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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".