Cognitive Load Theory (CLT) is a framework that explains how people store and retain new information by linking working memory and long-term memory to instructional practices.

Origin: Developed in the late 1980s by John Sweller and colleagues CLT is grounded in human cognitive architecture, in other words, the structure and processes of the mind that enable us to learn, think, and solve problems.

Purpose: CLT suggests that the brain has a limited capacity for processing information, and when learners face excessive mental effort, such as when navigating complex Learning Management System (LMS) interfaces, their ability to focus on primary learning tasks diminishes (Sweller, 1988). The purpose of this learning theory is not to simplify content for learners but to optimize their learning process.

Goal: By reducing cognitive demands, instructional design can help learners process information more effectively and, where possible, support the transfer of knowledge to long-term memory. This, in turn, can enhance their ability to acquire and apply knowledge. However, it is important to recognize that not all learners process or retain information in the same way. Factors such as disabilities, neurodiversity, and individual cognitive differences may impact memory function. Therefore, instructional strategies should be designed with flexibility and accessibility in mind, ensuring that students—regardless of cognitive differences—have equitable opportunities to engage with and apply course content.

Intrinsic Load: The effort required to understand the content itself, which depends on its complexity.

Extraneous Load: The unnecessary load caused by external factors such as the way information is presented or the tools used to interact with the content. LMS platform designs can increase this load unnecessarily. For example, digital text filled with multiple hyperlinks which can distract students and reduce their focus on the primary learning material. Hyperlinks can also be helpful for accessibility purposes and so hyperlinks should be used in moderation and in an organized way.

Germane Load: The mental effort dedicated to learning and integrating new knowledge. Effective LMS platforms should maximize germane load by facilitating cognitive processes that aid comprehension and retention. For example, in Brightspace, embedding an interactive diagram of a triangle that students can manipulate (e.g., adjusting angles and sides) is more effective and engaging than an extensive text-based explanation of geometric properties.

Cognitive Load Theory (CLT) can assist in optimizing student learning by managing cognitive demands. CLT can be applied to different areas of education, including: 

Course and Material Design: CLT can guide the reduction of extraneous load by simplifying  complex slides, readings, or instructions. Course materials such as syllabi, lecture slides, readings, handouts, instructional videos, discussion board prompts, and Learning Management Systems (e.g., Brightspace) should be designed to enhance comprehension and minimize unnecessary cognitive effort.

Assessments and Assignments: CLT supports clear and structured assessment criteria, helping students focus on essential content without cognitive overload. Effective applications include well-defined essay instructions, multiple-choice questions, problem-solving exercises, case studies, quizzes, exams, and rubrics with actionable feedback. Scaffolding assignments can also help students progress without excessive cognitive strain.

Teaching and Learning Strategies: Breaking down complex skills into manageable stages can prevent cognitive overload. CLT-informed strategies include clear group work instructions, flipped classroom materials, scaffolding techniques, and interactive tools that support step-by-step learning.

Learning Management Systems (LMS), Technology, and AI: CLT informs the design of user-friendly educational technologies and advocates for intuitive LMS navigation that can reduce distractions and prevent cognitive overload from excessive content. Furthermore, AI integration should be thoughtfully implemented to enhance, rather than overwhelm, the learning experience.

1. Difficult to Measure: No standardized way to assess cognitive load objectively.
2. Emphasis on Instruction: Prioritizes structured teaching over social, emotional, and motivational aspects of learning.
3. Learner Differences Matter: Cognitive capacity and optimal load levels vary.
4. Risk of Over-Structuring: Can limit student autonomy and discovery-based learning.
5. Relationship to Critical Thinking: Focus on content acquisition may not sufficiently develop reasoning and problem-solving skills.

CLT can be relevant for many reasons:

1. Optimize Learning: CLT can help reduce unnecessary mental effort, allowing students to focus on meaningful learning instead of struggling to process too much information at once.
2. Support Knowledge: CLT can provide the right information at the right time and allow students to develop effective knowledge structures in long-term memory.
3. Prevent Overload: It can avoid overwhelming students, reducing stress and fatigue that impede concentration and retention.
4. Effective Design: It can help create lessons and materials that are clear, well-structured, and aligned with students’ cognitive limits for better engagement.
5. Support Student Success: Create environments where students are more likely to remember, apply, and succeed with what they learn.