The Evolution of WebCT in a Baccalaureate Nursing Program: An Alice in Wonderland Reflection
Bibliographic record
Abstract
The use of WebCT in the Laurentian University Bachelor of Science in Nursing program began in 2001 when faculty were eager to explore different modes of delivery for fourth year courses. Since then, the use of WebCT within the baccalaureate program has increased substantively. This paper outlines the developmental growth of the use of this learning management system in addition to its strengths and challenges as experienced by Laurentian nursing faculty. It furthermore poses questions about the sustained use of existing as well as newer and emerging educational technologies within undergraduate nursing education. Resume L’utilisation de WebCT dans le programme de premier cycle en sciences infirmieres de la Laurentian University a debute en 2001, alors que les professeurs etaient desireux d’explorer differentes methodes de diffusion pour les cours de quatrieme annee. Depuis lors, l’utilisation de WebCT dans le cadre du programme de premier cycle a considerablement augmente. Cet article dresse un portrait de la croissance developpementale de l’utilisation de ce systeme de gestion de l’apprentissage, de meme qu’un portrait de ses points forts et des defis vecus par les professeurs de l’ecole des sciences infirmieres de l’universite. On y souleve aussi des questions concernant l’utilisation soutenue de technologies educatives plus nouvelles et emergentes dans le cadre d’un programme de premier cycle en sciences infirmieres.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".