Rubrics for assessing oral communication in the capstone design experience: development, application, analysis and refinement
Bibliographic record
Abstract
The importance of good communication skills is becoming increasingly relevant to engineers in today's globally competitive environment. The Accreditation Board for Engineering and Technology (ABET), recognizing this phenomenon, introduced six professional skills along with the various hard skills in their new accreditation criteria EC2000 for all engineering programs. At the Milwaukee School of Engineering (MSOE), rubrics were developed to aid in assessing the oral presentations made during the capstone senior design sequence. These rubrics have been applied by various senior design professors each quarter to assess all the mid-quarter presentations. The analysis (using the Spearman Rank Correlation Test and a Rater Disagreement Metric) of data collected over four quarters indicates that by repeatedly applying, analyzing and refining a rubric, it is possible to minimize the often subjective means of evaluating communication skills and move towards more objective evaluations. Over the past three years, multiple evaluators have shown strong agreement in the quality of student presentations. However, they have not yet arrived at a complete consensus indicating that we as yet do not have a completely reliable and objective tool and more work needs to be done in this area.
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 | 0.045 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".