Facilitation of Competency-Based Learning With a Practicum Administration Software: The User Experience
Bibliographic record
Abstract
Objective Technology is essential in the facilitation of many operations in higher educational institutions. The use of web-based platforms to deliver academic content, including practice-based training, has gained popularity. However, their use in practicum process administration is not well studied. In the 2020/2021 academic year, a graduate program in the Faculty of Health Science within a public university in Ontario incorporated the InPlace platform to streamline the administration of the practicum process, including goal setting. This study aimed to understand the user experience of the platform in facilitating competency-based learning. Methods Twelve students participated in two focus group sessions that lasted approximately 1.5 hr each. Two staff members participated in one-on-one semi-structured interviews. The System Usability Scale (SUS) was used as a measure of the platform’s usability. Other outcomes included staff and students’ user experience. Result Overall, the students and staff believe the platform is good for facilitating competency-based learning. The SUS score was 61.8 (95% confidence interval, [56.7, 66.9]). Eight students (66.7%) indicated that the platform was useful in helping them navigate their learning goals. Staff expressed appreciation of the program with respect to communication, practicum process, and overall program administration. Some suggestions for improving the platform were made. Conclusion The practicum placement platform has shown some initial benefits in communication and practicum process administration. In a future configuration of similar platforms, the implementation of the suggestions provided in this study may be necessary to improve usability and enhance the facilitation of competence-based learning.
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 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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".