Spring 2027

Item Response Theory Methods and Applications

Introduction to Item Response Theory (IRT) as a measurement framework, covering quantitative foundations, model types, estimation methods, and applications like test construction and adaptive testing.

offering recorded3 credits
Recorded instructors · Spring 2027 Daniel Bolt

Summary

1 / 2

Recent recorded grades — Spring 2008: 3.94 GPA, 100.0% A/AB (n=9 letter grades); Spring 2011: 3.92 GPA, 100.0% A/AB (n=6 letter grades); Spring 2020: 4.00 GPA, 100.0% A/AB (n=12 letter grades).

What you’ll be able to do

  • Acquire a strong foundation in current and past theories, research findings, and methodologies in their program area.

  • Utilize critical thinking skills to synthesize existing knowledge, evaluate strengths and limitations in existing theory and research.

  • Identify issues in need of additional inquiry including conceptual and methodological approaches available to address these issues.

Source: UW Guide

Grade history

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

Prerequisites

Course map

ED PSYCH 761 or PSYCH 610

EDPSYCH 871

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

Prerequisite text tree

Professors

Spring 2027

Recent recorded grades — Spring 2008: 3.94 GPA, 100.0% A/AB (n=9 letter grades); Spring 2011: 3.92 GPA, 100.0% A/AB (n=6 letter grades); Spring 2020: 4.00 GPA, 100.0% A/AB (n=12 letter grades).

Historical instructors & teaching patterns

DANIEL BOLT is recorded teaching in Spring 2008, Spring 2011, Spring 2020. 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

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SectionModeEnrolled / capacityWaitlist
LEC 001Classroom 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

Recent recorded grades — Spring 2008: 3.94 GPA, 100.0% A/AB (n=9 letter grades); Spring 2011: 3.92 GPA, 100.0% A/AB (n=6 letter grades); Spring 2020: 4.00 GPA, 100.0% A/AB (n=12 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Quantitative foundations of IRT and comparison with classical test theory and factor analysis.
  • Dichotomous, polytomous, and multidimensional IRT models.
  • Maximum likelihood and Bayesian estimation techniques.
  • Applications including test construction, short forms, DIF, and computerized adaptive testing.
  • Contemporary innovations involving process-related data and novel item formats.

Skills

  • Analyze dichotomous, polytomous, and multidimensional item response theory models.
  • Apply maximum likelihood and Bayesian estimation methods for IRT parameters.
  • Evaluate IRT applications such as test construction, short form development, DIF, and CAT.

Grades

Latest available · Spring 2020— 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 2020

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

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
EDPSYCH 871 · Spring 2027

Item Response Theory Methods and Applications

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": "1274:315:006246",
    "course_id": "EDPSYCH 871",
    "course_uid": "course_0ff2e0a3f6507ed8a757a075",
    "term_id": "1274",
    "source_course_id": "006246",
    "source_subject_id": "315",
    "title": "Item Response Theory Methods and Applications",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Occasionally"
  }
]
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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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 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "89fca2e1fcf7039eedf8041fc53b65bd31d0b5debb31888a5582f40048f3bad2",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}