Beginner level
No prior experience required
Designing Guided Discovery Experiences
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
- Distinguish guided discovery from unguided exploration and direct instruction.
- Design activities that support exploration while preserving productive instructional structure.
- Sequence feedback and supports to help learners reason through discovery tasks.
- Evaluate guided discovery activities for clarity, challenge, and learning value.
Course description
Discovery can be powerful for learning, but unguided exploration often leaves learners confused or stuck. Guided discovery works when learners have enough structure, feedback, and support to explore productively while still doing meaningful intellectual work themselves.
In this course, you will learn how to design guided discovery experiences that balance exploration with instructional support. The course focuses on structuring tasks, sequencing feedback, and creating activities that help learners reason, test ideas, and learn through well-supported inquiry.
Syllabus
Module 1: Guided Discovery
- Articulate the benefits of guided discovery, where it applies, and where it does not.
- Design guided discovery interventions by leveraging effective feedback.
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
Active Learning: Practice and Feedback
Create evidence-based active learning activities by choosing the right types of practice and feedback for different learning goals.
Scaffolding and Fading in Course Design
Create learning supports that guide learners effectively, then fade those supports as learners build independence.
Teaching Problem-Solving and Thinking Skills
Design e-learning experiences that build problem-solving, critical thinking, creativity, and metacognitive skills.