Active Learning in Mechanical and Mechatronics Engineering, Case Study: Computer-Based Interactive Learning in Robotics Classroom
Bibliographic record
Abstract
This paper is concerned with the review of active learning methods implemented in Mechanical and Mechatronics Engineering courses. The active learning methods are categorized into two groups of in-class activities without the use of computers and computer-based classrooms. The strategies to encourage university instructors to adopt active learning methods are also discussed. The paper also addresses the pilot project for the implementation of a novel computer-based experiential learning in the course of “Robot Manipulators: Kinematics, Dynamics, Control” at the University of Waterloo, Canada. A Student Interactive Learning System (SILS) has been developed for in-class activities in this course. The SILS system has two components: students’ mobile devices and a front-end website in which the instructor has control to upload the demonstrations and quizzes and receive students’ responses. The students are connected to the website through the WiFi connection. Findings of an initial survey, which was conducted at the start of the semester, revealed that majority of the students find the conventional classroom passive and believe adding interactivity in the lecture enhances their in-class learning experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".