Bibliographic record
Abstract
Assessing graduate attributes in large classes is a time consuming task. The assessment requires a carefully designed random sampling that ensures the sample is representative of all students in the class. In addition, the assessment becomes more difficult when soft-skill graduate attributes are involved. The purpose of this paper is to present an efficient method for assessing graduate attributes in large classes without sampling. The proposed method involves defining an indicator (learning objective) by knowledge elements (topics) that the student should know or by interaction elements in a case study that represent the principles related to the indicator. Multiple-choice questions are then developed for the knowledge or interaction elements and processed using scantron sheets. The method involves a weighted-score procedure and performance scales for determining class performance. Application of the method for assessing two graduate attributes (lifelong learning and professionalism) in a fourth-year common engineering course is illustrated in this paper. The results show that class performance is sensitive to the weights assigned to the questions and therefore these weights should be carefully established by the instructors. The proposed method has shown to be useful in identifying the indicators and the specific topics within the indicator that need improvements.
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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 | 0.018 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".