Spring 2027

Introduction to Computing

Introduction to Computing provides a broad overview of computing, security, robotics, and AI, focusing on how computers work and algorithms solve problems via creative applications in a visual coding environment.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Jeff Nyhoff

Students in Jeff Nyhoff's class said the material was fairly simple and that showing up to lecture, following along, and doing homework were enough to earn high grades.

What you’ll be able to do

  • Develop a fundamental understanding of the key concepts of computer science in a variety of contexts.

  • Create art, music, stories, games and other programs in a visual, introductory programming language

  • Understand how computers use algorithms to solve problems and act in intelligent ways.

  • Understand how computers utilize large sets of data to provide insight and knowledge.

  • Learn how software and hardware make modern computers work

Source: UW Guide 1 UW Guide 2

Grade history

↗
average GPA
letter grades
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AB
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BC
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All recorded terms · compare terms & instructors

Prerequisites

Course map

MATH 96or placement into MATH 141. MATH 118 does not fulfill the prerequisite. Not open to students with credit forCOMP SCI 300 or 320

COMPSCI/LIS 102 used by

“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

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

JEFF NYHOFF is recorded teaching in Fall 2023, Spring 2024, 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

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 2000
LEC 001Classroom Instruction0 / 2000

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Past student reviews · Jeff Nyhoff

Students said Jeff Nyhoff's class was easy and boring, and one described lectures as long monologues about nothing.

Recent recorded grades — Spring 2025: 3.81 GPA, 88.6% A/AB (n=176 letter grades); Fall 2025: 3.93 GPA, 97.4% A/AB (n=195 letter grades); Spring 2026: 3.87 GPA, 94.3% A/AB (n=194 letter grades).

difficulty & workload

Past student reviews · Jeff Nyhoff

Students in Jeff Nyhoff's class said the material was fairly simple and that showing up to lecture, following along, and doing homework were enough to earn high grades.

Past student reviews · Jeff Nyhoff

One student said Jeff Nyhoff called them rude when they suggested posting assignments before class; another said he never responded to multiple emails.

Topics

  • Security, robotics, and artificial intelligence
  • How computers work and how algorithms solve problems
  • Creative applications in an introductory coding environment

Skills

  • Understanding how computers work and how algorithms solve problems
  • Creating creative applications in a visual, introductory programming language

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

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

1280 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

UW–Madison

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.

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Selected offering source records
COMPSCI/LIS 102 · Spring 2027

Introduction to Computing

Recorded 2026-10-14
COMPSCI/LIS 102 · Spring 2027

Introduction to Computing

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:266:023191",
    "course_id": "COMPSCI/LIS 102",
    "course_uid": "course_72fca04984de023263764214",
    "term_id": "1274",
    "source_course_id": "023191",
    "source_subject_id": "266",
    "title": "Introduction to Computing",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall, Spring"
  },
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:544:023191",
    "course_id": "COMPSCI/LIS 102",
    "course_uid": "course_72fca04984de023263764214",
    "term_id": "1274",
    "source_course_id": "023191",
    "source_subject_id": "544",
    "title": "Introduction to Computing",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall, Spring"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "b372d593ccff2f5973b78a49206121f3bed3d78c022c74481208af81cd77e42b",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}