Spring 2027

Elementary Matrix and Linear Algebra

MATH 340 introduces linear algebra: matrices, vector spaces, transformations, eigenvalues, and orthogonality.

offering recorded3 credits
Recorded instructors · Spring 2027 Kaitlyn Phillipson4.2/5Autumn Kent2.5/5

Summary

1 / 6

Past student reviews · Kaitlyn Phillipson

The workload was described as fairly light, though one student noted the grade criteria is strict but clearly communicated throughout the semester.

What you’ll be able to do

  • Recall and state the formal definitions, properties, and theorems associated to elementary linear algebra (e.g., matrix, eigenvector, rank, linear independence, vector space, etc.).

  • Verify if a mathematical object has a given property used in elementary linear algebra (e.g., that a matrix is invertible, that a set is a vector subspace, that a vector is an eigenvector, etc.).

  • Check the premises of theorems used in elementary linear algebra in order to apply their conclusions (e.g., that a given matrix has zero determinant and therefore cannot be inverted).

  • Resolve algebraic statements related to elementary linear algebra through appropriate computations.

  • Express informal mathematical arguments in English using appropriate mathematical terminology and notation.

Source: UW Guide

Grade history

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

All recorded terms · compare terms & instructors

Prerequisites

Course map

MATH 222. Not open to students with credit forMATH 341, 345, or 375

MATH 340 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

Kaitlyn Phillipson

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.40/5 from 57 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.

For this course: 5.0/5 raw quality · 2.8/5 difficulty · 15 reviews

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

Past student reviews · Kaitlyn Phillipson

Kaitlyn Phillipson taught MATH 340 with interactive lectures and posted note templates that facilitated studying. Students found the practice tests and homework to be good predictors of exam content. The grading criteria were described as strict but clear, while one student noted the workload was fairly light.

Recent recorded grades — Fall 2022: 3.03 GPA, 43.8% A/AB (n=89 letter grades); Spring 2023: 3.17 GPA, 51.6% A/AB (n=124 letter grades); Fall 2023: 3.17 GPA, 50.7% A/AB (n=215 letter grades).

Autumn Kent

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 2.27/5 from 104 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.

For this course: 3.7/5 raw quality · 2.3/5 difficulty · 7 reviews

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

Past student reviews · Autumn Kent

Autumn Kent taught MATH 340 with exams that were straightforward and focused on applications rather than proofs. She explicitly told students what problems to expect, though one student found the lectures dry and lacking intuitive instruction on abstract concepts. That same student reported hard midterms and no homework solutions, making it difficult to get an A, while others noted the material was manageable.

Recent recorded grades — Fall 2017: 2.92 GPA, 41.6% A/AB (n=214 letter grades); Fall 2018: 3.01 GPA, 46.2% A/AB (n=338 letter grades).

Historical instructors & teaching patterns

Past student reviews · Alexandra Kjuchukova

Historically, Alexandra Kjuchukova assigned difficult homework without quizzes, and students noted that exams often consisted of true/false proof questions. She described her lectures as acceptable but sometimes confusing, suggesting that completing all homework independently prepared students well for her assessments.

ALEXANDRA KJUCHUKOVA is recorded teaching in Fall 2015. Recorded history may be incomplete and does not establish a future schedule.

AUTUMN KENT is recorded teaching in Fall 2017, Fall 2018. Recorded history may be incomplete and does not establish a future schedule.

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

FLORIAN BERTRAND is recorded teaching in Spring 2010, Fall 2010. Recorded history may be incomplete and does not establish a future schedule.

GARY HOWARD SCHROEDER is recorded teaching in Fall 2019. Recorded history may be incomplete and does not establish a future schedule.

GREGORY SHINAULT is recorded teaching in Spring 2013. Recorded history may be incomplete and does not establish a future schedule.

HONGCHANG JI is recorded teaching in Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

JIUHUA HU is recorded teaching in Fall 2021, Spring 2023. Recorded history may be incomplete and does not establish a future schedule.

KAITLYN PHILLIPSON is recorded teaching in Fall 2022, Spring 2023, Fall 2023. Recorded history may be incomplete and does not establish a future schedule.

