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. Résumé : Quelques semaines seulement avant la tenue de l'assemblée annuelle 2019 de l'American Education Research Association (AERA) à Toronto, en Ontario, le gouvernement provincial a annoncé une importante réforme de l'éducation pour cette province intitulée « L'éducation qui marche pour vous - Moderniser les classes ». Du point de vue de l'apprentissage en ligne, la proposition préconisait la centralisation de l'apprentissage en ligne, l'obligation d'obtenir un diplôme pour quatre cours d'apprentissage en ligne et l'augmentation de la taille maximale des classes pour les cours d'apprentissage en ligne à 35 étudiants. L'appel à soumissions de l'AERA pour la rencontre de 2020 a lancé aux chercheurs le défi de " se connecter avec les leaders organisationnels pour examiner de manière collaborative les problèmes éducatifs continus.... et] s'engager de manière programmatique avec les organisations éducatives. Le présent article relève ce défi et décrit une collaboration entre des chercheurs et un organisme pancanadien pour examiner la recherche qui sous-tend chacun des changements proposés concernant l'apprentissage en ligne. En se fondant sur cette collaboration, les auteurs explorent le système actuel d'apprentissage en ligne en Ontario et soulignent le manque de détails concernant de nombreux aspects de la proposition, ainsi que le manque de recherche sous-tendant les mesures proposées. Mots-clés : surveillance en ligne, apprentissage, test d'anxiété, inquiétude, émotivité
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.026 | 0.025 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".