Multi-Scale Toolbox: An Automated ArcGIS Tool for Evaluating Pupil–Teacher Ratios in U.S. Public School Districts

Round 1

Reviewer 1 Report

The automated python-based query tool offers monitoring of teachers’ and students’ numbers besides quantify the correlation.

 

Please add citation(s) to the text “Over the five year period from 2011 to 2016 the proportion of teachers who were not fully certified increased from 8.4% to 8.8%, teachers who had not taken a traditional route into teaching increased from 14.3% to 17.1%, teachers who…..”

 

Page 5: The dependent variable is the tendency of the student-teacher ratio. There is only one dependent variable.  How many dependent variables? Please check “…output feature class, unique ID, independent variables, dependent variables,….”

 

Refer to “3.3” in “…We found that out of the 25 variables…”. Is it “23” or “25”?

Comments for author File: Comments.pdf

Author Response

Dear Editors,

Thank you so much for giving us the opportunity to revise the manuscript (Manuscript ID: applsci-1958264). Please extend our thanks to the four anonymous reviewers for their valuable suggestions and comments. We have reviewed these comments carefully and have made revisions accordingly.  In addition, we have responded to some comments from the reviewers when appropriate.

 

We revised the introduction, discussion, and conclusion.  Given below is a summary of the revisions and responses to the reviewers’ suggestions and comments.  For your reading convenience, we colored the suggestions and comments from the editors and reviewers in blue.

 

Sincerely,

 

Xiu Wu

Reviewer #1:

The automated python-based query tool offers monitoring of teachers’ and students’ numbers besides quantify the correlation.

 Thanks for your summary and comments. We really appreciate it.

Please add citation(s) to the text “Over the five year period from 2011 to 2016 the proportion of teachers who were not fully certified increased from 8.4% to 8.8%, teachers who had not taken a traditional route into teaching increased from 14.3% to 17.1%, teachers who…..”

Thanks for your suggestions. We added a new citation at the last third line on Page 1 of the new manuscript and rearranged all citation numbers. 

5. Clark, M. A., Isenberg, E., Liu, A. Y., Makowsky, L., & Zukiewicz, M. (2017). Impacts of the Teach for America Investing in Innovation Scale-Up. Revised Final Report. Mathematica Policy Research.

Please see reference 5 on Page 11.

 Page 5: The dependent variable is the tendency of the student-teacher ratio. There is only one dependent variable.  How many dependent variables? Please check “…output feature class, unique ID, independent variables, dependent variables,….”

That is a good point. Thanks for your attention. The dependent variable is the only one. But when we design data structures and define parameters, the dependent variable is treated as one feature in Figure 4. We corrected the new manuscript. Please see the last eighth line on Page 5.

 Refer to “3.3” in “…We found that out of the 25 variables…”. Is it “23” or “25”?

Thanks for your consideration. You are right. It is 23. We corrected it (the last line 4 on page 8).

Author Response File: Author Response.pdf

Reviewer 2 Report

The topic of this manuscript is interesting. And the content is relatively rich, and the tools designed have practical significance. However, there are some shortcomings.

1. The introduction is confused. The introduction does not mention Toolbox or ArcGIS tool.

2. The previous research is unclear.

3. The section 2.1 data is suggested to add the website.

4. The text in Figure 5 is disordered

5. The title of Figure 3 is not uniform

6. What are the limitations of the tools studied in this paper?

Author Response

Reviewer #2:

The topic of this manuscript is interesting. And the content is relatively rich, and the tools designed have practical significance. However, there are some shortcomings.

 Thanks for your comments. We really appreciate it.

1. The introduction is confusing. The introduction does not mention Toolbox or ArcGIS tool.

Thanks for your advice. We mentioned the ArcGIS tool in the research objective (Line 19 on page 2). We mentioned the importance of the tool was to solve the modifiable areal unit problem (MAUP). Please see the first paragraph on page 2.

2. The previous research is unclear.

This is a constructive statement. The previous research includes previous GIS toolbox research and existing system research. We added previous GIS toolbox research contents to the last paragraph in the introduction section (from the last sixth row on page 2 to the first fifth row on page 3). The contents are as follows:

     “Owing to the advantage of visualizing the distribution of ecological diversity across geographic space, the GIS toolbox was well documented in the research of natural resource utilization and protection [26-31], even extended to molecular research. For instance, the genetic landscapes GIS toolbox could be used to explore associations between patterns of genetic diversity and geographic features [32]. Using the GIS toolbox to explore the association between the pupil-teachers ratio and factors of social-economic, and political growth is muti-disciplinary research. In Web of Science, there was little literature relevant to this topic, except for Dobesova Zdena (2012), who proposed visual programming, which could convert graphic versions of models into Python script for novice programmers to avoid simple mistakes [33]. They mainly took into account the application of Model Builder in Arc GIS software, not mentioned python scripts to produce the spatial distribution of pupil-teacher distribution.”

