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[# PeerJ Staff Note - this decision was reviewed and approved by Claudio Ardagna, a PeerJ Section Editor covering this Section #]
Applied: if possible, omit abbreviations in the summary.
Applied: Although the introduction states the two objectives to be worked on and answered during the paper, the abstract does not clearly state the objectives.
Applied: The methodology and results applied in the research are very good; however, the conclusions can be further deepened with respect to the results obtained. It should be answered according to the proposed objectives and the different tools of GIS and spatial autocorrelation analysis, especially.
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Overall, the manuscript is written in clear, professional English. However, some areas could use a little polishing for clarity.
- Lines 23, 77, 121, and 128 contain expressions that might confuse readers. It would be beneficial to consult with a colleague who is skilled in English or to use a professional editing service to refine these sections.
The introduction lays out a solid foundation regarding public attention in special education, but doesn't delve deep enough into the knowledge gap being explored.
Consider broadening the discussion in lines 57-86 to offer a more thorough rationale for the study.
- For Figure 3 (Yearly Public Attention): It needs a clearer legend explaining data sources and the significance of the peaks seen in June. Adding annotations to point out specific events influencing these peaks would be helpful.
- For Figure 4 (Monthly Public Attention): The x-axis labels are hard to read. Increasing the font size or rotating the labels would improve legibility. Using different colors for each year could help distinguish trends better.
The research question is clearly defined and pertinent, emphasizing the optimization of course assignments using NLP techniques. Nonetheless, the authors should clearly state how their research addresses the gaps identified in the current literature.
The methods section effectively outlines the GIS and SARIMA-LSTM hybrid model. However, it would strengthen the argument to clarify the reasoning behind choosing certain models over others. Backing up this choice with citations from previous studies could be helpful.
The assessment of the forecasting model's performance is thorough. Including a brief commentary on the strengths and weaknesses of each method would help readers grasp the implications of the findings.
- In the Methods section (lines 203-208), clarify the rationale behind selecting specific tools (Excel, SPSS, ArcGIS, GeoDa, Python). Offering a brief justification for each tool’s role in your analysis would help readers understand their importance.
- Make sure terms like SARIMA and LSTM are defined when they’re first introduced (lines 271-272) to help those who may not be familiar with these concepts.
The findings show that the SARIMA-LSTM model significantly outperforms others. However, the manuscript should acknowledge any possible limitations of the data used, such as sample size or diversity, which could influence how broadly the findings apply.
The statistical analysis seems solid. Adding more detailed explanations of the metrics (like MAPE, RMSE, DA, R²) and detailing their significance to the study would improve clarity. It's also vital to link these metrics to practical outcomes in forecasting public attention.
- In the Discussion section (lines 583-631), delve deeper into how the findings might shape future educational policies. Specifically, discuss how the identified trends in public attention could influence resource allocation or policy initiatives.
This manuscript provides a fresh perspective on analyzing public attention by harnessing GIS and hybrid forecasting models. The combination of big data analytics with sophisticated modeling techniques showcases the authors' solid grasp of the field.
The main issue lies in the clarity of language in some parts, which could impede understanding. Additionally, while the results are promising, a more thorough discussion on how the findings could impact educational institutions would be beneficial.
- Line 23: Modify “the current phrasing makes comprehension difficult” to “the current phrasing may hinder comprehension.”
- Line 77: Amend “the results suggested public attention” to “the results suggest that public attention.”
- Line 121: Revise “the integration of big data analytics, spatiotemporal modelling, and hybrid forecasting provides a robust framework” to “the integration of big data analytics, spatiotemporal modeling, and hybrid forecasting provides a robust framework.” (Consistency in spelling - 'modeling' vs 'modelling')
- Line 128: Update “the implications of the findings illustrate a framework to effectively improve responsiveness to education policies” to “the implications of the findings illustrate a framework for effectively improving responsiveness to educational policies.”
• Refine the language in the previously highlighted sections.
• Elaborate on the knowledge gap within the introduction.
• Clarify why particular models were selected in the methodology.
• Add more detailed explanations of the evaluation metrics.
if possible, omit abbreviations in the summary.
Although the introduction states the two objectives to be worked on and answered during the paper, the abstract does not clearly state the objectives.
The methodology and results applied in the research are very good, however, the conclusions can be further deepened with respect to the results obtained. It should be answered according to the proposed objectives and the different tools of GIS and spatial autocorrelation analysis especially.
Very good work and excellent research, congratulations.
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