An Online Summer Course for Prematriculation Medical Students
Bibliographic record
Abstract
Objective: An online summer course was developed to enhance students' skills in cooperative learning and self-directed learning, Internet search strategies, problem solving, creativity, and project-management skills. The four-week course was offered in the summer of 2000 to the class of 2004 as a voluntary, no-cost, no-credit opportunity. It was delivered over the Internet using WebCT and included both asynchronous and synchronous learning experiences. Description: Eighteen of the 50 students in the incoming class registered for the course and were divided into four teams. A second-year student facilitated each team to guide discussion and keep the team on track. Two faculty served as mentors to each facilitator, providing assistance as needed. Approximately eight hours of work were built into each week, including pre- and post-tests, evaluations, content material, self-directed learning exercises, bulletin board, and chat. The bulletin board was used to solve problems, share information, and make project status reports and to organize weekly chat sessions. The first two weeks of the course consisted of introductory, socialization, and team-building exercises and included a module on Internet search skills. The third week of the course was organized around a clinical problem (the students were required to research several specific aspects of diabetes mellitus), and in the fourth week the teams organized and developed papers on the clinical problem. A one-hour participant forum was conducted during orientation week so that students and facilitators could meet each other and share their reactions about what had and had not worked in the summer course. Information Technology Institute (ITI) of Halifax, Canada, developed this course under contract with the University of South Dakota School of Medicine. ITI was selected for the project because of the company's background in collaborative learning, group processes, and problem-based learning. One of the authors (DK) worked with the medical school and ITI to develop course content and evaluation mechanisms. Discussion: Pre- and post-tests examined the participants' knowledge, opinions, and skills with several statistically significant findings. After they completed the online course, the participants' knowledge of collaborative learning and teamwork increased from a mean of 21.7% to 51.7% (p = .01); knowledge of self-directed learning increased from a mean of 60% to 80% (p = .03). The overall increase in knowledge as measured by change in the pre- and post-tests was from 50.2% to 67% (p = .01). The students ratings of their information-retrieval skills increased from a mean of 3.58 to 3.87 (p = .03) after the completion of the course. In response to the question “I am continuously searching for better solutions to problems,” the mean response increased from 3.87 to 4.27 (p = .01). The students' opinions and learning about interpersonal and communication skills also showed a positive change, from 4.05 to 4.23 (p = .01). Students responded positively to the course in the formal evaluation and during the reflection period. All reported that they would recommend the course to a friend who had been accepted to medical school. Proposed changes to the course include reducing or compressing the socialization exercises and increasing the content on clinical problem solving.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.112 | 0.027 |
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".