Can schools realize the learning potential of knowledge management?
Bibliographic record
Abstract
Abstract: In this position paper, reservations are presented regarding the potential of knowledge management (KM) as it is currently applied to the learning and teaching activity of schools. We contend that effective KM is contingent upon the explication of a deep and shared understanding of the learning and teaching process. We argue that the most important transactions in schools, those related to learning and teaching, are frequently the least explicated. Further, where such explication does occur, it is rarely specific enough to generate the kind of meaningful data required to make timely improvements in the learning experience of individual students. Our intent is to inject a cautionary note regarding current conceptualizations of KM in education and to focus the KM discussion on potentially more valid applications in school settings. We offer strategy and examples that can be employed to address the reservations described herein as well as build the kind of professional culture of practice in schools that is more conducive to effective KM.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".