Journey Continues: Piloting Competency-based Assessment in a First-year Engineering Course on Ethics, Communication, and Creative Problem Solving
Bibliographic record
Abstract
Renaissance Engineering 1 is a first-year engineering course that is the “flagship course” of Lassonde School of Engineering, where students are introduced to essential concepts and practices in ethics, communication, and creative problem solving. It is a large course that impacts over 600 students per year. Since Fall 2020, partly as a response to the pandemic, we fundamentally transformed the content and delivery of the course. This year, we have continued this transformative journey with an emphasis on reinventing the assessment approach. The limitations of normative grading are wellknown in the education field. Specifically, to our situation, the appropriateness of this practice in professional education where the goal is to ensure every student acquires the necessary competence, is suspect. Specification grading bridges normative and competencybased grading paradigms and has been shown to be effective in the engineering education setting. We applied specification grading to Renaissance Engineering 1. In all assignments, including the final case study, students are asked to satisfy a number of requirements distributed across four levels of competencies: Level 1: Foundational requirements for being a well-adjusted citizen, Level 2: Foundational requirements for being a contributing engineer, Level 3: Advanced requirements for being a well adjusted citizen, and Level 4: Advanced requirements for being a contributing engineer. Students are assigned grades from D to A based on their requirement satisfaction. Students have a limited number of chances to revise and resubmit their work if they have failed to satisfy all requirements in order to demonstrate competency. If they fail to meet multiple level 1 requirements after resubmission, they will fail the course. During the Fall-2021 term, we faced a number of unexpected challenges and surprises. Compared to previous years, this cohort - having experienced tremendous difficulties through the pandemic - were more tentative and insecure and took to a new grading scheme with notable trepidation initially. Surprisingly, many students had notable difficulty following clear written instructions, which is likely another pandemic-induced abnormality. Nevertheless, the majority of the students became comfortable with the scheme by the end of the term and achieved satisfactory learning outcomes. Significantly, while the majority of the students (~58%) achieved A or B grades, a significant minority (~18%) of students had failed the course. The course is offered to a new cohort of students in Winter 2022. Following a system thinking approach, we adjusted the grading scheme implementation based on our experience and learnings from the Fall-2021 term through winter term that led us to new and consistent findings. However, the benefits of specification grading in ensuring students meet critical competencies is particularly relevant for a professional education program such as engineering. Indeed, the bimodal grade distribution calls into question the status quo of normbased grading and calls for further research on assessment schema in engineering.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".