Integrated Method of Teaching in Web Quest Activity and Its Impact on Undergraduate Students' Cognition and Learning Behaviors: A Future Trend in Medical Education
Bibliographic record
Abstract
INTRODUCTION: Web Quest is one of the new ways of teaching and learning that is based on research, and includes the principles of learning and cognitive activities, such as collaborative learning, social and cognitive learning, and active learning, and increases motivation. The aim of this study is to evaluate the Web Quest influence on students' learning behaviors. MATERIALS & METHODS: In this quasi-experimental study, which was performed on undergraduates taking a psychiatric course at Jahrom University of Medical Sciences, simple sampling was used to select the cases to be studied; the students entered the study through census and were trained according to Web Quest methodology. The procedure was to present the course as a case study and team work. Each topic included discussing concepts and then patient's treatment and the communicative principles for two weeks. Active participation of the students in response to the scenario and introduced problem was equal to preparing scientific videos about the disease and collecting the latest medical treatment for the disease from the Internet.Three questionnaires, including the self-directed learning Questionnaire, teamwork evaluation Questionnaire (value of team), and Buffard self-regulated Questionnaire, were the data gathering tools. RESULTS: The results showed that the average of self-regulated learning and self-directed learning (SDL) increased after the educational intervention. However, the increase was not significant. On the other hand, problem solving (P=0.001) and the value of teamwork (P=0.002), apart from increasing the average, had significant statistical values. CONCLUSIONS: In view of Web Quest's positive impacts on students' learning behaviors, problem solving and teamwork, the effective use of active learning and teaching practices and use of technology in medical education are recommended.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".