Beginner level
No prior experience required
Writing Better Questions for 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
- Identify common flaws in questions used in online learning environments.
- Match question types to learning goals and intended evidence of understanding.
- Write clearer, more effective prompts for quizzes, checks for understanding, and discussions.
- Revise existing questions to improve alignment, clarity, and instructional value.
Course description
Questions shape what learners notice, how deeply they think, and what evidence instructors can gather about understanding. Yet many online courses rely on weak or misaligned questions that do not support the learning goals they are meant to assess.
In this course, you will learn how to write stronger questions for online learning by matching question types to goals, choosing better prompts, and avoiding common design flaws. The course focuses on practical improvements to quizzes, checks for understanding, and discussion prompts.
Syllabus
Module 1: Writing Better Questions for Online Learning
- Categorize a given question by type.
- Design effective questions that align with specific learning objectives and Bloom’s taxonomy levels.
- Design effective assessment interactions by selecting question types and feedback strategies that optimally support specific learning goals, treating each question format as a design affordance.
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
Evidence-Based Backward Design for Online Learning
Design online learning experiences by aligning goals, assessments, and instruction with evidence-based methods and iterative improvement.
Active Learning: Practice and Feedback
Create evidence-based active learning activities by choosing the right types of practice and feedback for different learning goals.
Introduction to Learning Engineering
Apply learning engineering principles to design effective e-learning and use the KLI framework to match instructional strategies to the right knowledge components and learning processes.