Exploring Student Preconceptions of Readiness for Remote-Online Case-Based Learning: A Case Study
Bibliographic record
Abstract
BACKGROUND: Case-based learning (CBL) is an educational approach where students work in small, collaborative groups to solve problems. Web-conferencing software provides a platform to present information and share concepts that are vital to CBL. Previous studies have found that participants were resistant to change associated with implementing e-learning; however, strategies to reduce this resistance have not been explored. OBJECTIVE: This study was designed to explore student preconceptions and understanding of remote-online case-based learning (RO-CBL). METHODS: The study took place during the Bachelor of Physiotherapy program at Monash University, Victoria, Australia, in 2013. The entire third-year cohort (n=73) was invited to participate. The primary outcome of interest was students' preconceptions of RO-CBL, collected via pre- and posttraining surveys. RESULTS: Of the 73 students, 66 completed both surveys (attrition rate 9.6%). Three key themes relevant to student preconceptions of RO-CBL emerged: flexibility in time and location of CBL, readiness or hesitation to change to a Web-based format, and the value of training in RO-CBL that included a demonstration and trial run. Thirty-four percent of the participants were hesitant to move to an online format. CONCLUSIONS: This study explored students' preconceptions of Web-based learning and evaluated the change in students' attitudes after training. The results suggest that educational designers should not assume that students are confident and competent in applying these technologies to professional educational activities. By identifying students' needs before implementation, training sessions can be designed to target these needs, and improve the understanding of RO-CBL and how it works in practice. This may reduce resistance to change, enhance students' satisfaction, and ultimately improve the learning experience.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".