Generation-Z Learning Approaches to Improve Performance in the Fundamentals of Engineering Exam
Bibliographic record
Abstract
The Fundamentals of Engineering (FE) exam is now computer-based, allowing examinees to schedule the test more conveniently. The FE is also discipline-specific, so students can focus more on areas related to their course of study. Traditional university FE review courses cover material throughout a semester, eliminating a part of the year where students would take the exam. By developing online learning modules, including short video reviews of particular topics, videos of worked sample problems, and a bank of FE-like problems, students can better prepare for the exam on a just-in-time basis. Redesigning the course to include 5-7 minute topic-specific video reviews, in-class mentoring, application, assessment strategies and more interactive exercises better engages current students, sometimes called Generation Z (GenZ), who are familiar with YouTube, Khan Academy, and other topic-targeted websites. Rather than longer classes with little interaction, students can focus on areas where their knowledge needs improving, view (and re-view) the topic-related videos, and explore example problems on their own, in conjunction with interactive in-class activities. In parallel with subject assessments delivered through our learning management system, we were able to correlate frequency of student viewings of related video reviews to evaluate the overall impact on student performance. This feedback helped the design/development team identify subject areas that students were struggling in. Post-course surveys indicated that students found using the videos and online example problems to be both motivating and instructionally effective. This redesigned approach to the FE review course has been used in consecutive semesters, with encouraging results, and is currently being incorporated in other engineering and computer science courses.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".