Spring 2027

Hierarchical Linear Modeling

Introduction to hierarchical linear modeling, covering random intercept/slope models, longitudinal data, and multilevel generalized linear models.

offering recorded3 credits
Recorded instructors · Spring 2027 Jee-Seon Kim

Summary

1 / 3

Recent recorded grades — Spring 2024: 3.90 GPA, 89.7% A/AB (n=29 letter grades); Spring 2025: 3.97 GPA, 100.0% A/AB (n=19 letter grades); Spring 2026: 3.94 GPA, 100.0% A/AB (n=16 letter grades).

What you’ll be able to do

  • Identify the properties of clustered data and explain statistical treatments of multilevel data

  • Summarize and visualize multilevel data within and across levels

  • Select and apply multilevel models that are appropriate for various forms of clustered data

  • Interpret multilevel analysis results and communicate statistical findings

  • Write a project report for multilevel analysis including program code

Source: UW Guide

Grade history

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

Prerequisites

Course map

ED PSYCH 763

EDPSYCH 964

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

Prerequisite text tree

Professors

Spring 2027

Recent recorded grades — Spring 2024: 3.90 GPA, 89.7% A/AB (n=29 letter grades); Spring 2025: 3.97 GPA, 100.0% A/AB (n=19 letter grades); Spring 2026: 3.94 GPA, 100.0% A/AB (n=16 letter grades).

Historical instructors & teaching patterns

JEE-SEON KIM is recorded teaching in Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2022, Spring 2024, Spring 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

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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 2024: 3.90 GPA, 89.7% A/AB (n=29 letter grades); Spring 2025: 3.97 GPA, 100.0% A/AB (n=19 letter grades); Spring 2026: 3.94 GPA, 100.0% A/AB (n=16 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Random intercept and random slope models
  • Longitudinal data models
  • Multilevel generalized linear models
  • Clustered data properties and statistical treatments

Skills

  • Apply hierarchical linear modeling techniques including random intercept, random slope, and multilevel generalized linear models.
  • Summarize and visualize multilevel data within and across levels.
  • Interpret multilevel analysis results and communicate statistical findings.
  • Write project reports for multilevel analysis including program code.

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

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 964 · Spring 2027

Hierarchical Linear Modeling

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": "1274:315:022790",
    "course_id": "EDPSYCH 964",
    "course_uid": "course_e1b937d204037746c36a0f8f",
    "term_id": "1274",
    "source_course_id": "022790",
    "source_subject_id": "315",
    "title": "Hierarchical Linear Modeling",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Occasionally"
  }
]
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Student reviews

Original comments behind the course and instructor summaries.

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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": "24761f2c4b7a66ce9747c6f183b59ed145b6d4a02d933bba34055962f4896c52",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}