Bibliographic record
Abstract
Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract LEGO Builds Bridge for Communication and Teamwork J.E. Miller-Young, R. Warrington, D. Patterson, C. Jefferies Mount Royal College, Calgary, Alberta, Canada Introduction It is well recognized that engineering graduates require communication and teamwork skills in order to succeed in the workplace. Unfortunately, the traditional model of lecture/tutorial/lab for discrete subjects emphasizes reliance on the instructor for the delivery of facts and well- established principles rather than teaching students what engineers really do – design, revise and test solutions while analyzing and synthesizing the best available data and theories. Thus, the Canadian Engineering Accreditation Board (CEAB) and the American Accreditation Board for Engineering and Technology (ABET) both stipulate that every student must have real world, team-oriented, open-ended design experiences before graduation1,2. Mount Royal College instructors believe that students should be exposed in their first year to a design class that incorporates elements of team work, communication skills and creative problem solving so that they begin to develop these skills in parallel with their technical knowledge. Engineering Communications and Design I and II (ENGR 1251 and 1253) are two such courses. The communications component includes oral, written as well as visual communication skills, with a strong emphasis on sketching, which has been shown to have a positive impact on the engineering design process and quality of the designed solution3. Developed in conjunction with similar courses at the University of Calgary, the Engineering Design and Communication courses span the entire first year and are taught by a team of interdisciplinary instructors. Students spend only 1 hour per week in lecture, and 4.5 hours per week in labs where activities are mostly team-oriented. Students are assessed with equal weight on visual communication skills (technical drawing and sketching), oral and written communication skills (presentations, report writing as well as grammar and organization) and design (team project design performance, analysis and quality). However, most entering students in science and engineering believe there are unique answers to any problem, expect their instructors to know what those answer are, and expect their task to be memorizing and repeating those answers on tests4-7. Requiring students to take a course which emphasizes communication, teamwork and design often yields resentment and generates complaints such as “I went into engineering so I didn’t have to write and/or draw” and “If I had a teammember like this at a real job, he/she would be fired”. Explaining to students that what they practice in the course will be exactly what most of them will do as professionals can help to overcome some student resistance8. In addition, active learning has been shown to be an extremely effective way to improve student attitudes, increase motivation to learn and to improve Proceedings of the 2005 American Society for Engineering Education Annual Conference & Exposition Copyright © 2005, American Society for Engineering Education
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.127 | 0.045 |
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".