When Less is More: Design Decisions to Minimize Cognitive Load And Maximize Student Learning

• Cognitive Load Theory Components: Cognitive Load Theory distinguishes between intrinsic, extraneous, and germane cognitive loads, emphasizing the importance of managing these to enhance learning efficiency. Intrinsic load relates to the complexity of the material, extraneous load to the presentation of information, and germane load to the assimilation of information.
• Design Strategies for Managing Cognitive Load: Effective instructional design aims to reduce extraneous and intrinsic cognitive loads while increasing germane load. Strategies include simplifying material presentation, focusing on essential content, and enhancing students' prior knowledge.
• Impact of Design on Learning Outcomes: Research demonstrates that minimizing extraneous cognitive load through thoughtful design choices improves learning outcomes. This involves using visual aids, organizing information logically, and removing nonessential content to facilitate better comprehension and performance in tasks like mathematics.

Incredible and Limited

The human brain is amazingly complex, processing at rates estimated to be in the order of 1,000,000,000,000,000 FLOPS (Floating Point Operations Per Second)! Whatever that actually means, it is 10,000 times the processing power of a typical laptop! Yet, despite its incredible capabilities, the brain has its limits.

• The Stroop Effect: In this study, participants were asked to name the color of words that could either match or conflict with the word's meaning (e.g., the word "red" printed in blue ink). The results showed that people took longer to name the color of the ink for conflicting words.
• The Simon Effect: This experiment demonstrated that reaction times are faster and more accurate when the location of a stimulus matches the location of the response, even if the location is irrelevant to the task. For example, participants responded more quickly to a red light by pressing a button on the same side as the light.
• Change Blindness: In this study, participants engaged in a conversation with a person who was then swapped with another person after a brief interruption by a door being carried between them. Many participants failed to notice the switch.
• Selective Attention Test: Participants watching a video were asked to count the passes of a basketball between players, leading many to overlook a person in a gorilla suit walking through the scene.

While these studies each explore different facets of human cognition, together, they show that the human brain operates within constraints that impact what we notice and learn. Understanding these limitations can help us design learning environments that better support student success.

Cognitive Load Theory

Cognitive Load Theory offers insights into how our minds handle and integrate new information, stressing the importance of balancing different types of mental demands to boost learning efficiency and success [1].

Intrinsic Cognitive Load

Intrinsic cognitive load is determined by the complexity of the learning material and how the elements within it interact. It is fixed for a given task and a student’s knowledge level. The goal in instructional design is to reduce intrinsic cognitive load by changing either the nature of the material or the student’s current state of knowledge.

Extraneous Cognitive Load

Extraneous cognitive load is imposed by the way information is presented to students. Suboptimal instructional design can increase the cognitive load unnecessarily. Ideally, instructional design seeks to eliminate extraneous load, primarily by simplifying the presentation of material to make learning more efficient.

Germane Cognitive Load

German cognitive load refers to the cognitive resources devoted to processing and understanding the material, aiding the construction of schemas. Unlike intrinsic and extraneous cognitive load, germane cognitive load is focused on the student’s efforts to assimilate new information into their knowledge base. In this sense, the goal of instructional design is to actually increase germane load.

Addressing Cognitive Load Through Design

Overall, Cognitive Load Theory tells us to optimize instructional designs by reducing unnecessary extraneous load, managing the inherent intrinsic load of the material, and enhancing germane load to improve learning outcomes [2].

The primary concern when designing learning experiences in digital environments is reducing extraneous cognitive load [3].Particularly in the context of mathematical learning, high cognitive load conditions challenge students by splitting their attention between the distractions of nonessential or unclear design elements and the primary cognitive task at which they are working. When extraneous load is high, students take longer to reach solutions and are less accurate in their performance [4].

Design factors such as interactivity, immersion, and realism in digital learning environments can increase extraneous cognitive load [3].On the other hand, integrating visual aids with equations [5], adding relevant pictures to text-based information [6], and using design cues to focus students’ attention on the important aspects [7] reduce extraneous cognitive load and facilitate improved learning outcomes. Additionally, simplifying task complexity and enhancing learners' prior knowledge reduces intrinsic cognitive load [6].

