<研究ノート>リカレント教育におけるCanvasの活用 --京都大学私学経営アカデミーにおけるLMS講座の実装--
Bibliographic record
Abstract
This paper focuses on utilizing the Canvas in order to enhance the learning community for Kyoto University Educational Leadership Academy for Administration of Private Universities, Colleges and Schools. Canvas, which is one of the online LMS (Learning Management System) platform, is the most prevalent LMS platform among higher education in the US and Canada. However, it has not been highly recognized in Japan yet. This KU Educational Leadership Academy is a recurrent program for educators, consisting of 120 hours' lectures and workshops on 5 educational fields: administrative management, resource management, instruction and curriculum management, promotion of ICT use in education, and fieldwork for educational policy practice. Launched in 2017, the Academy focuses to prepare educators being capable of managing and utilizing LMS effectively from the beginning. The Academy has introduced a variety of LMS platforms in the program every year, and started the Canvas in 2022. In the program of 2022, cohort took two classes on Canvas: one was a 3-week summer course and another was a 4-week fall course. According to the survey after taking the courses, satisfaction rate showed 87% for the summer course, and 100% for the fall course. In the additional comments section, many positive responses were received: most of them were related to how the two way communication among members enabled them to realize their deep learning. In order to contribute more to recurrent education, further analysis is necessary for finding factors for effective Canvas usage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".