Multi-Scale Toolbox: An Automated ArcGIS Tool for Evaluating Pupil–Teacher Ratios in U.S. Public School Districts
Source abstract
Abstract
Due to the teacher shortage in the U.S., an automatic toolbox with secondary development based on the ArcPy package was created to explore the spatial imbalance of the pupil–teacher ratio. It consists of four tools (or toolsets) for multi-scale spatial visualization, a sensitivity analysis with a heat map, the ordinary least squares regression with spatial autocorrelation, and the random forest tree regression. This study demonstrated the application of the toolset in the evaluation of educational resource spatial misallocation. Firstly, multi-scale analysis results showed that, the loss of teachers was mainly distributed in Oregon, Nevada, Arizona, and California from the state level, while it focused on such counties as Terrebonne Parish, Concordia Parish, and Bienville Parish in Louisiana in the county level. Secondly, it was found through heatmap sensitivity analysis that pupil–teacher ratios were highly related to low levels of student support services staff, free lunch programs, and low levels of local education agency (LEA) administrators. Then, the OLS tool was used to automatically calculate the spatial weighted matrix, the Moran I, R2, and AICC indices, AdjR2, F-Stat, F-Prob, and the Wald statistic, which showed whether the model was significant or not. This was followed by random forest tree regression modeling, which found that the LEA administrative support staff and the totally free lunch number highly impacted pupil–teacher ratios. Besides, the designed tool provided ribbons for the Common Core of Data (CCD) to link to other data sources.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: School Choice and Performance · Spatial and Panel Data Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Geography and Environmental Studies, Texas State University, San Marcos, TX 78666, USA
School of Resource and Environmental Science, Wuhan University, Wuhan 430070, China
Liberal Arts and Science Academy, Austin, TX 78721, USA
School of Public Administration, China University of Geosciences, Wuhan 430074, China
References (43)
Show source-provided references
1. Understanding teacher shortages: An analysis of teacher supply and demand in the United States10.14507/epaa.27.3696 ↗
2. Teacher shortages and cultural mismatch: District and university collaboration for recruiting
3. García, E., Weiss, E., and Economic Policy Institute (2020). Examining the Factors That Play a Role in the Teacher Shortage Crisis: Key Findings from EPI’s “Perfect Storm in the Teacher Labor Market” Series, Economic Policy Institute.
4. Learning Policy Institute (2017). The Role of Principals in Addressing Teacher Shortages. Research Brief, Learning Policy Institute.
5. Clark, M.A., Isenberg, E., Liu, A.Y., Makowsky, L., and Zukiewicz, M. (2017). Impacts of the Teach for America Investing in Innovation Scale-Up. Revised Final Report, Mathematica Policy Research.
6. Hof, J., and Bevers, M. (2000). Spatial Optimization for Managed Ecosystems, Columbia University Press.
7. Carver-Thomas, D. (2022). Teacher Shortages Take Center Stage, Learning Policy Institute. Available online: https://learningpolicyinstitute.org/blog/teacher-shortages-take-center-stage.
8. Garcia, E., and Weiss, E. (2019). The Teacher Shortage is Real, Large and Growing, and Worse than We Thought, Learning Policy Institute. Available online: https://www.epi.org/publication/the-teacher-shortage-is-real-large-and-growing-and-worse-thanwe-thought-the-first-report-in-the-perfect-storm-in-the-teacher-labor-market-series/.
9. The Effects of Data Aggregation in Statistical Analysis10.1111/j.1538-4632.1976.tb00549.x ↗
10. The Modifiable Areal Unit Problem in Multivariate Statistical Analysis10.1068/a231025 ↗
11. Class size10.2307/1602449 ↗
12. Peers, C. (2016). Class Size and Pupil-Teacher Ratios: Where Education and Economics Collide, Information Age Publishing.
