Bibliographic record
Abstract
In the civil engineering curriculum at California Polytechnic State University, San Luis Obispo, the 421 traffic engineering course in civil engineering (CE) is intended to provide students with details of driver behavior, traffic characteristics, and design considerations for addressing traffic problems. In fall 2008, this class was taught in traditional face-to-face lecture format. On the basis of student feedback and success in achieving learning outcomes, it was determined that the course should be more student centered and that there should be a two-way feedback mechanism between students and instructor throughout the quarter. The course was redesigned and taught in the new hybrid format during the fall 2009 and spring 2010 quarters. This paper discusses how lessons learned from hybrid redesign of courses in other fields can be applied to a traffic engineering course. The CE 421 hybrid format involved reduced face-to-face meeting time and included learner-centered, online activities. The material was front-loaded for the students through PowerPoint presentations with narrations so that they could come prepared for the face-to-face lectures. The online activities also included simulation and surveys, which demonstrated the variation in reaction time of drivers, and videos that demonstrated the level-of-service concept and a new type of traffic control for intersections. After these online demonstrations, students were asked to fill out a survey. The results from these surveys were then discussed in class to achieve the underlying learning outcomes. A set of questions is provided as guidance for instructors who may be considering a similar redesign of their transportation engineering 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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.181 | 0.056 |
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".