Online teaching of acoustics to candidates for admission in audiology and speech-language pathology at the University of Ottawa
Bibliographic record
Abstract
This paper aims to share the experiences gained by migrating a preparatory course in acoustics for candidates applying to the graduate program in audiology or speech-language pathology at the University of Ottawa to an online environment. The course is offered in the summer for those who have no prior training in physical acoustics, acoustic phonetics or instrumental techniques in linguistics. Knowledge of the basic concepts in acoustics, signal analysis and electroacoustic systems is necessary for mastering several clinical skills for both audiologists and speech-language pathologists. Delivering the course online became necessary to allow more students to meet the admission prerequisite in acoustics given the wide geographical distribution of newly admitted students in audiology and speech-language pathology across the country. Since 2009, the course has been offered completely online using distance education tools (e.g. Blackboard™) and the development of acoustics teaching and learning materials in French targeting the area of audiology and speech-language pathology. While significant efforts were required to implement the online course over a five-year period, the project has proven to be a great success.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".