CONCEPTS ONLY PLEASE! INNOVATING A FIRST YEAR ENGINEERING COURSE
Bibliographic record
Abstract
MSCI 100, a first year course dedicated to Management Engineering, introduces the main concepts of this discipline to students in their first term. The course’s main goals are introducing core principles that students will apply throughout their undergraduate studies and also preparing them for their first co-operative education term. In Fall 2015, this course was pedagogically redesigned based on authentic self-directed learning, accommodating different learners, and providing students with opportunities to develop professional skills (especially teamwork, project planning, time management and critical thinking).The course was designed holistically with emphasis on integrating concepts and communicating the course plan to students. Although engineering design was an inherent part of the course, there were no memory-driven tests and no math. The course’s learning outcomes were instead formulated around students’ understanding of improving effectiveness and efficiency in various facets of business through the development of their professional skills.There were numerous teaching innovations from the perspective of a first year engineering course. The essence of many course deliverables was for students to experience constructive failure-recovery cycles. This allowed them to learn from their mistakes as they completed case study challenges, hands-on activities, unique assessments and a final team project requiring integration of knowledge and skills. These activities were supported by various groups on campus.A panel of educators was formed near the end of the term so students could reflect on their learning process and be provided with the educators' feedback.. Moreover, the results from the course evaluations indicated that the restructuring of MSCI 100 was largely successful. Most students were able to fully grasp fundamental concepts and apply critical thinking skills. In this paper, we share reasons for redesigning the course, our experience in delivery and assessment, the impact of different teaching and learning methods and finally feedback on switching to in-depth, student-centered learning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.140 | 0.094 |
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".