Spring 2027

Fundamental Statistics in Learning Analytics

Fundamental Statistics in Learning Analytics covers logic and methods of statistics for learning analytics, emphasizing concepts and application over computational details to build inferential statistics fundamentals.

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Recorded instructors · Spring 2027 No instructors listed

Summary

What you’ll be able to do

  • Explore the foundations and practical use of statistics in learning analytics practice and research

  • Understand how to apply quantitative reasoning to learning analytics topics

  • Practice working with introductory descriptive statistics, probability, and statistical inference

  • Acquire a working fluency with statistical theories, concepts, and terminology in learning analytics

Source: UW Guide

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Prerequisites

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Graduate/professional standing

  • Graduate/professional standing
EDPSYCH 505

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  • Graduate/professional standing

Professors

Spring 2027

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Calendar & sections

Spring 2027

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Student experience

the class

Logic and methods of statistics used in the field of learning analytics. Emphasizes concepts and application rather than computational details to build fundamentals in the areas of inferential statistics. Reliability and validity, scalability, and the implications of inference in applied statistical methodology.

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Topics

  • Logic and methods of statistics in learning analytics
  • Inferential statistics
  • Reliability, validity, scalability, and implications of inference
  • Descriptive statistics, probability, and statistical inference

Skills

  • Fundamentals of inferential statistics
  • Reliability, validity, and scalability in applied statistical methodology
  • Applying quantitative reasoning to learning analytics topics
  • Working fluency with statistical theories, concepts, and terminology in learning analytics

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Sources & history

UW–Madison

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Dataset & model

Recorded 2026-10-14

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Model outputs & technical records
Qwen/Qwen3.8-27B
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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "1e4bd0c1097eb71b13e0508678d9d0a7afdad0be573c371394d536f47297e5e1",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}