Sue Ahn teaches a course that is not hard but allows students to learn a lot, with mercy homework grading and clear feedback. Exams may be challenging, but attentive students who complete homework rarely miss points.
Transportation Engineering
CIVENGR 370 covers transportation engineering, focusing on supply/demand characteristics, demand estimation, system planning, and policy impacts.
Summary
Exams could be challenging, but students would hardly miss any point if they pay attention in class and do the homework.
Grade history
↗letter grades
All recorded terms · compare terms & instructors
Prerequisites
Course map(STAT 311, 324,I SY E 210, or concurrent enrollment), graduate/professional standing, or member of Engineering Guest Students
“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
Professors
Fall 2026Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Fall 2026Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LEC 001 | Classroom Instruction | 64 / 70 | 0 |
| LAB 301 | Classroom Instruction | 32 / 35 | 0 |
| LAB 302 | Classroom Instruction | 32 / 35 | 0 |
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.
Meeting source records
Student experience
the class
Sue Ahn's CIVENGR 370 is not hard but allows students to learn a lot, with clear feedback and manageable exams if they pay attention.
Recent recorded grades — Spring 2025: 3.41 GPA, 57.5% A/AB (n=40 letter grades); Fall 2025: 3.45 GPA, 57.9% A/AB (n=57 letter grades); Spring 2026: 3.50 GPA, 60.0% A/AB (n=45 letter grades).
difficulty & workload
Exams could be challenging, but students would hardly miss any point if they pay attention in class and do the homework.
Students appreciate the mercy homework grading and very clear feedback provided by the instructor.
Topics
Skills
Grades
Latest available · Spring 2026— not enough history to project Fall 2026.
Grade distribution · % of letter grades
Grades over time
Through Spring 2026
More grade details Grade mix, volume & source data
Where this course fits relative to
Latest available grades · Spring 2026 · all course levels
GPA
Higher than % of other courses in this group.
Course GPAs · red marks this course’s range
letter grades
More recorded grades than % of other courses in this group.
Typical course in this group: letter grades.
About this comparison
1283 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.
Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
Transportation Engineering
Recorded 2026-09-07Raw records
[
{
"run_id": "20260907T155543-ce3781c4",
"semester": "1272",
"observed_at": "2026-09-07 15:55:43.033547+00:00",
"offering_id": "1272:240:003338",
"course_id": "CIVENGR 370",
"course_uid": "course_7d8e7b53aa59c6c90fbf1f88",
"term_id": "1272",
"source_course_id": "003338",
"source_subject_id": "240",
"title": "Transportation Engineering",
"credits_min": 3,
"credits_max": 3,
"typically_offered": "Fall"
}
]Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesModel outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
"model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
"task_version": "14",
"output_id": "786c6a53cf8c121cc7b4eec56b239887bfb0870bbb4e86b2f0997bf080207c95",
"requirements_status": "needs_review",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}