Fall 2026

Programming II

Introduces object-oriented programming, data structures, and algorithmic design using Java.

offering recorded3 credits
Recorded instructors · Fall 2026 Hobbes Legault4.4/5Ben JacobsenCole Nelson

Summary

1 / 6

The course features weekly long programming assignments or cumulative tests, with one reviewer noting that exams can be tough and require office hour support.

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Satisfied QR-A and (COMP SCI 200, 220, 302, 310, 301, or placement into COMP SCI 300) or (E C E/​COMP SCI 252 and E C E 203); graduate/professional standing; declared in Capstone Certificate in COMP SCI. Not open to students with credit for COMP SCI 367.

COMPSCI 300 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

Fall 2026

Hobbes Legault

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.54/5 from 93 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

RMP profile ↗ · All captured review dates; profile matched by name.

Hobbes Legault is described as accessible and caring, with engaging lectures that help clarify content. Reviewers appreciate her responsiveness to questions and clear course structure. Some note that programming assignments can be vague or that the course format has specific demands, but her teaching style is viewed positively.

Historical instructors & teaching patterns

Historical instructor Laura Legault is described as fun and thorough in addressing questions, while Mark Mansi provides engaging, to-the-point lectures with on-the-spot examples. Both instructors receive high praise for clarity and student support.

ALEXANDER BROOKS is recorded teaching in Spring 2018, Fall 2018. Recorded history may be incomplete and does not establish a future schedule.

BLERINA GKOTSE is recorded teaching in Fall 2024. Recorded history may be incomplete and does not establish a future schedule.

GARY DAHL is recorded teaching in Fall 2017, Spring 2018, Fall 2018, Spring 2019, Fall 2019, Spring 2020. Recorded history may be incomplete and does not establish a future schedule.

JEFF NYHOFF is recorded teaching in Fall 2022. Recorded history may be incomplete and does not establish a future schedule.

LAURA LEGAULT is recorded teaching in Spring 2020, Fall 2020, Fall 2021, Spring 2022, Fall 2022, Spring 2023, Fall 2023, Spring 2024, Fall 2024, Spring 2025, Fall 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

MARK MANSI is recorded teaching in Fall 2023. Recorded history may be incomplete and does not establish a future schedule.

MOUNA AYARI BEN HADJ KACEM is recorded teaching in Spring 2018, Fall 2018, Spring 2019, Fall 2019, Spring 2020, Fall 2020, Fall 2021, Spring 2022, Fall 2022, Spring 2023, Fall 2023, Spring 2024, Spring 2025. 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 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction146 / 1500
LEC 002Classroom Instruction261 / 2651
LEC 003Classroom Instruction64 / 605

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

Reviewers describe Hobbes Legault as an accessible instructor with engaging lectures, though one notes the course covers basic object-oriented programming rather than advanced topics.

Recent recorded grades — Spring 2025: 3.00 GPA, 40.1% A/AB (n=593 letter grades); Fall 2025: 3.37 GPA, 62.9% A/AB (n=556 letter grades); Spring 2026: 3.17 GPA, 48.3% A/AB (n=478 letter grades).

difficulty & workload

The course features weekly long programming assignments or cumulative tests, with one reviewer noting that exams can be tough and require office hour support.

Students find the projects engaging and the professor helpful, but some report that programming assignments are vague and the mid-year project was poorly planned.

Topics

  • Object-oriented design, classes, objects, interfaces, and iterators.
  • Abstract data types including lists, stacks, queues, priority queues, and binary search trees.
  • Searching and sorting algorithms.
  • Complexity analysis and recursion.

Skills

  • Object-oriented programming using classes and objects.
  • Array-based and linked data structures (lists, stacks, queues).
  • Writing multi-class programs using interfaces, generics, and exception handling.
  • Object-oriented design, iterators, searching, sorting, ADTs, complexity analysis, and recursion.

Grades

Historical instructor

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.

7 earlier forecasts · 0.13 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 · 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

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 300 · Fall 2026

Programming II

Recorded 2026-09-07
Raw records 
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:266:024795",
    "course_id": "COMPSCI 300",
    "course_uid": "course_28c3390ba944d49fd17f7c72",
    "term_id": "1272",
    "source_course_id": "024795",
    "source_subject_id": "266",
    "title": "Programming II",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall, Spring, Summer"
  }
]
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 2026-09-07

View the dataset on Hugging Face
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Download part 1Download part 2
Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "cdf54e305274426413326e4e99e99b776f7d9cba57dab7e44f04538c99bc248c",
  "requirements_status": "needs_review",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}