Comparison of learning analytics and educational data mining: A topic modeling approach
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
Educational data mining and learning analytics, although experiencing an upsurge in exploration and use, continue to elude precise definition; the two terms are often interchangeably used. This could be owing to the fact that the two fields exhibit common thematic elements. One avenue to provide clarity, uniformity, and consistency around the two fields, is to identify similarities and differences in topics between the two evolving fields. This paper conducted a topic modeling analysis of articles related to educational data mining and learning analytics to reveal thematic features of the two fields. Specifically, we employed structural topic modeling to identify the topics of the two fields from the abstracts. We apply structural topic modeling on N=192 articles for educational data mining and N=489 articles for learning analytics. We infer five-topic models for both educational data mining and learning analytics. We find that while there appears to be disciplinary differences in terms of research focus, there is little support for a clear distinction between the two disciplines, beyond their different lineage. The trend points to a convergence within the field of educational research on the applications of advanced statistical learning techniques to extract actionable insights from large data streams for optimizing teaching and learning. Both fields have converged on an increasing focus on student behaviors over the last five years.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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