Leveraging Visualization and Machine Learning Techniques in Education: A Case Study of K-12 State Assessment Data

Round 1

Reviewer 1 Report

The manuscript deals with data-driven decision-making strategies for the education domain. The practical implementation solution is discussed with web technology and machine learning.

The topic is relevant to the journal, but there are issues for revisions.

• Research motivation and background are somewhat vague. A more specific and definitive Introduction is required.
• A dedicated contribution list is required, in addition to objectives in the Introduction.
• Unlike the Abstract, the correlation between web technology and machine learning is weak in an educational way.
• Why is the dataset used, and why not others? Detailed motivation and its description are required.
• Some of the figures are meaningless and hard to figure out when printed. Reconstruction of figures is required.
• Also, some figures are based on screenshots.
• No specific procedures, flowcharts, algorithms, or pseudocodes for the proposed evaluation.
• Formatting the manuscript is required. No proofreading is performed.
• What are your insights and implications? Detailed discussion is required. The current version of the discussion is too short.
• What are pros and cons of the proposed method for results?
• No detailed comparative analysis is provided with state of the art studies. Does your research add knowledge to existing literature in the field?

None

Author Response

Thank you very much for taking the time to review this manuscript. Please find the detailed responses in the attached file "Reviewer 1 response" and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files.

Author Response File: Author Response.pdf

Reviewer 2 Report

The paper stands out for its practical approach to applying ML and visualization techniques to real-world educational data. It contributes to filling the existing gap in educational research by demonstrating a cost-effective, replicable method for educational agencies to leverage big data for decision-making. The emphasis on practical implementation using publicly available data adds value to the educational domain, particularly in terms of policy-making and instructional improvement.

The methodology is solidly structured around leveraging GeoJSON map files and end-of-year assessment data to create interactive visual representations and predictive analytics. The use of linear and logistic regression models for predicting educational outcomes is well-justified. However, a more detailed exploration of the choice of machine learning algorithms and their comparative effectiveness could enhance the paper's methodological robustness.

The decision to use openly accessible data ensures replicability and encourages wider application of the research findings. The tools and technologies employed (e.g., Python, Jupyter Notebook, Streamlit) are well-suited for the objectives of the study. Future iterations could benefit from exploring additional ML models and visualization tools to enrich the analysis.

The results effectively demonstrate the potential of visualization and ML in providing actionable insights for educational stakeholders. The discussion thoughtfully connects the findings with implications for educational policy and practice, underscoring the value of data-driven decision-making in education. Expanding the discussion to include limitations and potential biases in ML models could further strengthen the paper.

The paper is generally well-written and structured, making complex concepts accessible. However, minor improvements in clarity and editing could enhance its readability. Specifically, refining figures and visualizations for better clarity and detail would improve the paper's communicative effectiveness.

The authors outline a clear path for future research, including exploring additional data sources and ML algorithms. Emphasizing interdisciplinary collaboration with educational researchers and practitioners could also yield fruitful directions for future work, ensuring that the research continues to address practical educational needs.

The paper provides a valuable contribution to the field of educational technology by demonstrating how machine learning and visualization techniques can be applied to educational data to support decision-making. While the research is solid, minor revisions focusing on methodological detail, clarification of visualizations, and exploration of limitations would enhance the paper's impact. The potential for practical application and the emphasis on cost-effectiveness and accessibility make this research particularly relevant for educational stakeholders looking to leverage data analytics for improvement.

The manuscript by Taylor, Gupta, and Jung is generally well-composed with a clear narrative that effectively communicates the research's aims, methodologies, findings, and implications. The use of English is proficient, facilitating understanding of complex technical content and contributing to the scholarly value of the work. However, to elevate the manuscript's overall clarity and professional presentation, a few suggestions are provided below:

1. **Consistency and Precision:** Ensure consistency in terminology and phrasing throughout the document to avoid confusion. For instance, terms like "data-driven decision-making" and "educational data analytics" should be used consistently to refer to the same concepts. Precision in language will also help in clarifying complex ideas more effectively.

2. **Grammatical Refinements:** While the manuscript is largely free of grammatical errors, a thorough proofreading session could further refine sentence structure and grammar, enhancing readability and academic tone.

3. **Technical Jargon and Clarity:** Given the technical nature of the subject matter, it's essential to strike a balance between the use of technical jargon and the need for clarity. Simplifying complex sentences and providing definitions or explanations for specialized terms at their first occurrence can make the content more accessible to readers not specialized in this specific field of educational technology.

4. **Active vs. Passive Voice:** The manuscript occasionally employs passive constructions, which, while not incorrect, can obscure the action's agent and lead to less engaging text. Where appropriate, consider using the active voice to make statements more direct and dynamic.

5. **Transitions and Flow:** Improve transitions between sections and within paragraphs to ensure a smoother flow of ideas. Logical connectors and transitional phrases can help guide the reader through the argumentation more seamlessly.

6. **Editing for Conciseness:** Some sections could benefit from conciseness to strengthen the impact of the findings and arguments. Eliminating redundancies and tightening phrasing can make the manuscript more compelling and easier to digest.

In summary, with minor revisions focused on these aspects of language use, the manuscript's quality of English can be enhanced, ensuring that the research is communicated effectively and professionally to an international audience.

Author Response

Thank you very much for taking the time to review this manuscript. Please find the detailed responses in the attached file "Reviewer2-response" and the corresponding revisions/corrections highlighted/in track changes in the re-submitted files.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The manuscript has been modified for the review.

Thus, I recommend the manuscript for publication. 

None