CMALT and cMOOC - a community of educators and their learning technologies
Bibliographic record
Abstract
CMALT is a peer-reviewed accreditation based upon the UKPSF (UK Professional Standards Framework) to enable staff (whether academic or administrative) who embed learning technologies in either their teaching or support roles, to showcase their experiences and gain recognition. This programme has been developed by ALT and is co-delivered online, by ASCILITE.
 
 Building upon the experiences of supporting a geographically distributed project involving six institutions nationally across New Zealand during 2014-2015, we (AUT) have developed a support structure for building communities around CMALT accreditation using a cMOOC model. The cMOOC framework enables us to bridge and broker authentic participation within an international community of academics and learning technologists interested in exploring CMALT accreditation, and we have had participation from the UK, Japan, Canada, Australia, and NZ. The CMALT cMOOC was developed in 2017 by the Centre for Learning and Teaching, at Auckland University of Technology, and endorsed by ALT and ASCILITE in 2019.
 
 This presentation will highlight the ecology of resources that are used to support the community and hear from current participants of the programme
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".