3. The section 2.1 data is suggested to add the website.

Thanks for your attention. We added the website in the first line of section 2.1 data.

4. The text in Figure 5 is disordered

Thanks for your suggestions. We revised the text in Figure 5. Please see the last four rows on page 7.

5. The title of Figure 3 is not uniform

A good point. Thanks for your advice. We corrected it. Please see Figure 3 in page 5.

6. What are the limitations of the tools studied in this paper?

This is a significant suggestion. We added limitation content at the last paragraph on Page 10 and revised the discussion section. The added contents are as follows:

“Albeit our ArcToobox is accomplished, several limitations should be concerned. First, the proposed toolset is only appliable in the context of the GIS environment, which means users are required to have open access to QGIS or a valid Spatial Analyst license of the ArcGIS pro (ArcMap) software that could run the ArcPy site package. Second, the tool is limited to Python-installed packages. In other words, users are required to install the seaborn and the sci-kit-learn of Python package and then cloned them in the GIS environment before running the ArcToolbox. Third, Python skill is required. The CCD data are big data from 1986 to 2021. Each year CCD data contain school districts and schools’ records in the U.S. If users don’t have Python experience, once spatial data processing is problematic, the ArcToolbox is insignificant. At last, interface limitations inevitably exist. For instance, the format of the Spatial weighted matrix is an SWM file that could not automatically be opened. It is better to use the function of converting the Spatial Weights Matrix to the table in the Spatial Statistics toolbox. With the update of ArcGIS version, those limitations will be eliminated.”

Author Response File: Author Response.pdf

Reviewer 3 Report

The paper applied four 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.  The scientific contribution lies in analysing the results of these tools at the level of pupil-teacher ratio in the United States. In fact, applying tools with different data and analysing their results is not considered as a scientific contribution. The authors should have to add new methods not used before or combine some of these tools with each other to produce a new model.

Please include some justifications for this study in the introductory part as there isn't a motivation there. Rewriting the introduction part is necessary to improve the paper quality. In order to draw in potential readers, the introduction section often concentrates on showcasing the paper's core topic. It is a better location to discuss the difficulties with the pupil-teacher ratio and the issue with US Public School Districts, as well as the current solutions, the suggested method, and its innovation.

Section three (Case study) needs more clarification in the Multiscale Visualization. This case study describes a mixed methods knowledge and skills needs assessment conducted for Public Schools in a different city in the United States. It needs a real assessment to conduct and identify the gap between the knowledge of the pupil-teacher ratio and the issue with US Public School Districts skills to produce a solid analysis result.

The results of the tools is limited and not indicate any real contributions (only in figure 9 and 10 discussed the results with less justifications)

 

Author Response

Reviewer #3:

The paper applied four 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.  The scientific contribution lies in analyzing the results of these tools at the level of pupil-teacher ratio in the United States. In fact, applying tools with different data and analysing their results is not considered as a scientific contribution. The authors should have to add new methods not used before or combine some of these tools with each other to produce a new model.
Thanks for the good suggestions. We fully agree with you, but the journal we submitted focuses on applied research. Practical research is also important for academic development. There is plenty of GIS toolbox research to be published in academic journals. We created different modules in one Arctoolbox to realize function integration in GIS environments. Ostensibly, it combines different tools. Indeed, we synthesized GIS spatial statistics, data processing, and machine learning language in the specific teacher shortage case in the U.S. An automatic toolbox with secondary development based on the ArcPy site package is created to explore the spatial imbalance of pupil-teacher ratio. We added methodology in the 2.4 toolbox design procedure on Page 5.

Please include some justifications for this study in the introductory part as there isn't a motivation there. Rewriting the introduction part is necessary to improve the paper quality. In order to draw in potential readers, the introduction section often concentrates on showcasing the paper's core topic. It is a better location to discuss the difficulties with the pupil-teacher ratio and the issue with US Public School Districts, as well as the current solutions, the suggested method, and its innovation.

That is a great point. Thanks for your comments. We revised the introduction section and added previous research content. Please see page 2.

     “Owing to the advantage of visualizing the distribution of ecological diversity across geographic space, the GIS toolbox was well documented in the research of natural resource utilization and protection [26-31], even extended to molecular research. For instance, the genetic landscapes GIS toolbox could be used to explore associations between patterns of genetic diversity and geographic features [32]. Using the GIS toolbox to explore the association between the pupil-teachers ratio and factors of social-economic, and political growth is muti-disciplinary research. In Web of Science, there was little literature relevant to this topic, except for Dobesova Zdena (2012), who proposed visual programming, which could convert graphic versions of models into Python script for novice programmers to avoid simple mistakes [33]. They mainly took into account the application of Model Builder in Arc GIS software, not mentioned python scripts to produce the spatial distribution of pupil-teacher distribution.”

