LearningViz: a dashboard for visualizing, analyzing and closing learning performance gaps—a case study approach

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.