Recent recorded grades — Spring 2025: 3.88 GPA, 96.6% A/AB (n=29 letter grades); Fall 2025: 3.86 GPA, 92.0% A/AB (n=25 letter grades); Spring 2026: 3.89 GPA, 96.3% A/AB (n=27 letter grades).
Introducing Computer Science to K-12 Students
COMPSCI 402 teaches students to lead CS clubs and workshops for K-12 students, designing activities to teach computational thinking and programming.
Summary
Recent recorded grades — Spring 2025: 3.88 GPA, 96.6% A/AB (n=29 letter grades); Fall 2025: 3.86 GPA, 92.0% A/AB (n=25 letter grades); Spring 2026: 3.89 GPA, 96.3% A/AB (n=27 letter grades).
Grade history
↗letter grades
All recorded terms · compare terms & instructors
Prerequisites
Course mapCOMP SCI 200, 220, 300, 301, 302, 310, 367, placement into COMP SCI 300, orL I S/COMP SCI 102(COMP SCI 202 prior to Fall 2023), graduate/professional standing, or declared in the Capstone Certificate in Computer Sciences for Professionals
- COMPSCI 200
- COMPSCI 220
- COMPSCI 300
- 301
- 302
- COMPSCI 310
- 367
- placement into COMP SCI 300
- L I S/COMP SCI 102
- graduate/professional standing
- declared in the Capstone Certificate in Computer Sciences for Professionals
- COMP SCI 202 prior to Fall 2023
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Any of
- COMPSCI 200
- COMPSCI 220
- COMPSCI 300
- 301
- 302
- COMPSCI 310
- 367
- placement into COMP SCI 300
- L I S/COMP SCI 102
- graduate/professional standing
- declared in the Capstone Certificate in Computer Sciences for Professionals
- COMP SCI 202 prior to Fall 2023
Professors
Fall 2026Historical instructors & teaching patterns
PETER KIRSCHMANN is recorded teaching in Fall 2024, Spring 2025, Fall 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.
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 | 16 / 35 | 1 |
| LAB 301 | Classroom Instruction | 3 / 3 | 0 |
| LAB 310 | Classroom Instruction | 3 / 3 | 1 |
| LAB 321 | Classroom Instruction | 2 / 3 | 0 |
| LAB 331 | Classroom Instruction | 2 / 3 | 0 |
| LAB 334 | Classroom Instruction | 3 / 6 | 0 |
| LAB 341 | Classroom Instruction | 0 / 0 | 0 |
| LAB 324 | Classroom Instruction | 3 / 3 | 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
Recent recorded grades — Spring 2025: 3.88 GPA, 96.6% A/AB (n=29 letter grades); Fall 2025: 3.86 GPA, 92.0% A/AB (n=25 letter grades); Spring 2026: 3.89 GPA, 96.3% A/AB (n=27 letter grades).
difficulty & workload
No workload feedback recorded.
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
Not enough comparable courses for Fall 2026 in UW–Madison.
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
Introducing Computer Science to K-12 Students
Recorded 2026-09-07Raw records
[
{
"run_id": "20260907T155543-ce3781c4",
"semester": "1272",
"observed_at": "2026-09-07 15:55:43.033547+00:00",
"offering_id": "1272:266:023793",
"course_id": "COMPSCI 402",
"course_uid": "course_2102bb0ff9049a47df41645c",
"term_id": "1272",
"source_course_id": "023793",
"source_subject_id": "266",
"title": "Introducing Computer Science to K-12 Students",
"credits_min": 2,
"credits_max": 2,
"typically_offered": "Fall, Spring"
}
]Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
No records available.
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": "dadea09c273b8b5cd9020b8986f30019d26612d21c520472802d66bf30f3078e",
"requirements_status": "needs_review",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}