Review Guidelines

We are committed to setting a high standard of quality and integrity for the EDM2024 review process. There are two main goals in reviewing:

• To decide fairly whether each paper is worthy of acceptance; and 
• To provide the authors with feedback on how to improve the quality of their research and writing.

With these goals in mind, we request that all reviewers follow the review guidelines below (based on the EDM 2023 guidelines, which in turn were based loosely on the ICLR 2020, EDM 2022, and LAK conference guidelines).

Reviews should include:

• A total of 200-500 words of detailed feedback that give a complete assessment of a submission that contains rationale that explain recommendations and concerns. Please aim to write the kind you would like to receive for your own work. Please avoid very short reviews, they are frustrating for authors and detrimental to the overall review process. 
• The review should include:
  • A brief summary of the paper itself (e.g., the question being addressed, the high level approach used, what was found).
  • A thorough assessment of the submission’s main strengths and weaknesses in making a substantial conceptual, technical, or empirical contribution to educational data mining
  • Where possible, suggestions for improvement should be given
• The following categories and questions are useful to consider in writing your review:
  • Relevance: 
    • Is the submission trying to solve an important educational problem, which is interesting and relevant for the educational data mining community?
    • Does the submission attend to the real-world context, including issues of impact, fairness, and equity?
  • Novelty: 
    • Is there a novel contribution in the submission in relation to previous work in the area?
    • If a replication study is reported, is it clear what is the contribution to knowledge in comparison to the original study?
  • Grounding:  
    • Is the work situated appropriately with respect to the current state of the field, including sufficient coverage of relevant literature?
      
  • Methods: 
    • Are the methods used suitable, well-described and justified with reference to the literature?
    • Does the submission show accepted evidence of rigour in the tradition followed (statistical, computational, qualitative, design)?
  • Results: 
    • Do the claims made have appropriate empirical support?
    • If negative results are presented, have different explanations for the lack of findings been considered?

• Implications
  • Are contributions to theory and/or practice outlined clearly? 
  • Are limitations with respect to data, analysis or framing factors taken into account? 
  • Are potential issues of fairness and equity considered?
• Communication: 
  • Is the submission written clearly for understanding by an interdisciplinary audience?

Ethics and Reproducibility

The review form provides a separate field to indicate any issues that you would like the authors to cover. In particular, please consider the ethics and reproducibility checklist in the “Instructions for authors” website: https://educationaldatamining.org/edm2024/instructions-for-authors/ 

Additional Considerations

1. If the paper is not properly anonymized (i.e., the identity of the authors is revealed in the paper) please review the paper as usual, but indicate this in the “Confidential remarks for the program committee” box on the reviewing form.
2. If there are issues with the English in the submission (e.g., grammatical mistakes, misspellings or unusual phrasings), this can be noted in the “Confidential remarks for the program committee” box on the reviewing form, but it should not affect the review and assessment of the submissions with respect to its scientific merit.

Meta-reviewers will compare all reviews and numeric assessments of quality and confidence as well as author rebuttals (if made) and make final recommendations for paper acceptance or rejection with justification to the program committee chairs.