A FRAMEWORK TO ADD DEPTH, CAREER RELEVANCE, AND SKILLS DEVELOPMENT INTO ASSESSMENT IN A 2ND YEAR DESIGN COURSE
Bibliographic record
Abstract
In a typical engineering classroom, there are many skills that students are expected to learn, develop and apply. Educators struggle on a regular basis to find meaningful ways to get students to develop skills. While it is possible to make large educational reforms in a program, sometimes change can be found in small and meaningful modifications to assessments. This paper focuses on simple, knowledge-based assessments used in accredited programs. An example is provided of a framework used in a second-year design course to transform simple assessments into opportunities for the development of deep skills, while at the same time managing educational resources and maintaining a focus on outcome-based assessment. The primary components of the framework are to tie in long term skills development, aspects of STEAM (specifically the arts elements), as well as aspects of career development and engineering identity into assignments, to allow students to contextualize their skill development into the broader understanding of their own career and identity development as an engineer.
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 | 0.026 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".