A scoping review of empirical studies on theory-driven learning analytics
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
The field of learning analytics (LA) is developing rapidly. However, previous empirical studies on LA were largely data-driven. Little attention has been paid to theory-driven LA studies. The present scoping review identified and summarized empirical theory-driven LA studies, aiming to reveal how theories were integrated into LA. The review examined 37 peer-reviewed journal articles published from 2016 to 2020 from six databases. Results show that most studies were guided by the theories of self-regulated learning and social constructivism; most integrated theories into LA for better interpreting the data analysis results; and most linked theoretical constructs and log variables directly. Several studies employed well-developed survey instruments to measure theoretical constructs. The review results indicate that LA studies still need to strive for new theory advances in learning. Recommendations for future study are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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