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Title: Application of AutoML in the Automated Coding of Educational Discourse Data
Authors: Lee, Seung
Gui, Xiaofan
Hamilton, Eric
Keywords: Scale
Issue Date: Jun-2020
Publisher: International Society of the Learning Sciences (ISLS)
Citation: Lee, S., Gui, X., & Hamilton, E. (2020). Application of AutoML in the Automated Coding of Educational Discourse Data. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 5 (pp. 2597-2600). Nashville, Tennessee: International Society of the Learning Sciences.
Abstract: This paper examines the potential for using AutoML techniques to develop automated classification models for coding educational discourse data. In particular, it provides a direct comparison between automated classifiers developed through rule-based and AutoML approaches. Through a presentation of an applied example, the paper offers insights on the challenges and strategies associated with utilizing AutoML in the automation of discourse coding. Results indicate sufficient levels of reliability and validity for classification models developed through both approaches. These findings suggest that AutoML approaches can perform at a level similar to rule-based approaches in the automated coding of discourse data.
Appears in Collections:ICLS 2020

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