Topics in Rhetoric and Communication Science
COMARTS 310 explores various topics in rhetoric or communication science.
Summary
Historical reviews of Sara McKinnon: The course material is described as more than manageable, with all work conducted online via Canvas.
Grade history
↗letter grades
All recorded terms · compare terms & instructors
Prerequisites
Course mapSophomore standing
- Sophomore standing
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Sophomore standing
Professors
Fall 2026No instructors recorded for this selection.
Historical instructors & teaching patterns
Historical reviews for Dr. Sara McKinnon describe engaging course material and manageable workload. She provided clear grading rubrics and responded to student inquiries within 24 hours via Canvas.
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 |
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Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
No records available.
Student experience
the class
Historical reviews of Sara McKinnon: Dr. Sara McKinnon's COMARTS 310 offers engaging material and manageable workload in an online format, with responsive communication and clear grading.
Recent recorded grades — Fall 2024: 3.78 GPA, 92.3% A/AB (n=39 letter grades); Spring 2025: 3.61 GPA, 80.0% A/AB (n=80 letter grades); Fall 2025: 3.68 GPA, 84.4% A/AB (n=45 letter grades).
difficulty & workload
Historical reviews of Sara McKinnon: The course material is described as more than manageable, with all work conducted online via Canvas.
Historical reviews of Sara McKinnon: Students appreciate the instructor's responsiveness within 24 hours and the use of clear grading rubrics.
Topics
Grades
Fall 2026 · Projected
Before grades are released– average GPA
Approximate 80% prediction interval
About this estimate
The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.
The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.
8 earlier forecasts · 0.11 GPA average error.
Grades over time
Through Fall 2026
More grade details Grade mix, volume & source data
Where this course fits relative to
Latest available grades · Fall 2025 · 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
1320 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
No offering records for the selected term.
Raw records
[]
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": "78ad9b4e30ed83a3a28e7c59dd899569a38da4c5fa123d8a70702526de657dd9",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}