Web-Based Technology: Its Effects on Small Group “Problem-Based Learning” Interactions in a Professional Veterinary Medical Program
Bibliographic record
Abstract
The objective of this investigation was to ascertain whether and how the introduction of a new technology (WebCT) influenced faculty teaching styles while facilitating small group problem-based learning (PBL) sessions in a professional veterinary medical (PVM) program. The following questions guided the study: (1) How does the use of technology affect faculty teaching behaviors? (2) Do the facilitators' interactions with WebCT technology change over the course of one semester? (3) What is the perceived impact of WebCT on facilitators' role in PBL? The study employed a combination of qualitative (case study) and semi-quantitative (survey) methods to explore these issues. Nine clinical sciences faculty members, leading a total of six PBL groups, were observed over the course of an academic semester for a total of 20 instructional sessions. The qualitative data gathered by observing faculty as they facilitated PBL sessions yielded three major themes: (1) How do PBL facilitators adapt to the addition of WebCT technology? (2) Does this technology affect teaching? and (3) How do PBL facilitators interact with their students and each other over the course of a semester? No direct evidence was found to suggest that use of WebCT affected teaching behaviors (e.g., student-centered vs. teacher-centered instruction). However, all facilitators showed a moderate increase in comfort with the technology during the semester, and one participant showed remarkable gains in technology skills. The teaching theme provided insight into how facilitators foster learning in a PBL setting as compared to a traditional lecture. A high degree of variability in teaching styles was observed, but individuals' styles tended to remain stable over the course of the semester. Nevertheless, all facilitators interacted similarly with students, in a more caring and approachable manner, when compared to the classroom or clinic atmospheres.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".