Cognitive load theory: definition and examples

Cognitive load theory explains why some instructional designs work while others leave students exhausted without having learned anything.

Capturing students’ attention is becoming increasingly difficult for teachers and training professionals, especially considering that attention is easily fragmented in a world saturated with notifications, social media, and constant digital stimuli.

In this context, understanding the definition of cognitive load theory and familiarizing yourself with some examples is a good starting point for achieving measurable results and fostering real, efficient learning in your educational strategy.

What is cognitive load theory? Understanding the architecture of learning

Cognitive load theory explains how a student’s brain can become overwhelmed and unable to retain information when the mental effort required exceeds available resources.

If we use a computer as an analogy for the brain, when you exceed its memory capacity, you can no longer save anything else. The same thing happens in your students’ minds.

This theory was developed by the Australian psychologist John Sweller, in the late 1980s, based on his research on problem-solving and discovery learning. In the 1990s, he continued his research in collaboration with another colleague, Paul Chandler, with whom he published his research Cognitive Load Theory and the Format of Instruction (1991).

Together, they addressed two effects closely related to teaching practice and how students learn:

1. Split-attention effect: describes how, when students have to mentally integrate separate sources of information (such as text from an image on one side and a graph on the other), their working memory becomes overloaded.
2. Redundancy effect: when the same information is presented in two different formats (for example, reading aloud what appears on a slide), learning efficiency is hindered because the brain expends energy processing duplicated information.

To understand why this happens, it is important to delve deeper into the relationship between working memory and long-term memory.

The architecture of human memory: short-term vs. long-term

Let’s look at the two types of memory discussed in cognitive load theory:

• Working memory: serves to process active information at a given moment. It is a very powerful but limited resource, as it can handle between 5 and 9 units of information simultaneously before becoming saturated.
• Long-term memory: its capacity is practically unlimited, and it stores information in structures called schemas, which are patterns that group related knowledge. This mechanism facilitates the easy retrieval of information.

The third variable that must be considered is cognitive load: the amount of mental effort required by our working memory at a specific moment. In this process, two scenarios can occur:

1. The load is manageable: the information received is processed, integrated into existing schemas, and transferred to long-term memory.
2. The load exceeds available capacity: cognitive overload occurs. The student stops processing information, no longer understands anything, and retention drops significantly.

Fun fact: Want to know what distinguishes an expert from a beginner when it comes to information processing? Expert can handle situations that paralyze a beginner, finding quick and effective solutions, because their mental schemas have reduced the processing cost, consolidating into a single unit of working memory. In contrast, complexity can paralyze a learner, as they lack solid structures.

This process is known in cognitive psychology as the automation of schemas, a fundamental pillar of expertise. While the student sees isolated elements where each occupies a space, treating each concept individually, the expert sees patterns and configurations that occupy a single space.

This reality is something to keep in mind for instructional design, since an excess of irrelevant information paralyzes the novice, and an excess of help or detailed instructions can be counterproductive for the expert.

Vygotsky & Sweller: the perfect duo for instructional design

Lev Vygotsky’s work focused on the zone of proximal development (ZPD). This refers to the gap between what a learner can do independently and what they can achieve when guided by someone more experienced.

This zone is essential to avoid overloading a student: if you ask them to operate too far beyond their current understanding, their working memory may collapse.

One way to avoid this is by implementing a cognitive load management strategy through adapted scaffolding, where the cognitive challenge is difficult enough to motivate but not so much as to cause overload.

In this way, Vygotsky describes the space where learning is possible, and Sweller explains the mechanism by which that space functions or becomes blocked. Both are essential for instructional design.

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The three types of cognitive load: intrinsic, extraneous, and germane load

Each type of cognitive load requires a different design response, so it is advisable to understand each one separately.

Intrinsic cognitive load

Intrinsic cognitive load reflects the difficulty of the material presented; that is, it focuses on how many elements the student must manage simultaneously to understand it. For example, if the level of interactivity is high (understanding one part requires understanding related parts), it will generate more cognitive load than an isolated concept.

Think about learning vocabulary in a language: the cognitive load is low because each word can be memorized independently. However, learning complex grammar is a task with a high intrinsic load because grammatical rules are interdependent.

