Bibliographic record
Abstract
Four descriptions of learning analytics are widely cited.Siemens (2010) described learning analytics as and analysis models to discover information and socialThe website for the 1 st International Conference on Learning Analytics and Knowledge posted this de-1 Educause models to predict student progress and performance, modelling capacity of analytics: to predict behaviour, act on predictions, and then feed those results back into the process in order to improve the predictions These descriptions beg fundamental questions.What data should be gathered for input to methods that 1 https:/ /tekri.athabascau.ca/analytics/computations that underlie analytics and, second, what analytics can say about phenomena.For instance, ordinal (rank) data preclude using arithmetic operations on data, such as addition or division.If data are not ordinal, A cannot be described as greater than B, nor are transitive statements valid: if A > B and B > C, then A > C. What properties of data bear on the validity of interventions based on learning analytics developed from grounds for intervening without other data.None of these data classes are legitimately considered a with other courses or a job).And, notably, prediction does not supply valid grounds for inferring causality.Learning ecologies involve multiple actors.Authors of learners about how to study; font styles and formats Chapter 21: Learning Analytics for Self-Regulated Learning
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".