Spring 2027

Quantitative Reasoning and Problem Solving

Quantitative Reasoning and Problem Solving develops competency in working with numerical data, solving quantitative problems in authentic contexts, and making logical arguments supported by quantitative evidence.

offering recorded3 credits
Recorded instructors · Spring 2027 Matt DeLong4.1/5

Summary

1 / 6

Past student reviews · Travis Olson

The class featured numerous tests, but they were easy and the instructor dropped a significant number of them.

What you’ll be able to do

  • Identify appropriate models to fit scenarios described with numerical data and/or verbal descriptions (e.g. related to basic financial, probabilistic, and statistical models).

  • Make predictions and draw conclusions in real world contexts using a model, and recognize the limitations of mathematical models in those contexts.

  • Understand and be able to create and evaluate arguments supported by quantitative evidence and clearly communicate those arguments using words, tables, graphs, mathematical equations, etc., as appropriate in various contexts (e.g. with problems related to financial planning, basic probability).

  • Construct and interpret graphical displays of data and understand how they can be used and misused.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
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F

All recorded terms · compare terms & instructors

Prerequisites

Course map

MATH 96or placement into MATH 141. MATH 118 does not fulfill the requisite

    • take one
    • MATH 118 does not fulfill the requisite
    take all
MATH 141

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • All of
    • Any of
    • MATH 118 does not fulfill the requisite

Professors

Spring 2027

Matt DeLong

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.59/5 from 22 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.

No course-specific feedback yet.

Historical instructors & teaching patterns

AARON GOLDSMITH is recorded teaching in Fall 2018, Spring 2019. Recorded history may be incomplete and does not establish a future schedule.

ABBISHEK DESHPANDE is recorded teaching in Fall 2020. Recorded history may be incomplete and does not establish a future schedule.

AMIR ASSADI is recorded teaching in Fall 2014, Spring 2016, Spring 2018. Recorded history may be incomplete and does not establish a future schedule.

AOBING LI is recorded teaching in Spring 2008. Recorded history may be incomplete and does not establish a future schedule.

DIANE RIVARD is recorded teaching in Fall 2013. Recorded history may be incomplete and does not establish a future schedule.

JOEL ROBBIN is recorded teaching in Fall 2006. Recorded history may be incomplete and does not establish a future schedule.

LEAH RINECK is recorded teaching in Fall 2021. Recorded history may be incomplete and does not establish a future schedule.

TRAVIS OLSON is recorded teaching in Fall 2024, Fall 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

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 1200

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

Meeting source records
Download part 1

Student experience

the class

Past student reviews · Travis Olson

Students described the course as rudimentary and laid-back, with accessible explanations that allowed a non-STEM major to earn a good grade.

Recent recorded grades — Spring 2025: 3.59 GPA, 79.3% A/AB (n=87 letter grades); Fall 2025: 3.59 GPA, 77.4% A/AB (n=217 letter grades); Spring 2026: 3.36 GPA, 68.5% A/AB (n=108 letter grades).

difficulty & workload

Past student reviews · Travis Olson

The class featured numerous tests, but they were easy and the instructor dropped a significant number of them.

Past student reviews · Travis Olson

There was no final exam unless a student had failed a previous test and wished to retake it.

Topics

  • Solving quantitative problems in authentic contexts and everyday life situations.
  • Basic financial, probabilistic, and statistical models.
  • Graphical displays of data.

Skills

  • Working with numerical data and identifying appropriate models for scenarios.
  • Creating and evaluating logical arguments supported by quantitative evidence.
  • Constructing and interpreting graphical displays of data.

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 · 0.26 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.

Download part 1
Selected offering source records
MATH 141 · Spring 2027

Quantitative Reasoning and Problem Solving

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": "1274:600:011615",
    "course_id": "MATH 141",
    "course_uid": "course_c9bdf631650ccfb896fcfc26",
    "term_id": "1274",
    "source_course_id": "011615",
    "source_subject_id": "600",
    "title": "Quantitative Reasoning and Problem Solving",
    "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.

Download part 1Download part 2Download part 3
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "b75a586dfaf33b4934bbce41c2b32341fce13ab1b5d18d24888ef279526e8eb2",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}