Problem-Based Learning in Speech-Language Pathology Programs: A Scoping Review
Bibliographic record
Abstract
Purpose: Problem-based learning (PBL) promotes student-centered, active learning and has been applied in many health disciplines, including speech-language pathology (SLP). There is some SLP literature outlining how PBL has been applied and its components, however, how PBL is applied across SLP programs worldwide is yet to be explored. We therefore sought to answer the question, how do SLP programs apply PBL and what are the associated student outcomes? Methods: Five databases were searched, as well as searching the grey literature for relevant articles. Covidence was used to de-duplicate, collate, and review articles. SLP program, study, PBL application, and student outcome data was extracted and synthesized. Results: Thirty articles were included. PBL was applied in undergraduate and graduate SLP programs, typically using a hybrid model with most articles published in the United States, China, and Australia. Key components of PBL included group learning, a realistic clinical case, and a facilitator. Positive aspects (e.g., motivation, communication and reasoning skills, retention of information), as well as negative aspects of PBL were identified (e.g., time needed for preparation, student stress). Conclusions: PBL is applied in various ways in SLP training, with a variety of strengths, challenges, and delivery methods identified in the literature. Overall, PBL has established utility in SLP programs, and research is warranted to further investigate PBL components such as outcomes, modes of delivery, case development, and facilitator training.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".