Design of an Experiential Learning Course in Sensors, Measurement and Instrumentation
Bibliographic record
Abstract
Recently a new course in Sensors and Measurement was introduced to the Mechatronics Option in the Mechanical Engineering Program at Ryerson University. In order to enhance the learning and comprehension of fundamental concepts in measurement and instrumentation, experiential learning was introduced through the extensive use of “hands-on” laboratories to demonstrate the theory taught in the lectures. In the course, the application of modern instrumentation and measurement of both static and dynamic mechanical systems are covered through the use of interfacing of hardware sensors with Labview software. Students learn about transducers, signal conditioning, and analogue to digital data conversion through the writing of their own Labview programs which is used to collect and perform preliminary analysis of the data. These labs are designed to follow Kolb’s experiential learning cycle, where students learn the theory, are introduced to the physical equipment, plan how they are going to program the Labview software to collect the data that they require, and then test their programs in the laboratory. Finally, after the lab, students are required to analyse the data they collected and write a lab report. By taking this experiential approach to learning, the course was successful in teaching and reinforcing the required principles to students.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".