In such cases, to avoid mental overload for students, information is broken down into simpler components before combining them into more complex tasks. Another solution is to prepare the student’s cognitive foundation before the main instruction. We can think of it as cultivating a garden before planting a tree to create the optimal conditions for it to thrive.

Extraneous cognitive load

Extraneous cognitive load focuses on how information is presented and highlights one of the enemies of effective learning: unnecessary information that does not build schemas but wastes resources.

This is the case with redundant data, text-heavy images, unclear instructions, complex navigation on e-learning platforms, or even the split-attention effect of a graph whose legend is not placed nearby.

Likewise, it can occur when a course overuses interactivity or adds game elements that distract rather than motivate, leading to gamification overload.

Germane cognitive load

Germane cognitive load is the type directly dedicated to building and automating schemas in long-term memory; therefore, it is important to increase it.

This occurs when a person actively works to connect new information with what they already know, generalize a learned pattern, or practice a skill to automate it.

The teacher must be able to free up space in the student’s mind by eliminating extrinsic load so that the germane cognitive load can operate effectively.

What are the signs of high cognitive load?

The signs that a student is experiencing cognitive overload are subtle and, in many cases, can be misinterpreted as a lack of motivation or ability.

Here are some of the most common examples in three specific contexts:

• In the classroom: atypical errors may occur in tasks the student should have mastered, a need to reread instructions multiple times without making progress, avoidance of exercises requiring deep thinking, or an inability to answer basic questions immediately after receiving instruction (cognitive silence).
• In e-learning and corporate training: thanks to metrics from the learning management systems (LMS) being used, you can detect symptoms such as chaotic navigation, insufficient time spent on pages with dense content, poor performance after training, and a high bounce rate.
• The illusion of learning: the student may have the subjective feeling of having learned something upon completing a module, when in fact they have forgotten much of the information. This may indicate a flaw in the design of the learning experience, which may have prioritized aesthetics over cognitive architecture, creating a false sense of fluency because it is easy to read.

Beyond these symptoms, other physiological ones may occur. They are less accessible to the instructor in their day-to-day work, but educational neuroscience has identified some, including increased heart rate, pupil dilation, or changes in brain activity.

Identifying these symptoms early allows you to adjust the scaffolding and reduce the extrinsic load before the student drops out.

This becomes even more relevant in remote assessment settings, where stress from using the platform can add unnecessary burden. Under such conditions, the results will not actually measure knowledge but rather students’ stress resilience.

Cognitive load examples: from the classroom to corporate onboarding

Learning about some examples in the most common contexts will help you solidify your understanding of cognitive load theory.

Cognitive overload in the classroom

Imagine a professor giving a lecture at a university on a topic while projecting text-heavy slides. As the professor paraphrases the information on the slides, students try to manage two tasks simultaneously: reading the slides and listening to the professor.

Both actions compete for the same cognitive channel, and as a result, students are unable to read or listen effectively.

Another example might be a high school teacher trying to introduce five new, interrelated concepts in a 50-minute class.

Novice learners with fewer prior schemas will overload their working memory with the first concepts and will be unable to continue processing information.

In contrast, students with more prior knowledge can absorb information with less effort, highlighting why the same materials may work for some students but not for others.

And on top of all that, you must factor in digital distractions (smartphone notifications, social media open in the background, etc.) that consume cognitive resources that will no longer be available for learning.

Cognitive overload in e-learning

In the digital environment, cognitive overload can be invisible to the instructor. Among the most critical mistakes when designing online educational experiences are the following:

• Complex interface. If navigation is confusing, it forces the student to expend energy figuring out how to use the platform, diverting resources away from information processing.
• Unstructured videos. Long audiovisual content without breaks can overload working memory because it demands sustained effort.
• Seductive details. Animations or graphics that serve only a decorative purpose capture the student’s attention, acting as visual noise and distracting them from the activity’s objective.
• Redundancy effect. If you display text on the screen while a voiceover reads it, it can cause cognitive interference. The brain processes the same information through two different channels, causing fatigue rather than reinforcement.

First impressions and mental load: the challenge of an onboarding process

In training new employees, properly managing mental load is also a critical issue. Keep in mind that a new team member will generally arrive with a high cognitive load; they must navigate a new environment and a new organizational culture.

