CRISP-NET: Integration of the CRISP-DM Model with Network Analysis

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The reviewed paper introduces a methodological integration of CRISP-DM and network analysis, guided by Situational Method Engineering (SME), and applies it within an educational data mining context. The subject matter is original and relevant, especially for data science projects requiring a combination of structured data analysis and relational exploration.
Strengths of the work include its clear explanation of both CRISP-DM and network analysis frameworks, a thorough theoretical background, and an explicit effort to connect different methodological paradigms. The application of the process in an academic setting, supported by surveys and practical outputs, demonstrates initial feasibility and practical relevance.
However, the article's weaknesses are notable. The research design and methods section, lack important details, especially regarding data processing, the criteria for component adaptation, and the operationalization of network metrics. The integration steps sometimes appear superficial and would benefit from deeper justification and critical analysis. The results are presented with many procedural details but relatively little critical interpretation, making it harder to judge the real impact and generalizability of the proposed approach. Conclusions, would be stronger if they more clearly addressed limitations and possible future improvements. Overall, the quality of presentation could be improved by streamlining repetitive explanations and clarifying the core methodological advances. 

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

In this paper, the authors proposed a method based on the Cross-Industry Standard Process for Data Mining (CRISP-DM) and network analysis that helps to guide data analysis. The paper is interesting and useful for people who conduct data analysis. However, I would like the authors to address the following issues to make the paper more useful.

1. Fix Figure ?? (page 11)
2. Fonts of the figures are small or not clear
3. Section 4.5 is about the evaluation of the proposed method. However, this section just includes some survey data about the method. Based on this section, I still do not see whether the method is effective or not. Instead, I would like to see a real case study of applying the proposed method.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have thoroughly addressed the key points raised in the previous review, providing concrete methodological clarifications and improving the overall presentation of their work.

The revised manuscript now offers clear details concerning data preparation. Essential network metrics are well defined and linked to subsequent modeling stages. This level of transparency significantly enhances the reproducibility and robustness of the proposed framework. The operational assembly and integration steps have been considerably expanded. The manuscript now clearly demonstrates how network analysis tasks are embedded within CRISP-DM. The added discussion of nonlinear relationships and central nodes in the case study provides a much deeper interpretation of the impact and generalizability of the results. The revised conclusions now openly address key limitations as well as technical implementation challenges. The authors propose specific future directions, evidencing critical reflection and forward-looking planning.

Overall, the authors have made substantial improvements in response to my feedback, resulting in a significant improvements.

Reviewer 2 Report

Comments and Suggestions for Authors

I am fine with the revision.