Practice makes performance: using a practice test to improve FE participation and pass rate
Why this work is in the frame
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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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it