Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/73426

Item-level learning analytics: Ensuring quality in an online French course

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Title:Item-level learning analytics: Ensuring quality in an online French course
Authors:Youngs, Bonnie L.
Keywords:Online
Learning
Analytics
Exploratory
Date Issued:12 Feb 2021
Publisher:University of Hawaii National Foreign Language Resource Center
Center for Language & Technology
(co-sponsored by Center for Open Educational Resources and Language Learning, University of Texas at Austin)
Citation:Youngs, B. L. (2021). Item-level learning analytics: Ensuring quality in an online French course. Language Learning & Technology, 25(1), 73–91. https://hdl.handle.net/10125/73426
Abstract:Learning analytics (LA) offer benefits and challenges for online learning, but prior to collecting data on high-stakes summative assessments as proof of student learning, LA researchers should engage instructors as partners to ensure the quality of course materials through the formative evaluation of individual items (Bienkowski et al., 2012; Dyckhoff et al., 2013; Mantra, 2019; van Leeuwen, 2015). This exploratory study describes a visualization tool that provides actionable data for early intervention with students, and actionable data highlighting odd patterns in student responses (Chatti et al., 2012; Gibson & de Freitas, 2016; Morgenthaler, 2009; Pei et al., 2017), thus allowing instructors to make full use of their teaching skillset in the online environment as they would in a traditional classroom (Davis & Varma, 2008; Dunbar, et al., 2014; Grossman & Thompson, 2008; Lockyer et al., 2013). To answer research questions related to the value of learning analytics and their use in making informed decisions about student learning, a visualization tool was developed for and piloted in an online French course. The findings suggest that using this tool can lead not only to intervention with low- achieving students but can also determine if students struggle due to poor course materials.
URI:http://hdl.handle.net/10125/73426
ISSN:1094-3501
Volume:25
Issue/Number:1
Appears in Collections: Volume 25 Number 1, February 2021 Special Issue: Big Data in Language Education & Research


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