Section three (Case study) needs more clarification in the Multiscale Visualization. This case study describes a mixed methods knowledge and skills needs assessment conducted for Public Schools in a different city in the United States. It needs a real assessment to conduct and identify the gap between the knowledge of the pupil-teacher ratio and the issue with US Public School Districts' skills to produce a solid analysis result.

Thanks for your significant suggestions. We revised the Multiscale visualization part in the case study part. Please see Page 7.

“The pupil-teacher ratio is displayed at the state level in Figure 5. States in the U.S are classified into five classes with the dark red color states having the highest ratios and white states having the lowest ratios. In light of Figure 5, we could see that the high pupil-teacher ratios were located in New Hampshire, Indiana, and Washington while the low ones were distributed in Oklahoma, Kansas, and Missouri. Interestingly, when we examined the same data at the county level, it was shown that the highest pupil-teacher ratios were narrowed down to a couple of counties such as Pierce and Thurston counties in Washington, and Robertson and Montgomery counties in Tennessee in Figure 6, This indicated that student-teacher ratio at the county level is more accurate than that at the state level."

The results of the tools is limited and not indicate any real contributions (only in figure 9 and 10 discussed the results with less justifications)

That is a good statement. The tools are designed to connect the U.S census tract and generate a channel for exploring the association between CCD data and other social-economic, and environmental Data at the state and county levels. Figures 9 and 10 used cross-validation to justify the correction of the results.

Author Response File: Author Response.pdf

Reviewer 4 Report

The document includes the development of a specific software applied to the educational system administration.
It includes an exhaustive review of the literature and a fair justification.
The development of the article is correct.
The case study is adequate and sufficiently well explained to know the program's features.
However, the software is not about a new topic, although it resolves some weaknesses of other programs. In addition, although the topic where it is applied might be of interest, it does not cover the whole picture of the problems of the educational system, and instead focuses only on a descriptive aspect.
In any case, the study deserves to be taken into account.
It is recommended:
- Put the objective of the study (This project aims to create an automated ...) at the end of the epigraph.
- There are minor typos.

Author Response

Reviewer #4:

The document includes the development of a specific software applied to the educational system administration. It includes an exhaustive review of the literature and a fair justification. The development of the article is correct. The case study is adequate and sufficiently well explained to know the program's features.

Thanks for your comments. We fully agree with you.

However, the software is not about a new topic, although it resolves some weaknesses of other programs. In addition, although the topic where it is applied might be of interest, it does not cover the whole picture of the problems of the educational system, and instead focuses only on a descriptive aspect.

An excellent point. ArcGIS Pro is a mature tool. The GIS toolbox contains a powerful set of tools that perform the most fundamental GIS operations. With the tools in this toolbox, you can perform overlays, create buffers, calculate statistics, perform proximity analysis, and much more. Education spatial static is a kind of new topic due to different local education policies. It will facilitate educational resource equity and education justice by spatial static analysis in multiple dimensions.

In any case, the study deserves to be taken into account. It is recommended:
- Put the objective of the study (This project aims to create an automated ...) at the end of the epigraph.
Thanks for your kind comments. We revised the introduction section. We mentioned the ArcGIS tool in the research objective at the bottom of the first paragraph on Page 2. We mentioned the importance of the tool was to solve the modifiable areal unit problem (MAUP). Please see the first paragraph on page 2.

“This shortage has an impact on the quality of teaching that occurs within these institutions and dates back to before the pandemic started. The Economic Policy Institute published a report entailing that during the 2017-2018 school year there was an estimated 110,000 teachers demanded from the education system that was not being met by the current supply [7]. This was especially the case within low-poverty areas in which education should be taking precedence. The shortage follows after the COVID pandemic caused mass furloughing. The Learning Policy Institute details the US Department of Education’s findings that “48 states and Washington, DC, reported having shortages of special education teachers; 43 states and DC reported math teacher shortages, and 41 states and DC had shortages of science teachers” [8]. Therefore, improving access to public teachers in underserved areas is a significant avenue for enhancing the quality of public education. In this research, pupil-teacher ratios are assessed using different ranking dimensions nationally at multiple scales ranging from the individual county and state. . This is relevant to the modifiable areal unit problem (MAUP), which means analytical results were sensitive to the definition and aggregation of geographic units [9]. To solve this issue, the pupil-teacher ratio must be analyzed at varying levels of scale and aggregation [10]. This research aims to create an automated python-based query toolbox to calculate pupil-teacher ratios and produce distribution maps of these ratios at different scales. An effective Public School District Teacher-Student Match Tool (PTMT) could be automated to quantify the correlation between teachers and students using muti-scalar statistical analysis.”

- There are minor typos.

   Thanks for your attention. We corrected “Module 1”,” Module 2”, “Module 3”, “Module 4” etc. typos in the new manuscript. Please see page 6.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

The authors have tried to answer the previous comments and have been somewhat successful. I think the general shape of the paper is currently acceptable.