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Title: Does Active Learning Reduce Human Coding?: A Systematic Comparison of a Neural Network with nCoder
In quantitative ethnography (QE) studies which often involve large da-tasets that cannot be entirely hand-coded by human raters, researchers have used supervised machine learning approaches to develop automated classi-fiers. However, QE researchers are rightly concerned with the amount of human coding that may be required to develop classifiers that achieve the high levels of accuracy that QE studies typically require. In this study, we compare a neural network, a powerful traditional supervised learning ap-proach, with nCoder, an active learning technique commonly used in QE studies, to determine which technique requires the least human coding to produce a sufficiently accurate classifier. To do this, we constructed multi-ple training sets from a large dataset used in prior QE studies and designed a Monte Carlo simulation to test the performance of the two techniques sys-tematically. Our results show that nCoder can achieve high predictive accu-racy with significantly less human-coded data than a neural network.  more » « less
Award ID(s):
Author(s) / Creator(s):
; ; ; ;
Barany, A.; Damsa, C.
Date Published:
Journal Name:
Advances in Quantitative Ethnography: Fourth International Conference, International Conference on Quantitative Ethnography 2022
Medium: X
Sponsoring Org:
National Science Foundation
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