Toward Hierarchical Cluster Analysis Heatmaps as Visual Data Analysis of Entire Student Cohort Longitudinal Trajectories and Outcomes From Grade 9 Through College

• In Event: New Pathways: Breaking Ground on Pioneering Data Use Practices for Education

Abstract

Research on data use and school Early Warning Systems note a central practice of researchers and practitioners is to search for patterns in student data to predict outcomes such that schools then support success when students experience challenge. Yet, the domain lacks a means to visualize the rich longitudinal data that schools collect. Here, we use visual data analytic hierarchical cluster analysis (HCA) heatmaps to pattern and visualize entire longitudinal grading histories of a national sample of n=14,290 students from grade 9 to college in every course, subject, and year, visualizing 6,728,920 individual datapoints. We provide an online application allowing anyone to upload their data and create a HCA heatmap providing support for visual data analytic data science practice.

Authors

• Alex J. Bowers, Teachers College, Columbia University

• Yihan Zhao, Teachers College, Columbia University

• Eric Ho, University of California - Los Angeles