Designing for the Mind: Cognitive Science in Online Learning
Start Date
November 2025
End Date
November 2025
Keywords
Cognitive Load Theory, Online Course Design, Credit Hour Analysis, Workload Balancing, Bloom’s Taxonomy, Learning Outcomes, Instructional Design, Student Engagement, Assessment Strategies, Online Learning
Abstract
Designing online courses requires more than aligning content with credit hour standards; it demands intentional balance between workload, learning outcomes, and cognitive processing. This session explores how principles from cognitive science, such as Cognitive Load Theory, Bloom’s Taxonomy, and the Spacing Effect, can guide faculty in building online and hybrid courses that foster deep learning while avoiding overload and redundancy. Drawing on my work as an instructional design consultant and faculty developer, I will demonstrate a systematic approach to workload balancing, cognitive demand distribution, and outcome alignment across different course formats (traditional, accelerated, and compressed). Participants will also see how an AI-driven “credit analysis chatbot” can support faculty in evaluating workload and optimizing course design in real time. Faculty and instructional designers will leave with practical tools for mapping outcomes to assessments, scaffolding complex tasks, and creating learner-centered digital environments that sustain engagement and improve performance.
Learning Outcomes
By the end of this session, participants will be able to:
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Apply credit hour standards to online course design.
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Analyze the cognitive demands of learning activities using Cognitive Load Theory.
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Map outcomes to Bloom’s Taxonomy for visible cognitive progression.
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Identify and address points of overload or redundancy in course design.
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Use AI tools to support workload analysis and optimize instructional design.
Track
Evidence-Based Practice
Session Type
50-Minute Session
Designing for the Mind: Cognitive Science in Online Learning
Designing online courses requires more than aligning content with credit hour standards; it demands intentional balance between workload, learning outcomes, and cognitive processing. This session explores how principles from cognitive science, such as Cognitive Load Theory, Bloom’s Taxonomy, and the Spacing Effect, can guide faculty in building online and hybrid courses that foster deep learning while avoiding overload and redundancy. Drawing on my work as an instructional design consultant and faculty developer, I will demonstrate a systematic approach to workload balancing, cognitive demand distribution, and outcome alignment across different course formats (traditional, accelerated, and compressed). Participants will also see how an AI-driven “credit analysis chatbot” can support faculty in evaluating workload and optimizing course design in real time. Faculty and instructional designers will leave with practical tools for mapping outcomes to assessments, scaffolding complex tasks, and creating learner-centered digital environments that sustain engagement and improve performance.