Educational Data Mining versus Learning Analytics: A Review of Publications From 2015 to 2019
Bibliographic record
Abstract
To examine the similarities and differences between two closely related yet distinct fields – Educational Data Mining (EDM) and Learning Analytics (LA) – this study conducted a literature review of the empirical studies published in both fields. We synthesized 492 LA and 194 EDM articles published during 2015–2019. We compared the similarities and differences in research across the two fields by examining data analysis tools, common keywords, theories, and definitions listed. We found that most studies in both fields did not clearly identify a theoretical framework. For both fields, theories of self-regulated learning are most frequently used. We found, through keyword analysis, that both fields are closely related to each other as “learning analytics” is most frequently listed keyword for EDM and vice versa for LA. However, one notable difference relates to how LA studies listed social-related keywords whereas EDM studies listed keywords related to technical methods. The tools used for data analysis overlap largely but some of the LA studies listed tools for qualitative data analysis and social network analysis whereas EDM studies did not. Finally, the distinction of the two fields is defined differently by authors as some demarcate the differences whereas some address them interchangeably.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".