If the onboarding process is dense and uses inconsistent formats, the employee’s cognitive system may become overwhelmed. So, when designing a good onboarding course, approach it with cognitive empathy to enhance your new employee’s experience.

How to reduce cognitive load: 6 instructional design strategies 

Cognitive load theory is a strategic toolkit for instructional designers seeking verifiable results.

If you want to create high-impact experiences and reduce digital distractions, you can apply the following strategies we’ve compiled from leading U.S. organizations and educational institutions.

Optimize information architecture: chunking and microlearning

Chunking is the fragmentation of information into manageable units and can be used as a direct response to the limits of working memory. In workplace training, this translates to microlearning.

The goal of this strategy is to design modules focused on a single learning objective that clearly respects the hierarchy of information.

By applying this method, you will reduce the intrinsic cognitive load before moving forward, allowing the learner to gradually integrate the knowledge into their mental models.

Leverage dual coding and multimedia learning

According to research conducted by Richard Mayer on multimedia learning, the auditory and visual channels are independent.

The key to using dual coding is to combine diagrams or infographics with brief narration that does not compete with each other, to avoid triggering the redundancy effect. So, do not read aloud exactly what appears on the screen.

Use worked examples for novice learners

A common mistake in curriculum design is throwing students into solving complex problems without first mastering the basic concepts.

One way to avoid this is to provide step-by-step solved examples that break down the logic of the task, preparing the student to practice independently.

By observing a reference model and having an effective guide, novice learners free up space in their working memory to understand the how and why.

Activate prior knowledge

Activating prior knowledge acts as an anchor in memory, serving as a cognitive shortcut that increases learning effectiveness.

You can incorporate pre-training sessions to clarify key terms or foundational concepts before delivering the main lesson. A brief review helps prepare the student’s mind for the task complexity of the new topic.

Design for low friction and visual consistency

In this scenario, the aesthetics must serve a pedagogical purpose rather than a decorative one. Keep in mind that every irrelevant element or confusing click on your platform is one less cognitive resource that can be dedicated to learning.

Therefore, strive to maintain consistent layouts and simple navigation across all your remote learning environments. A clean design reduces cognitive load and improves content comprehension.

As you can see, user experience (UX) is fundamental.

Drive long-term retention: spaced repetition and retrieval

Real learning occurs when the student attempts to retrieve information from their memory.

Therefore, it is beneficial to integrate dynamics such as spaced repetition or retrieval practice throughout the academic term, without having to wait for the final exam. You can configure them as part of your formative assessment strategies.

Integrity and cognitive focus in online evaluation

There is a relevant aspect of cognitive load theory, especially in online learning contexts: the role of assessment. The key question here is how does cognitive load affect exam performance?

Put yourself in the shoes of a student facing a high extrinsic load, such as an unexpected format or a difficult-to-use platform. Under such circumstances, they will be unable to demonstrate their true level of understanding due to the friction caused by the environment.

Therefore, the assessment environment must be clear, consistent, unobtrusive, and secure. An online assessment that generates anxiety in students through excessive control is counterproductive.

However, when the remote proctoring system is well-designed and monitors rather than controls, and is transparent and low-friction, it allows the student’s working memory to focus on answering the questions, rather than managing the uncertainty or pressure of the environment.

This is how we have designed the proctoring plans at Smowltech—as tools that record evidence (presence of people, objects, computer usage)—without interfering with the student’s concentration. Furthermore, although the innovative technology incorporated into our solutions monitors the process, it is always the teacher who makes the final decision.

If you’d like to learn more about how it works, feel free to request a free demo. We’d be happy to give you a guided tour of our solutions, which can be adapted to any assessment need.

Want to delve deeper into cognitive load theory?

With the resources presented below, you can transform your pedagogical approach and enhance the efficiency of your training programs, in both physical and digital environments:

• Quality Matters (QM). It is a non-profit organization dedicated to helping educational institutions review, improve, and certify the quality of their online courses.
• Evidence-Based Guidelines to Manage Cognitive Load (Ruth C. Clark, Frank Nguyen, John Sweller). The essential guide for instructional designers seeking evidence-based guidelines for real-world applications.
• The Cambridge Handbook of Multimedia Learning (Richard Mayer). A book that will help you understand how cognitive load affects visual and auditory processing.