Bibliographic record
Abstract
This paper discusses the results of two experiments in self assessment and discusses their value in evaluating student consciousness of their competence, and the opportunity to improve self-awareness and competence in students. The data was gathered from two different engineering courses. The first experiment was conducted in a second-year course on basic electronics and electrical power. As part of the final examination, students were asked to assess their confidence in their answer to each question. The student self-assessment was compared to the actual result in an effort to determine the student’s perception of their competence. Student assessment was coded with respect to consciousness and competence. The second experiment was performed on a midterm examination in engineering ethics and professionalism, a senior course discussing the impact and interaction of the engineering profession on society. Students were given an annotated exemplar and a marking rubric and asked to grade their own midterm submissions. The student assessments were compared to the instructor assessment and again the results were coded with respect to consciousness and competence. The results showed a contrast between the second-year and senior courses. For the second-year course, 50.3% were coded as consciously competent or incompetent. In the senior course, 80% of students were coded as consciously competent. The comparison of the two results suggest that senior students, given suitable instruction, are more aware of their competence than junior students suggesting that current methods do develop an improved awareness of competence, although other factors may be relevant. It is suggested that student awareness be formally monitored, and results used to modify pedagogy to improve and accelerate consciousness in graduates.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".