Using a Hyflex Learning Format in a Second-year Mechatronics Course
Bibliographic record
Abstract
This evidence-based practice paper details a Hyflex learning format used in a second-year Mechatronics course for Mechanical Engineering majors.At York College of Pennsylvania, Mechatronics introduces second-year Mechanical Engineering students to essential aspects of electronics and instrumentation through experiential hands-on learning.Students regularly conduct laboratory exercises and work on short projects as they learn about common electronic components, basic circuit analysis and sensors, and how these components can be used to create electro-mechanical devices.The course was modified in Spring 2021 to incorporate aspects of the Hyflex course format necessary to accommodate the ongoing COVID-19 pandemic.The course format enabled students to attend in person or remotely through Zoom video conferencing.The format expanded the use and support of asynchronous learning activities to better enable students quarantined, due to close contacts or positive Covid tests, to keep up or catch up on the course instruction.The goal of the instructors was to enable the same learning outcomes for all students, independent of personal circumstances.Online software tools (Canvas learning management system, Tinkercad and Nearpod) were used to deliver content and engage students.Conceptual topics were introduced followed by hands-on activities from Make: Electronics 2nd edition.Each student was also given a kit of electronic components, wire, a breadboard and a multimeter.Students completed and submitted assignments in a variety of digital formats, such as video reports.This paper details the Hyflex modifications made to Mechatronics.It also includes student feedback and instructor reflections.Although the Hyflex format required significant new planning and experimentation it provided a means of accommodating a mix of face-to-face and online students and also provided an opportunity to increase the long term effectiveness of the course.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.013 |
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".