Bibliographic record
Abstract
Undergraduate engineering students are customarily tested for their content knowledge in exams which usually involve long-answer calculation-based questions. Although most programs use exam grades as a proxy for student knowledge uptake and evidence for attainment of CEAB graduate attributes, there is no guarantee that students have understood the underlying fundamental concepts let alone long-time retention of their newly acquired knowledge. This paper discusses the development of a yearly assessment in the form of a concept inventory that is administered once at the beginning of the academic year (pre-test) and repeated in about a year (post-test). The analysis of the results showed a normalized gain of 20% in student knowledge. Further analysis revealed that there are no statistically significant differences between the male and female students suggesting an overall equitable learning experience by gender in our program. The normalized gain for each core course of the curriculum was also calculated which serve as curricular feedback for instructors identifying areas where improved instructional methods would provide benefits. Finally, a report is produced for each student to provide them with valuable feedback on how well they learned and retained the fundamental concepts they learned over the past academic year.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".