Integration of e-learning technologies in an interprofessional health science course
Bibliographic record
Abstract
BACKGROUND: Advances in information and communication technology are influencing instructional formats and delivery modes for post secondary teaching and learning. AIMS: The purpose of this study was to determine whether interprofessional team process skills traditionally taught in a small group face-to-face classroom setting could be taught in a blended learning environment; without compromising the pedagogical approach and collaborative Group Investigation Model (Sharan & Sharan 1992) used in the course. METHOD: A required interprofessional team development course designed to teach health science students (Medicine, Nursing, Pharmacy, Occupational Therapy, Physical Therapy, Dentistry, Dental Hygiene, Medical Laboratory Science, and Nutrition) team process skills was redesigned from a 100% face-to-face delivery format to a blended learning format where 70% of the instruction was delivered using a new synchronous virtual classroom technology (Elluminate www.elluminate.com) in conjunction with asynchronous technology (WebCT). It was hypothesized there would be no significant difference between the blended learning format and the traditional face-to-face format in the development of interprofessional team knowledge, skills and attitudes. The two formats were evaluated on demographic information, computer experience, and interprofessional team attitudes, knowledge and skills. RESULTS: The three main findings are: (a) no significant differences between student groups on achieving team process skills, (b) an observation of differences between the groups on team dynamics, and (c) a more positive achievement of course learning objectives perceived by students in the blended learning class. CONCLUSIONS: The results provide evidence to support our blended learning format without compromising pedagogy. They also suggest that this format enhances students' perceptions of their learning.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".