The DOI matches its DataCite record.
Discovering Educational Data Mining: An Introduction
Open accessAbstract
This article introduces researchers in the science concerned with developing and studying research methods, measurement, and evaluation (RMME) to the educational data mining (EDM) community. It assumes that the audience is familiar with traditional priorities of statistical analyses, such as accurately estimating model parameters and inferences from those models. Instead, this article focuses on data mining’s adoption of statistics and machine learning to produce cutting-edge methods in educational contexts. It answers three questions: (1) What are the primary interests of EDM and RMME researchers? (2) What is their discipline-specific vocabulary? and (3) What are the similarities and differences in how the EDM and RMME communities analyze similar types of data?
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“If not carefully designed, AI grading systems risk perpetuating biases present in training datasets [ 45, 46].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [46]
Citations
Cited by
Sorted by citations
Ncube, P. D. N., Dzvapatsva, G. P., Matobobo, C., & Ranga, M. M. (2026) · AI and Ethics · doi:10.1007/s43681-025-00871-w · Cited by 31
Chavez, O. J. F., & Palaoag, T. D. (2026) · Artificial Intelligence in Education · doi:10.1108/aiie-06-2025-0131 · Cited by 2
Metadata
- Authors
- Collier, Z., Sukumar, J., & Barmaki, R.
- Venue
- University of Massachusetts Amherst Libraries
- Publisher
- University of Massachusetts Amherst Libraries
- Published
- 2024
- ISSN
- 1531-7714 (ISSN-L)
- OpenAlex
- W6902856229
- Cited by
- 2
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1
- FWCI
- 0.76
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Practical assessment, research & evaluation
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Keywords
- educational data mining, machine learning in education, education statistics, disciplinary terminology, educational data analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Discovering Educational Data Mining: An Introduction. https://app.cochat.ai/papers/10.7275/pare.2037
- The paper
- Collier, Z., Sukumar, J., & Barmaki, R. (2024). Discovering Educational Data Mining: An Introduction. University of Massachusetts Amherst Libraries. https://doi.org/10.7275/pare.2037
