Bibliographic record
Abstract
Due to the pandemic lockdown, York University’s Fall 2020 offerings of a pair of 1st and 2nd year undergraduate engineering and computer science courses were heavily modified to accommodate a completely online approach to teaching. The objective was to maximize interactivity and hands-on elements while also providing a supportive and authentic learning experience. Class presentations were made asynchronous by uploading them to YouTube and superimposing H5P elements via our Moodle-based LMS. Our traditional laboratory equipment was replaced with inexpensive lab kits that were obtained from commercial vendors and shipped to students via the university’s Bookstore. All tests, quizzes and exams were eliminated in both courses. Instead, a specifications-based assessment approach was taken, with all students given the opportunity to achieve a B+ if they completed all the work in the class. Students who wished to submit a final project could do so for an opportunity to boost their grade to A or A+. Most intra-semester deadlines were removed, with material associated with the synchronous lab sessions being the notable exception. The resulting grade distribution and averages were similar to previous years inwhich we relied to in-person testing. The rate of A/A+ was 21% and 8%, while the failure rate was 13% and 3% , respectively, for the first and second year classes. Informal feedback from students, including those with academic accommodations, was nearly universally positive, with most acknowledging that their stress levels were lower, making the learning more manageable. En raison de la crise sanitaire et le confinement COVID19, deux cours d’ingénierie de 1`ere et 2`eme année de l’université York ont été modifiés pour s’adapter à une approche d’enseignement entièrement numérique. L’objectif des adaptations était de permettre aux étudiants d’apprendre du matériel technique de manière pratique et interactive sur internet. Les présentations en classe ont été rendues interactives et asynchrones en les téléchargeant sur YouTube et en superposant des ressources H5P via notre environnement numérique d’apprentissage Moodle. Nos équipements de laboratoire traditionnel ont été remplacé par des kits de laboratoire abordables obtenus auprès de fournisseurs commerciaux et expédies aux étudiants via la librairie de l’université. Nous avons éliminé tous les tests, questionnaires et examens dans les deux cours. Une approche basée sur les spécifications a été adoptée, permettant les élèves d’obtenir un B+ s’ils terminent tous les travaux de la classe. Les étudiants qui souhaitaient un A ou A+ devaient soumettre un projet final. La plupart des délais intra-semestriels ont été supprimés, le matériel associé aux sessions de laboratoire synchrones étant l’exception notable. La distribution des notes et les moyennes étaient similaires aux années au cours desquelles nous nous sommes appuyés sur des tests en personne. Le taux de A / A + était de 21% et 8%, tandis que le taux d’échec était de 13% et 3%, respectivement, pour les classes de premières et deuxièmes années. La rétroaction informelle des étudiants, y compris ceux qui avaient des accommodements scolaires, était presque universellement positive, la plupart reconnaissant que leur niveau de stress était réduit et que l’apprentissage était gérable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".