Educational Data Mining: A Bibliometric Analysis of an Emerging Field
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We are now able to collect enormous amounts of information at the learner level. Mining educational data to provide data-driven analytics has spurred great interest among researchers and policymakers that continues to grow. This growing research area is called educational data mining (EDM). Yet the growing interest in the topic has also resulted in a fragmented body of literature. This recent growth justifies and renders it important to synthesize the extant body of multidisciplinary research to bring this literature together into a systematic whole and to assess the extent of our current knowledge. To this purpose, this article provides a bibliometric review of the accumulated literature (<inline-formula> <tex-math notation="LaTeX">$N=194$ </tex-math></inline-formula>) on educational data mining during 2015–2019. Findings suggest that interest in educational data mining has increased in recent years. The studies in this stream of research mainly focus on using state-of-the-art EDM techniques to optimize prediction models to accurately predict learners’ academic performance and to detect behaviors of learners for timely intervention. In addition, our findings show that EDM literature contains publications of researchers from diverse countries. Most studies were a result of collaborations between multiple authors, and most authors collaborated with authors from the same country. The United States, China, and Spain are the countries with the most prolific publications in EDM literature. For future research, EDM researchers should increase discussions on connecting theories with EDM techniques, ethics and privacy issues, and international collaboration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.029 | 0.148 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it