2022 Articles
Towards Hierarchical Cluster Analysis Heatmaps as Visual Data Analysis of Entire Student Cohort Longitudinal Trajectories and Outcomes from Grade 9 through College
Research on data use and school Early Warning Systems (EWS) notes a central practice of researchers and practitioners is to search for patterns in student data to predict outcomes so schools can support success when students experience challenges. 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 enrolled subject and year, visualizing 6,728,920 individual datapoints. We provide both the open access code in R and an open-access online tool allowing anyone to upload their data and create a HCA heatmap, providing support for visual data analytic and data science practice for both education researchers and schooling organizations.
Keywords: cluster analysis, heatmap, early warning indicator, early warning system, data use, education data mining, education data science, visual data analytics, longitudinal data, grades, dropout, high school, post-secondary, degree, STEM
Subjects
Files
-
Bowers et al 2022 Towards Hierarchical Cluster Analysis Heatmaps.pdf
application/pdf
1000 KB
Download File
Also Published In
- Title
- The High School Journal
- DOI
- https://doi.org/10.1353/hsj.2022.a906700
More About This Work
- Academic Units
- Education Leadership
- Published Here
- February 7, 2024
Notes
This document is a preprint of this manuscript published in the High School Journal. Citation:
Bowers, A.J., Zhao, Y., & Ho, E. (2022). Towards Hierarchical Cluster Analysis Heatmaps as Visual Data Analysis of Entire Student Cohort Longitudinal Trajectories and Outcomes from Grade 9 through College. The High School Journal, 106(1), 5-36. https://doi.org/10.1353/hsj.2022.a906700.
This material is based upon work supported by the National Science Foundation under Grant No. NSF IIS-1546653. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