13. Pupil-teacher ratio, disciplinary problems, classroom emotional climate, and turnover intention: Evidence from a randomized control trial10.1016/j.tate.2021.103415 ↗
14. Beyond class size reduction: Towards more flexible ways of implementing a reduced pupil–teacher ratio10.1016/j.ijer.2018.10.008 ↗
15. Retracted: Multilevel Predictors of Math Classroom Climate: A Comparison Study of Student and Teacher Perceptions10.1111/jora.12153 ↗
16. A Multilevel Perspective on the Climate of Bullying: Discrepancies among Students, School Staff, and Parents10.1080/15388220.2010.539164 ↗
17. Educational Sequelae of High School Misbehavior10.3200/JOER.101.5.259-274 ↗
18. Are test-based policies in the schools associated with burnout and bullying? A study of direct and indirect associations with pupil-teacher ratio as a moderator10.1016/j.tate.2022.103670 ↗
19. Teacher Attrition and Retention: A Meta-Analytic and Narrative Review of the Research10.3102/0034654308321455 ↗
20. Exploring associations between supervisory support, teacher burnout and classroom emotional climate: The moderating role of pupil teacher ratio10.1080/01443410.2019.1673881 ↗
21. Complex Systems View of Educational Policy Research10.1126/science.1195153 ↗
22. The Modular Design of an English Pronunciation Level Evaluation System Based on Machine Learning
23. Jung, A. (2022). Machine Learning: The Basics, Springer.10.1007/978-981-16-8193-6 ↗
24. Amin, M.N., Iqbal, M., Ashfaq, M., Salami, B.A., Khan, K., Faraz, M.I., Alabdullah, A.A., and Jalal, F.E. (2022). Prediction of Strength and CBR Characteristics of Chemically Stabilized Coal Gangue: ANN and Random Forest Tree Approach. Materials, 15.10.3390/ma15124330 ↗
25. A comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: The case of the Seoul metropolitan area10.1080/13658816.2021.1887490 ↗
26. Flood Modeling and Simulation Using HEC-HMS/HEC-GeoHMS and GIS Tools for River Sindh-NW Himalayas
27. Prediction of waterlogged zones under heavy rainfall conditions using machine learning and GIS tools: A case study of Mumbai
28. Shen, D., and Dong, S. (2022). Transition of Urban Morphology in the Mountainous Areas Since Early-Modern Times from the Perspective of Urban Historic Landscape—A GIS Tools and Historical Map Translation Approach. Sustainability, 14.10.3390/su141912896 ↗
29. Dawidowicz, A., and Dudzińska, M. (2022). The Potential of GIS Tools for Diagnosing the SFS of Multi-Family Housing towards Friendly Cities—A Case Study of the EU Member State of Poland. Sustainability, 14.10.3390/su14116642 ↗
30. Comparison of ANN model and GIS tools for delineation of groundwater potential zones, Fincha Catchment, Abay Basin, Ethiopia10.1080/10106049.2021.1946171 ↗
31. Potential Soil Erosion Modeled with RUSLE Approach and Geospatial Techniques (GIS Tools and Remote Sensing) in Oued Joumouaa Watershed (Western Prerif-Morocco)10.46717/igj.55.2B.5Ms-2022-08-21 ↗
32. Genetic landscapes GIS Toolbox: Tools to map patterns of genetic divergence and diversity10.1111/j.1755-0998.2010.02904.x ↗
33. Visual Programming for Novice Programmers in Geoinformatics
34. Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR)10.1007/s11356-021-13653-8 ↗
35. Wu, X., Denise, B.-B., Zhan, F., and Zhang, J. (2022). Determining Association between Lung Cancer Mortality Worldwide and Risk Factors Using Fuzzy Inference Modeling and Random Forest Modeling. Int. J. Environ. Res. Public Health, 19.10.3390/ijerph192114161 ↗
36. SDMtoolbox 2.0: The next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses10.7717/peerj.4095 ↗
37. Batelaan, O., Zhong-Min, W., and De Smedt, F. (1996). An Adaptive GIS Toolbox for Hydrological Modelling, IAHS Publication.
38. Gravity model toolbox: An automated and open-source ArcGIS tool to build and prioritize ecological corridors in urban landscapes
39. Marwedel, P. (2021). Embedded System Design: Embedded Systems Foundations of Cyber-Physical Systems, and the Internet of Things, Springer. [4th ed.].10.1007/978-3-030-60910-8 ↗
40. Deambi, S. (2016). Photovoltaic System Design: Procedures, Tools and Applications, CRC Press.10.1201/9781315372181 ↗
41. Dennis, A., Wixom, B.H., and Roth, R.M. (2012). System Analysis and Design, John Wiley. [5th ed.].
42. Growth trends for selected occupations considered at risk from automation
43. Can public subsidy on education reduce wage inequality in the presence of automation?