Development and Evaluation of a Hybrid Course in Clinical Virology at a Faculty of Pharmacy in Lille, France
Bibliographic record
Abstract
BACKGROUND: During their studies, pharmacy students must acquire the specific skills in clinical virology required for their subsequent professional practice. Recent experiments on teaching and learning in higher education have shown that hybrid courses strengthen the students' commitment to learning and enable high-quality knowledge acquisition. OBJECTIVE: This study concerned the design and deployment of a hybrid course that combines face-to-face and Web-based instruction in clinical virology for fourth-year pharmacy students. The study's objectives were to (1) measure the students' level of involvement in the course, (2) gauge their interest in this type of learning, and (3) highlight any associated difficulties. METHODS: The study included 194 fourth-year pharmacy students from the Lille Faculty of Pharmacy (University of Lille, Lille, France) between January and June 2017. The students followed a hybrid course comprising an online learning module and 5 tutorial sessions in which professional situations were simulated. The learning module and 3 online evaluation sessions were delivered via the Moodle learning management system. Each tutorial session ended with an evaluation. The number of Moodle log-ins, the number of views of learning resources, and the evaluation marks were recorded. The coefficient for the correlation between the marks in the online evaluation and those in the tutorials was calculated. The students' opinions and level of satisfaction were evaluated via a course questionnaire. RESULTS: The course's learning resources and Web pages were viewed 21,446 and 3413 times, respectively. Of the 194 students, 188 (96.9%) passed the course (ie, marks of at least 10 out of 20). There was a satisfactory correlation between the marks obtained in the online evaluations and those obtained after the tutorials. The course met the students' expectations in 53.2% of cases, and 57.4% of the students stated that they were able to work at their own pace. Finally, 26.6% of the students stated that they had difficulty organizing their work around this hybrid course. CONCLUSIONS: Our results showed that pharmacy students were strongly in favor of a hybrid course. The levels of attendance and participation were high. However, teachers must be aware that some students will encounter organizational difficulties.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".