Structured Self-Assessment Exercises as a Substitute for Small-Group Tutorial Teaching in Diagnostic Imaging Student Preferences and Effects on Examination Performance
Bibliographic record
Abstract
ABSTRACT Research into chiropractic and medical education supports the concept of teaching in a problem-solving approach, simulating the realities of clinical practice. Along with this approach, small-group tutorials have more commonly become the method of delivery of course material, focusing on the learner rather than the teacher. However, offering frequent small-group tutorial sessions is very labor intensive for faculty. The purpose of this study was to determine whether or not structured self-assessment exercises could substitute for some small-group tutorial time without jeopardizing the quality of the students’ education. At the end of the 1999–2000 academic year, a short questionnaire was administered to all 4th-year students to assess their attitudes and opinions about the value of each of the teaching/learning approaches utilized in radiology using a 5-point Likert scale. Final examination marks were compared to the previous cohort of students to determine changes in performance/radiological ability. The examination marks from the 4th-year cohort were compared to their own radiological examination marks from the previous year. The results indicate that students strongly prefer the small-group face-to-face tutorials with the faculty members but rated the self-assessment exercises as ‘‘above average’’ in usefulness. The interactive lecture was also rated as ‘‘very useful.’’ There was no meaningful or significant change in the final examination performances between the current 4th-year cohort and the previous group of students. For the first time in 10 years, no student failed the final film reading examination. It was concluded that the structured self-assessment exercises can serve as a very valuable learning tool and significantly reduce tutor contact time in an overburdened time-table but do not have a negative impact on students’ diagnostic abilities. A combination of small-group tutorials and structured self-assessment exercises is the preferred approach, balancing the wishes of the students with the needs of the faculty as well as providing a varied educational learning experience. (The Journal of Chiropractic Education 15(2): 61–68, 2001)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".