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LearningViz: A Dashboard for Visualizing, Analyzing and Closing Learning Performance Gaps--A Case Study Approach

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Title: LearningViz: A Dashboard for Visualizing, Analyzing and Closing Learning Performance Gaps--A Case Study Approach
Language: English
Authors: Bo Pei (ORCID 0000-0002-6328-6929), Ying Cheng, Alex Ambrose, Eva Dziadula, Wanli Xing, Jie Lu
Source: Smart Learning Environments. 2024 11.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 25
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Learning Analytics, Learning Management Systems, Computer Uses in Education, Educational Technology, Technology Uses in Education, Computer Managed Instruction, Teaching Methods, Visual Aids, Student Evaluation, Higher Education, Data Analysis
DOI: 10.1186/s40561-024-00346-1
ISSN: 2196-7091
Abstract: The availability of large-scale learning data presents unprecedented opportunities for investigating student learning processes. However, it is challenging for instructors to fully make sense of this data and effectively support their teaching practices. This study introduces LearningViz, an interactive learning analytics dashboard to help instructors identify, analyze, and close performance gaps among students in their classes. In this dashboard, we incorporated three modules to enhance human and computer interactions for better supporting the teaching practices: the Student Overall Performance Analysis Module, which provides a comprehensive understanding of students' learning in the course; the Student Group Performance Analysis Module, which examines performance gaps across different groups and identifies factors contributing to these gaps; and the Final Exam Item Analysis Module, which evaluates the quality of exam questions and identifies strategies for closing performance gaps. The overall design of the platform follows a user-centered approach, integrating data analysis with various visualization strategies in a unified platform. A case study is then conducted to highlight the effectiveness of LearningViz in supporting instructors analyzing students' learning patterns and associated factors impacting learning performance. We further conduct a usability test with several domain experts, to evaluate the usefulness and effectiveness of this platform in supporting the teaching practices. Our findings underscore the platform's ability to support instructors in detecting performance gaps among students, investigating influential factors, evaluating assessment quality and implementing targeted instructional strategies for closing performance gaps.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1451507
Database: ERIC
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  Data: <searchLink fieldCode="AR" term="%22Bo+Pei%22">Bo Pei</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-6328-6929">0000-0002-6328-6929</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ying+Cheng%22">Ying Cheng</searchLink><br /><searchLink fieldCode="AR" term="%22Alex+Ambrose%22">Alex Ambrose</searchLink><br /><searchLink fieldCode="AR" term="%22Eva+Dziadula%22">Eva Dziadula</searchLink><br /><searchLink fieldCode="AR" term="%22Wanli+Xing%22">Wanli Xing</searchLink><br /><searchLink fieldCode="AR" term="%22Jie+Lu%22">Jie Lu</searchLink>
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: The availability of large-scale learning data presents unprecedented opportunities for investigating student learning processes. However, it is challenging for instructors to fully make sense of this data and effectively support their teaching practices. This study introduces LearningViz, an interactive learning analytics dashboard to help instructors identify, analyze, and close performance gaps among students in their classes. In this dashboard, we incorporated three modules to enhance human and computer interactions for better supporting the teaching practices: the Student Overall Performance Analysis Module, which provides a comprehensive understanding of students' learning in the course; the Student Group Performance Analysis Module, which examines performance gaps across different groups and identifies factors contributing to these gaps; and the Final Exam Item Analysis Module, which evaluates the quality of exam questions and identifies strategies for closing performance gaps. The overall design of the platform follows a user-centered approach, integrating data analysis with various visualization strategies in a unified platform. A case study is then conducted to highlight the effectiveness of LearningViz in supporting instructors analyzing students' learning patterns and associated factors impacting learning performance. We further conduct a usability test with several domain experts, to evaluate the usefulness and effectiveness of this platform in supporting the teaching practices. Our findings underscore the platform's ability to support instructors in detecting performance gaps among students, investigating influential factors, evaluating assessment quality and implementing targeted instructional strategies for closing performance gaps.
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