MUSTAFA KALAFAT is recorded teaching in Spring 2008, Spring 2010. Recorded history may be incomplete and does not establish a future schedule.

NICHOLAS DERR is recorded teaching in Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

TREVOR LESLIE is recorded teaching in Fall 2019. Recorded history may be incomplete and does not establish a future schedule.

XIUXIONG CHEN is recorded teaching in Fall 2006. 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 002Classroom Instruction0 / 1110
LEC 001Classroom Instruction0 / 1200
LEC 003Classroom Instruction0 / 870
DIS 303Classroom Instruction0 / 300
DIS 343Classroom Instruction0 / 300
DIS 302Classroom Instruction0 / 300
DIS 304Classroom Instruction0 / 300
DIS 321Classroom Instruction0 / 300
DIS 322Classroom Instruction0 / 300
DIS 323Classroom Instruction0 / 300
DIS 342Classroom Instruction0 / 300
DIS 305Classroom Instruction0 / 300
DIS 325Classroom Instruction0 / 300
DIS 345Classroom Instruction0 / 300
DIS 301Classroom Instruction0 / 300
DIS 344Classroom Instruction0 / 300
DIS 324Classroom Instruction0 / 300
DIS 309Classroom Instruction0 / 300
DIS 310Classroom Instruction0 / 300
DIS 326Classroom Instruction0 / 300

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 · Kaitlyn Phillipson

Kaitlyn Phillipson delivers energetic, engaging lectures with posted note templates and recordings, making the material highly accessible for students.

Recent recorded grades — Spring 2025: 2.98 GPA, 40.7% A/AB (n=621 letter grades); Fall 2025: 2.92 GPA, 40.0% A/AB (n=608 letter grades); Spring 2026: 2.87 GPA, 40.1% A/AB (n=469 letter grades).

difficulty & workload

Past student reviews · Kaitlyn Phillipson

The workload was described as fairly light, though one student noted the grade criteria is strict but clearly communicated throughout the semester.

Past student reviews · Kaitlyn Phillipson

Students found the tests fair and well-predicted by practice problems, while TAs provided thorough explanations and were consistently available for questions.

Topics

  • Matrix algebra and linear systems
  • Vector spaces and subspaces
  • Linear dependence, span, basis, and rank
  • Determinants and invertibility
  • Linear transformations, kernels, ranges, and coordinate representations
  • Eigenvalues, eigenvectors, and diagonalization
  • Inner products, orthogonality, and symmetric matrices

Skills

  • Recall and state formal definitions, properties, and theorems in elementary linear algebra.
  • Verify whether mathematical objects satisfy linear algebra properties such as invertibility, subspace status, or eigenvector status.
  • Check theorem hypotheses before applying conclusions in linear algebra contexts.
  • Perform computations to resolve algebraic statements involving matrices, determinants, eigenvalues, and related structures.
  • Express mathematical arguments in English using correct terminology and notation.
  • Analyze matrix rank, basis, span, and linear dependence.

Grades

Historical instructor

Spring 2027 · 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 · 0.20 GPA average error.

Grades over time

Through Spring 2027

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
MATH 340 · Spring 2027

Elementary Matrix and Linear Algebra

Recorded 2026-10-29
Raw records 
[
  {
    "run_id": "20261029T213040-0fa85c20",
    "semester": "1274",
    "observed_at": "2026-10-29 21:30:40.248904+00:00",
    "offering_id": "1274:600:011655",
    "course_id": "MATH 340",
    "course_uid": "course_eec4aae06b946a73f1878be4",
    "term_id": "1274",
    "source_course_id": "011655",
    "source_subject_id": "600",
    "title": "Elementary Matrix and Linear Algebra",
    "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 2026-10-29

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.

Download part 1Download part 2
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "b99a7d7ef311bc65feebd29cf32757e0ac4be5fcca36d30da2b71f1498fc36a7",
  "requirements_status": "valid",
  "dataset_revision": "c9cd06eaa45f9ddf7e69faa38b89ff3d3dd995dd",
  "observed_at": "2026-10-29 21:30:40.248904+00:00"
}