Analytics for Knowledge Creation: Towards Epistemic Agency and Design-Mode Thinking
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
Innovation and knowledge creation call for high-level epistemic agency and design-mode thinking, two competencies beyond the traditional scopes of schooling. In this paper, we discuss the need for learning analytics to support these two competencies, and more broadly, the demand for education for innovation. We ground these arguments on a distinctive Knowledge Building pedagogy that treats education as a knowledge-creation enterprise. By critiquing current learning analytics for their focus on static-state knowledge and skills, we argue for agency-driven, choice-based analytics more attuned to higher order competencies in innovation. We further describe ongoing learning analytics initiatives that attend to these elements of design. Prospects and challenges are discussed, as well as broader issues regarding analytics for higher order competencies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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