Redesigning Online Learning for Graduate Seminar Delivery
Bibliographic record
Abstract
Given the crucial role played by universities in a knowledge-based society, understanding how and under what conditions online learning (OL) can improve access to graduate studies is of the highest importance to today’s growing global economy. Over the past decade, phenomenal advances have been made in the application of communication and information technologies to support student learning in higher education. Yet, in proportion to overall provision of higher education, the use of technology by faculty for graduate-level, online learning (OL) has been minimal, especially among regular faculty. This paper presents an adapted form of OL, especially designed for traditional universities, with initial data from studies underway in two Canadian universities. Finally, an emerging network of researchers interested in the role of online learning within mainstream higher education is presented. Resume Etant donne le role crucial joue par les universites au sein d’une societe fondee sur le savoir, comprendre comment et a quelles conditions l’apprentissage en ligne (AL) peut ameliorer l’accessibilite aux etudes superieures est primordial pour l’economie globale croissante d’aujourd’hui. Au cours de la derniere decennie, des avancees phenomenales ont ete realisees dans l’application des technologies de communication et de l’information afin de soutenir l’apprentissage etudiant dans le cadre d’etudes superieures. Toutefois, proportionnellement a l’ensemble de l’enseignement superieur dispense, l’utilisation de la technologie par les enseignants pour l’apprentissage en ligne (AL), aux etudes superieures, a ete minimal, particulierement parmi les membres reguliers du corps professoral. Cette etude presente une forme adaptee d’AL, specialement concue pour les universites traditionnelles, avec des donnees initiales provenant d’etudes en cours dans deux universites canadiennes. Finalement, un reseau emergent de chercheurs s’interessant au role de l’apprentissage en ligne dans le cadre de l’education superieure reguliere est presente
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.022 |
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".