Bibliographic record
Abstract
Purpose The purpose of this paper is to provide details on Simon Fraser University's new School of Interactive Arts and Technology (SIAT) approach to creating a culture that supports rapid development of high‐quality e‐learning materials, why it has been successful, and what has been learned. Design/methodology/approach Describes how, over a period of four years SIAT has had the unique opportunity to build an integrated set of graduate and undergraduate degree programs from the ground up. The organization has recognized the importance of its people – both in terms of the knowledge they bring to the organization, and the added value it gains by providing an environment where all employees can continue to learn – from courses, from their own experience, and from each other. Findings Because of the organization's structured, yet flexible, approach to course design and development workflow, each set of courses was ready just as the students became ready to take them. The organizational culture's combination of clear structure, openness to new ideas, and commitment to high quality courseware has enabled us to meet the needs of our students and prepare them for careers in today's high‐tech workforce. Originality/value SIAT have developed best practices for the implementation of organizational e‐learning strategies and the approach will be useful in informing the strategies adopted by other organizations.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".