Sense of Irony or Perfect Timing: Examining the Research Supporting Proposed e-Learning Changes in Ontario
Bibliographic record
Abstract
Only weeks before the 2019 annual meeting of the American Education Research Association (AERA) was held in Toronto, Ontario, the provincial government announced a major reform of education for that province entitled Education that Works for You – Modernizing Classrooms . From an e-learning perspective the proposal called for a centralization of e-learning, a graduation requirement of four e-learning courses, and increase the class size limit for e-learning courses to 35 students. The AERA call for submissions for the 2020 meeting issued a challenge for scholars to ‘connect with organizational leaders to examine collaboratively continuing educational problems... [and] programmatically engaging with educational organizations.’ This article accepts that challenge and describes a collaboration between scholars and a pan-Canadian organization to examine the research behind each of these proposed e-learning changes. Resume : Quelques semaines seulement avant la tenue de l'assemblee annuelle 2019 de l'American Education Research Association (AERA) a Toronto, en Ontario, le gouvernement provincial a annonce une importante reforme de l'education pour cette province intitulee « L'education qui marche pour vous - Moderniser les classes ». Du point de vue de l'apprentissage en ligne, la proposition preconisait la centralisation de l'apprentissage en ligne, l'obligation d'obtenir un diplome pour quatre cours d'apprentissage en ligne et l'augmentation de la taille maximale des classes pour les cours d'apprentissage en ligne a 35 etudiants. L'appel a soumissions de l'AERA pour la rencontre de 2020 a lance aux chercheurs le defi de se connecter avec les leaders organisationnels pour examiner de maniere collaborative les problemes educatifs continus.... et] s'engager de maniere programmatique avec les organisations educatives. Le present article releve ce defi et decrit une collaboration entre des chercheurs et un organisme pancanadien pour examiner la recherche qui sous-tend chacun des changements proposes concernant l'apprentissage en ligne. En se fondant sur cette collaboration, les auteurs explorent le systeme actuel d'apprentissage en ligne en Ontario et soulignent le manque de details concernant de nombreux aspects de la proposition, ainsi que le manque de recherche sous-tendant les mesures proposees. Mots-cles : surveillance en ligne, apprentissage, test d'anxiete, inquietude, emotivite
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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 | 0.016 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".