Bridging Classroom and Lab Teaching in Audiology Using Problem Based Learning
Bibliographic record
Abstract
In traditional classroom settings, disciplinary content is generally presented first and students’ abilities to acquire this knowledge are then assessed through assignments and exams. Problem based learning (PBL), on the other hand, works in reverse: students learn in the context of the problem to be solved (Ram, 1999). PBL is based on both learning theories and constructivist principles.\nIn Audiology, students’ learning is divided: they study theory in classrooms and the use of sophisticated equipment, and instruments, in lab practicum, separately. In clinical placements, however, student audiologists encounter diverse patients and, consequently, are expected to draw from their theoretical knowledge as well as from their technical know-how (of instruments and skills for operating equipment) at the same time.The problem in Audiology studies is that theoretical and practical skills are treated as separate entities in traditional teaching, despite the fact that both components must be applied together in real-life practice. PBL offers instructors a framework through which to assist students in learning and developing theoretical and practical skills simultaneously. This workshop will focus on preparing instructors to implement PBL and devise efficient assessment strategies to bridge classroom and lab-based learning. Since some basic understanding of core Audiology concepts is necessary to solve topic-specific problems, this workshop will focus on the use of PBL instruction in upper-year Audiology courses. Employing a meta-approach (using PBL to learn about PBL), participants will gain a first-hand experience of PBL while also learning about the research and principles underpinning this model.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".