Short Course

Using Learnersourcing to Improve Learning Experiences

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Beginner level

No prior experience required

Flexible schedule

1 week, 6 to 8 hours per week

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*Proof of full-time student enrollment required. Acceptable forms of ID include a letter from your university’s registrar office or an unofficial transcript. Email your documents to learnlab-help@lists.andrew.cmu.edu.

What you will learn

Explain how learnersourcing can improve course content, support, and feedback.
Design learnersourcing tasks that generate useful learner contributions.
Evaluate the quality and instructional value of learner-generated outputs.
Plan how to incorporate learnersourced content into ongoing course improvement.

Course description

Learners can contribute far more than answers. When designed well, their work can generate explanations, hints, examples, and feedback that improve the learning experience for future learners. Learnersourcing turns participation into a source of instructional value.

In this course, you will learn how to design learnersourcing activities that collect useful learner contributions and incorporate them into better educational experiences. The course focuses on what kinds of tasks produce valuable outputs, how to evaluate contribution quality, and how to use learnersourcing for continuous improvement.

Syllabus

Module 1: Learnersourcing

Define learnersourcing.
Identify tasks that are appropriate for learnersourcing.
Name key challenges with learnersourcing.
Order the key steps in implementing a learnersourcing task.
Explain the methods behind each key step in implementing a learnersourcing task.

Meet the instructor

Dr. Steven Moore

Assistant Professor
George Mason University

Steven Moore is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University. He studies how to design educational technologies that improve student learning and how people use AI to learn. Drawing on learning science, human-computer interaction, and applied natural language processing, he builds and evaluates AI-enhanced courseware and assessment tools. His work advances learnersourcing, crowdsourcing, and human-AI collaboration for content creation and feedback at scale. Recently, he has focused on using large language models to support instructional design by applying structured rubrics consistently across varied content types. His academic research is informed by extensive industry experience and consulting with universities and school districts.

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