A double-blind randomized and controlled experiment of 138 high school students found that changes in cognitive load perceptions fully account for the effect on transfer performance [8].

Similarly, a study of 222 8th grade math students showed that design improvements including signaling important information, organizing items logically and aesthetically, and removing nonessential content all reduced extraneous cognitive load and resulted in improved performance in math assessments [9].

Overall, these findings underscore the importance of minimizing extraneous cognitive load to free up cognitive capacity for generative processing, enhancing learning and comprehension.

How We Manage Cognitive Load

We have written about how our well-CRAFTED questions and intentional curriculum progressions are designed to build confidence. In the context of cognitive load, these designs choices help to manage intrinsic load, ensuring that students’ level of knowledge is sufficient for each new task.

Our articles about utilizing multiple representations and our overall visual emphasis highlight how we use representations and visuals to help students process and remember information. These design choices also serve to both reduce extraneous cognitive load and support germane load by focusing students’ attention directly on the learning task.

Design decisions around font selection and color and our focus on hand-drawn elements were made to help students connect with and make sense of the learning materials with which they are engaging. These ideas further come together to reduce extraneous cognitive load by making the materials familiar and accessible.

We Hope You Don’t Notice

We are proud to have poured our passions and resources into crafting learning experiences that support student success in every possible way we could imagine. While these design choices were extraordinarily intentional and exceptionally well-implemented, we expect you wouldn’t have noticed any of them. We certainly hope our students don’t. And that is the point.

Properly designed, the digital interface should essentially be invisible to the users. We want our students to be calm, focused, and engaged. In other words, we want to minimize extraneous cognitive load, effectively manage intrinsic load, and maximize germane load. We have accomplished this by designing an environment that eliminates all potential for distraction and confusion, creating learning pathways to progressively develop students’ confidence and competence, and empowering students to use their mental energies to learn, grow, and succeed.

References

[1] Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22, 123-138. https://doi.org/10.1007/s10648-010-9128-5

[2] Deck, C., Jahedi, S., & Sheremeta, R. (2021). On the consistency of cognitive load. European Economic Review, 134, 103695. https://doi.org/10.1016/j.euroecorev.2021.103695

[3] Skulmowski, A., & Xu, K. M. (2022). Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load. Educational Psychology Review, 34(1), 171-196. https://doi.org/10.1007/s10648-021-09624-7

[4] Avgerinou, V. A., & Tolmie, A. (2020). Inhibition and cognitive load in fractions and decimals. British Journal of Educational Psychology, 90, 240-256. https://doi.org/10.1111/bjep.12321

[5] Van de Weijer-Bergsma, E., & Van der Ven, S. H. (2021). Why and for whom does personalizing math problems enhance performance? Testing the mediation of enjoyment and cognitive load at different ability levels. Learning and Individual Differences, 87, 101982. https://doi.org/10.1016/j.lindif.2021.101982

[6] Klepsch, M., & Seufert, T. (2020). Understanding instructional design effects by differentiated measurement of intrinsic, extraneous, and germane cognitive load. Instructional Science, 48(1), 45-77. https://doi.org/10.1007/s11251-020-09502-9

[7] Xie, H., Wang, F., Hao, Y., Chen, J., An, J., Wang, Y., & Liu, H. (2017). The more total cognitive load is reduced by cues, the better retention and transfer of multimedia learning: A meta-analysis and two meta-regression analyses. PloS one, 12(8). https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183884

[8] Xu, K. M., Koorn, P., De Koning, B., Skuballa, I. T., Lin, L., Henderikx, M., ... & Paas, F. (2021). A growth mindset lowers perceived cognitive load and improves learning: Integrating motivation to cognitive load. Journal of Educational Psychology, 113(6), 1177. https://psycnet.apa.org/doi/10.1037/edu0000631

[9] Gillmor, S. C., Poggio, J., & Embretson, S. (2015). Effects of reducing the cognitive load of mathematics test items on student performance. Numeracy, 8(1), 4. https://digitalcommons.usf.edu/numeracy/vol8/iss1/art4/