Practice makes performance: using a practice test to improve FE participation and pass rate
Bibliographic record
Abstract
The Cedarville University Engineering Department has undertaken an effort to both encourage and prepare students to participate in the Fundamentals of Engineering (FE) Exam. The program utilizes a mandatory practice exam and timely feedback, including comparison to the FE pass rate for previous classes. The goal was to create an internal assessment tool that would encourage the great majority of students to voluntarily participate in the FE Exam, to take the FE exam seriously, and therefore, successfully. This practice exam is administered early in the winter quarter of the students' senior year. It is one-half the length of the FE exam and is divided into two parts, one general and the other discipline-specific. Data from four years' experience shows a strong correlation between student scores and performance on the FE exam. Collectively, the pass rate for students in this program has been greater than 90%, consistently exceeding the state and national averages. More than 80% of the graduating students voluntarily participate in the FE exam. Because of the previously mentioned correlation of the practice exam results to the FE exam results, students can reasonably predict their performance before taking the exam. A secondary result is that the engineering department can assess their program, predicting the likely FE pass rate for those students who opt not to take it. The practice exam is used as one piece of the department's overall assessment plan. This paper evaluates and discusses the drawbacks and shortcomings of this approach.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".