Implementation of web-based learning in colleges of education: Barriers and enablers
Bibliographic record
Abstract
This paper describes the results of a survey to determine the factors that serve as barriers or enablers to the implementation of web-based learning in colleges of education. A total of 229 faculty members responded to the survey. Of these, 104 had never taught a web-based course while 125 had taught at least one online course. Results of the survey showed that Education faculty in this sample had an overall neutral position about the readiness of colleges of education to implement web-based learning. The survey found that financial resources, infrastructure, and support were seen as barriers to implementation while organizational culture, policies, a commitment to learning, and evaluation were seen as enablers to implementation. Open-ended responses showed that there were interesting differences based on the perceived lack of time and perceived lack of social interaction between faculty who have taught online and those who have not. Résumé :Cet article décrit les résultats d’un sondage visant à déterminer les facteurs qui font obstacle ou qui contribuent à la mise en œuvre de l’apprentissage en ligne dans les établissements d’enseignement. Un total de 229 membres du corps professoral ont répondu au sondage. De ce nombre, 104 n’avaient jamais donné un cours en ligne tandis que 125 avaient enseigné au moins un cours en ligne. Les résultats du sondage ont montré que la faculté d’éducation de cet échantillon était globalement neutre au sujet de la volonté des établissements d’enseignement à mettre en œuvre l’apprentissage en ligne. Le sondage a révélé que les ressources financières, l’infrastructure et le soutien étaient considérés comme des facteurs obstacles à la mise en œuvre alors que la culture organisationnelle, les politiques, l’engagement envers l’apprentissage et l'évaluation étaient considérés comme des facteurs facilitants de la mise en œuvre. Les questions à réponses libres ont mis en lumière des différences intéressantes entre les professeurs qui avaient enseigné en ligne et ceux qui ne l’avaient pas fait relativement au manque de temps et d’interaction sociale perçu.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".