp-1.10.pdf