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THIS DIGITAL ARTIFACT IS A STUDENT ASSIGNMENT FOR ETEC 512 IN THE UBC MET PROGRAM AND WILL BE MONITORED FOR COMMENTS BETWEEN MARCH 21 AND APRIL 1, 2022

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Cognitive Load Theory + Natural Information Systems

Updated: Mar 20, 2022

3 min. video, 4 min. read.

Historically, cognitive load theory (CLT) has been concerned with individual learning. Paas et al. (2004) contend that the capacity to learn diminishes with either too low or too high of a cognitive load. The authors argue that instructional designers manage the working memory load of learning complex tasks by considering cognitive architecture. Kirschner et al. (2017) suggest little attention has been paid to the use of CLT when designing for online collaborative learning.

Paas et al. (2004) propose that each person has a working memory limited in dealing with new information. The researchers offer that learners have existing cognitive schemas (categories of data) to sort new data into and create new schemas when needed.

The authors submit that working memory is not taxed when retrieving information from schemas that are already in long-term memory. Automation happens, and skills increase when more complex schemas are created. Practice moves data into long-term memory, freeing up working memory capacity (Paas et al., 2004).

Categories of Cognitive Load

Paas et al. (2004) tender that CLT distinguishes between three categories of cognitive load: "intrinsic, extraneous, and germane." The authors suggest that the load is intrinsic when data is interconnected and intrinsic to the goal, extraneous when data has nothing to do with the actual learning goal, and germane when directly linked to the learning outcome. The researchers pose that irrelevant information may be needed to illustrate examples in some cases. Too low of a cognitive load leads to boredom, and too high of a load leads to working memory overload, with both scenarios resulting in lower learning success. The goal is to increase the germane and intrinsic categories of cognitive load without causing overload. Teachers and instructional designers may consider the categories, appropriate instructional design methods, and CLT instructional design principles (Paas et al., 2004).

Biologically Primary and Biologically Secondary Intelligence

Biological primary and secondary knowledge contain distinct differences (Sweller & Sweller, 2006). The authors discusses that primary knowledge is assigned to categories we have evolved as humans to amass. Primary knowledge examples include:

  • Acquiring a first language through learning listening and speaking skills,

  • Learning general skills in problem-solving, and

  • Developing social interaction.

Sweller & Sweller (2006) put forward that most people quickly acquire primary knowledge. The researcher illustrates that secondary knowledge includes information that we have not evolved to achieve, like reading, and encompasses virtually all the understanding taught in schools.

Natural Information Systems

Sweller & Sweller (2006) argue that natural information processing systems are found all around us in the natural world. Their purpose is to catalogue information that regulates the movement of natural bodies such as living entities. The authors propose five principles of information processing that "apply to both human cognition and evolution." The researchers suggest the following (simplified, and as it relates to cognitive, not evolutionary, activity):

1. The information store principle

Sweller & Sweller (2006) submits that long-term memory has a critical role in cognition, is the largest store of information, and most cognitive activity is directly linked to it. Biologically primary information (seeing, hearing, thinking) is governed by previously-stored knowledge in long-term memory.


2. The borrowing and reorganizing principle

The researchers contend that almost all linguistic knowledge held in human long-term memory is borrowed from another human's long-term memory. Individuals hear others speak and read what others have written. Schema theory explains how previous knowledge is reorganized and constructed with the addition of new information.

3. The randomness as genesis principle

Sweller & Sweller (2006) argue that although most information in long-term memory is borrowed and reorganized, there is also a creative potential to generate new knowledge through problem-solving. This new knowledge can be stored in long-term memory for later use. The authors pose, "The randomness as genesis principle is the ultimate source of all novel information and is dependent on random generation followed by tests of effectiveness."

4. The narrow limits of change principle

Sweller & Sweller (2006) illustrate that "in problem-solving...the contents of working memory determine which problems will be attempted. Consequently, some long-term memory areas are more likely to be altered by problem-solving than other areas."

5. The environmental organizing and linking principle

The researchers suggest that environmental stimuli activate particular schemas while the other schemas lay dormant. Working memory activation links long-term memory and the environment (Sweller & Sweller, 2006). Thus, this principle permits the use of stored knowledge to direct the activities of humans in their environment.


Conclusion


Cognitive load theory reminds us that working memory space is limited and intentional actions in teaching and learning design can be taken to minimize overload of this cognitive resource. There are three types of cognitive load: intrinsic, extraneous, and germane and too low and too high loads can decrease the ability to learn. Most primary information is learned naturally from others' long-term memory and most secondary information has to be taught. Natural information systems have five principles that explain how working memory, long-term memory, schema activation, and environmental triggers direct learners' activities.


References


Paas, F., Renkl, A., & Sweller, J. (2004). Cognitive load theory: Instructional implications of the interaction between information structures and cognitive architecture. Instructional Science, 32(1), 1-8. https://doi.org/10.1023/B:TRUC.0000021806.17516.d0


Sweller, J., & Sweller, S. (2006). Natural information processing systems. Evolutionary Psychology, 4, 434–458. https://doi.org/10.1177/147470490600400135







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