Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/6296
Title: Automated Collaboration Assessment Using Behavioral Analytics
Authors: Alozie, Nonye
Dhamija, Svati
McBride, Elizabeth
Tamrakar, Amir
Keywords: Design
Issue Date: Jun-2020
Publisher: International Society of the Learning Sciences (ISLS)
Citation: Alozie, N., Dhamija, S., McBride, E., & Tamrakar, A. (2020). Automated Collaboration Assessment Using Behavioral Analytics. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 2 (pp. 1071-1078). Nashville, Tennessee: International Society of the Learning Sciences.
Abstract: The 21st century skills and STEM learning standards include collaboration as a necessary learning skill in K-12 science education. To support the development of collaboration skills among students, it is important to assess and support students’ proficiency in collaboration. We present the process of developing a tool that assesses collaboration quality based on behavioral communication at individual and group levels. The assessment tool uses behavior analytics comprised of multistage machine learning models built on an intricate collaboration conceptual model and coding scheme. Our collaboration conceptual model shows how layers of behavioral cues contribute to collaboration and serves as the foundation of an automated assessment tool for collaboration. We present initial findings that show reliability between our assessment of behavioral interactions with and without speech. An automated collaboration assessment tool will give teachers information about student collaboration and help inform instruction that will guide and support students’ collaboration skill development.
URI: https://doi.dx.org/10.22318/icls2020.1071
https://repository.isls.org//handle/1/6296
Appears in Collections:ICLS 2020

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