Editing Cognitive Load Theory

= Overview =
Cognitive Load Theory was developed in the late 80s by John Sweller. The theory emphasizes [[Working Memory | working memory]] constraints as determinants of instructional design effectiveness and reduces the unnecessary cognitive load (Sweller, 1998). Three types of cognitive load: Intrinsic, Extraneous, and Germane. Intrinsic load is the difficulty associated with the topics, which is not a controllable factor by the designer. Extraneous load is the load by the manner in presentation of the information, which is a controllable factor for the instructional designer. Germane load is the processing of working memory that creates knowledge and contributes to the construction of schemas (Sweller, 1994).

= Evidence =
The foundation of cognitive load theory is based on the cognitive science perspective that designs cognitively friendly instruction. The human brain can only process a small amount of information but can store a large amount of information. The human brain's capacity is limited, which reduces the learner's extraneous load, which can avoid memory overload, ultimately making learning effective. The influence of cognitive load theory has been proven effective by research and experiments, and the idea of cognitive load theory is being applied to different fields. In the medical field, the use of cognitive load theory can benefit learners in simulation laboratory performance (Fraser, 2015), and in the education field, redesigning the activity by using cognitive load theory can reduce the anxiety of learners and free the capacity for cognitive activities, and increase learning effectiveness (Chen, 2009).

= Design Implications =
Cognitive Load Theory helps learning designers understand the cognitive load and reduce the unnecessary load that limits learning efficacy. One such strategy to achieve this includes [[chunking]] information. 

Secondly, promoting germane cognitive load, can facilitate schema construction, which can be considered a major focus in learning design (Sweller, 1998). In K 12 classrooms, the instructor should create an inclusive and supportive learning environment to reduce extraneous load and promote germane load through teaching strategies, [[metacognition | metacognitive strategies ]] scaffolding, and visual aids.

= Challenges =
The challenge for Cognitive Load Theory in 21st century is to adapt to the rapid change of new technology and how that technology affects cognitive load. In the modern classroom, the medium of learning resources, and way of learning, is changed over time. Technology development brings a new cognitive load, making it harder to predict the load in learning. From a social perspective, the learning resources, such as free internet access and public library, made the resources gap smaller between people who experience poverty and between the middle class (Hackman, 2009). Technology closes the gap in individual differences in processing capacity, which means fewer people need pre-training in learning. Technology development brings new learning solutions, and the learning environment and resources are changed compared to the last century.

= Also see= 
[[Chunking]]

[[Dual coding theory]]

[[Cognitive Theory of Multimedia Learning]] 

[[Mayer's Principles in Multimedia Learning]]

= References =
Swller, J., (1994). Cognitive load theory, learning difficulty, and instructional design. ''Learning and Instrction.'' 4: 295-312

Sweller, van Merrienboer, Pass (1998). Cognitive Architecture and Instructional Design, ''Educational Psychology Review'', Vol. 10, No.3, p.251-291

Hackman, Daniel A.; Farah, Martha J. (2009). Socioeconomic status and the developing brain. ''Trends in Cognitive Sciences''. '''13''' (2): 65–73.

I-June Chen, Chi-Cheng Chang, (2009). Cognitive Load Theory: An Empirical Study of Anxiety and Task Performance in Language Learning, ''Electronic Journal of Research in Educational Psychology'', 7(2), 729-746

Fraser.K. L., Ayres P., Sweller J., (2015). Cognitive Load Theory for the Design of Medical Simulations, ''Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare'', Volume 10-Issue 5, p 295-307