Traffic Engineering in a Hybrid Format: More Learning with Less Meeting
Bibliographic record
Abstract
In the Civil Engineering curriculum at Cal Poly, CE 421 (Traffic Engineering) course 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 (f2f) lecture format. Based on the student feedback received at the end of the quarter and success in achieving learning outcomes, it was determined that the course should be more student-centered and there should be a 2-way feedback mechanism between students and instructor throughout the quarter. Based on this evaluation, the course was redesigned and taught in the new “hybrid” format during fall 2009 and spring 2010 quarters. This paper discusses how the lessons learned from hybrid redesign of a course in other fields of higher education can be applied in the context of a traffic engineering course. The hybrid format involved reduced f2f meeting time and included learner centered online activities. The material was ‘front-loaded’ for the students by using PowerPoint presentations with narrations so that they could come prepared for the in-class lecture. The online activities also included simulation and surveys demonstrating the variation in reaction time of drivers, videos for demonstrating the level of service (LOS) concept, and a new type of traffic control for intersections. These online demonstrations were followed with a survey for the students to fill out. The results from these surveys were then discussed in the class for achieving the underlying learning outcomes. A set of potential survey questions is provided in this paper as guidance for the instructors in the transportation engineering area.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".