Introducing Computer Programming With Lego Robotics
Bibliographic record
Abstract
The first year undergraduate course Introduction to Computer Programming for Engineers (APSC 142) was considered to be challenging for both students and instructors alike.The engineering curriculum at Queen's University is structured so that the students take a common first year, and select their particular engineering discipline near the end of that year.Not only did the course therefore have large enrolment, with 700 students, but it also comprised many students who did not feel that computer programming was a required skill set for their ultimate intended disciplines (e.g., Chemical, Industrial, Mining, or Mechanical Engineering).In order to re-engage the students, the course was significantly redesigned to center around the use of LegoNXT robots.A series of labs was developed where the students would program directly on the robotic platforms, which offered immediate and tangible feedback for their efforts.A software simulator of the robot was also custom-developed and distributed to the students, so that they could further exercise their programming skills.This also allowed students to work independently and to test their programs and ideas in a simulated robot environment, thus alleviating the need for 700 students to have access to the more limited hardware during nonworking hours.The role of lectures was reduced, in favour of instruction through an interactive design studio.This paper will describe the rationale for the changes that were put in place, some of the issues and challenges that resulted, and the positive effect that they have had on improving student engagement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 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".