Beginner level
No prior experience required
Designing for Help-Seeking in Online Learning
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 why help-seeking matters for effective learning in online environments.
- Identify design features that encourage or discourage productive help-seeking.
- Create supports that help learners find, interpret, and use help effectively.
- Design learning experiences that balance help availability with learner independence.
Course description
Help-seeking is a critical learning behavior, yet many online environments either fail to support it or unintentionally discourage it. Designing for productive help-seeking means making help visible, timely, and worth using without promoting dependence.
In this course, you will learn how to design online learning environments that encourage productive help-seeking. You will examine when learners seek help, what kinds of support are effective, and how to structure interfaces and instructional cues so learners can find and use help well.
Syllabus
Module 1: Fostering Help-Seeking
- Identify the different stages in the help-seeking process.
- Identify strategies that improve help-seeking in each stage of the help-seeking process.
- Describe strategies to leverage a learner’s prior knowledge to assist in the help-seeking process.
- Evaluate the extent to which an existing tool fosters help-seeking behavior.
Meet the instructor
Dr. Steven Moore
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.
Where to go next
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