Cognitive Load Theory Implications

Principles of Cognitve Learning (Cipperfield, 2006)
According to Chipperfield (2006), CLT is based on the following principles of cognitive learning:

1. That short-term memory (working memory) is limited in capacity to about seven informational units.
2. Long-term memory is unlimited in capacity and is where all information and knowledge is stored.
3. Knowledge is stored in long-term memory as schemas or schemata.
4. Schemas, no matter how large or how complex, are treated as a single entity in working memory.
5. Schemas can become automated.

Principles of Cognitive Load Theory (Cooper, 1998)

1. Working memory is extremely limited.
2. Long term memory is essentially unlimited.
3. The process of learning requires working memory to be actively engaged in the comprehension (and processing) of instructional material to encode to-be-learned information into long term memory.
4. If the resources of working memory are exceeded then learning will be ineffective.

Principles of cognitive load theory application to instructional design (Copper, 1998)

1. Excessively high levels of cognitive load may result directly from the instructional materials presented to students.
2. Redesigning instructional materials to reduce the levels of extraneous cognitive load may enhance learning.
3. Content areas that are more likely to demonstrate beneficial results from improved instructional design are those that deal with “complex” information when the elements of to-be-learned information interact with one another (therefore imposing a high level of intrinsic cognitive load).

Practical Design Recommendations:

According to Kearsley (2006), cognitive load theory can be best applied in the areas of instructional design of cognitively difficult and technically challenging material. He states that to maintain effective learning environment we need to keep the cognitive load of the learners at a minimum during the learning process.

Miller (2006) discusses from Swellers paper written in 1999 about some instructional design recommendation provided by Sweller (1999). (Sweller, J. (1999). Instructional design in technology areas. Australian Educational Review No. 43, ACER Press, Camberwell, Australia.)

1. Change problem solving methods to avoid means-ends approaches that impose a heavy working memory load by using goal-free problems or worked examples.
2. Physically integrate multiple sources of information whenever possible to eliminate the need for learners to have to mentally integrate that information which increases the load on working memory.
3. Reduce redundancy and repetitive information whenever possible so that the load on working memory is lessened.
4. Use auditory and visual information under conditions where both sources of information are essential (i.e. non-redundant) to understanding. This helps increase the capacity of working memory.

Theory Implications

Cognitive load theory has many implications in the design of learning materials. It is our objective to minimize extraneous cognitive load thus maximizing the germane load. Extraneous cognitive load does not contribute to learning.
(Copper, 1998)

1. Remembering information (Cooper, 1998): Our brain has an ability to think and behave intellectually because of this, human are able to quickly identify meaning to a stimulated provided and are held relatively permanently in the long-term memory. It is similar to Gestalt’s Law of Closure: people tent to fill in missing pieces to form a complete picture (Ormrod, 1999).

Example: Look at the diagram even though some part of the text is not visible we can complete the information as “THE MAT ”, because our brain has the ability to fill in the gaps to help interpret the symbols.

2. Chunking information (Cooper, 1998): When presenting a “large” set of to-be-learned information it is better to “chunk” the information into smaller groups. This is possible based on Gestalt’s Law of Proximity (people tend to perceive as a unit those things that are close together in space [Ormrod, 1999]) and Similarity (people tent to perceive as a unit those things that are similar to one another [Ormrod, 1999]).

a. Example: Remembering a shopping list where items are chunked into like group, (Cooper, 1998)

b. Example: While trying to remember several digits of a phone number, we can chunk the numbers into groups 251-380-2642 rather than the sequence 2 5 1 3 8 0 2 6 4 2.

3. Use Goal free problems (Cooper, 1998): To understand this first we need to look at means-ends analysis.

Means-ends analysis: Means-ends analysis to solve problems (Cooper, 1998): this is a problem solving strategy, which is widely used to solve traditional problems by people who are not highly familiar with specific problem type. In this procedure, you solve problem backwards from goal to the given problem rather than working forward from the given theproblem to the goal.

a. Bad Example: if y = x +6, z = 6, find the value of y.
A novice problem solver (using means-ends analysis) would first focus on the goal state (find the value of y). After reading the question half way through, the problem the novice learner will forget certain elements of the equation, because their working memory has been heavily taxed attending to many elements of the problem. Nevertheless, eventually he will come to the answer of y=15, which is the goal state.

Goal free problem: Means-ends analysis operates on the principle of reducing differences between the goal state and problem givens. If problems are “goal free” ie “find what you can”, then a problem solver has little option but to focus on the information provided (the given data) and to use it where ever possible. This automatically induces a forwards working solution path similar to those generated byexpert problem solvers. Such forward working solutions impose very low levels of cognitive load and facilitate learning.

b. Good Example: if y = x +6, z = 6, find what you can
In this case the attention will be focused on “z=6” as this is the only variable with numeric value. Re-reading the question you can identify what value needs to be substituted in the equation. Thus, the learner comes to the answer y = 15 because nothing else needs to be found anymore. It is seen that this type of solution path is far simpler than that generated by means ends analysis.

4. Modality effect: People learn better when words are presented as speech rather than onscreen text.

5. Multimedia effect: People learn better when both words and graphics are included, as long as the graph is not self-explanatory.

6. Contiguity effect: People learn better when you place print words near corresponding graphics.

7. Redundancy effect: Simultaneous presentations of similar (redundant) content must be avoided. Avoid words as narrations and identical text with graphics.

8. Coherence effect: Peoples’ learning is hindered when extraneous sound, pictures, and words are used in teaching.

9. Split attention effect: Instructional materials, which require both textual and graphical sources of instruction, should integrate the text into the graphic in such a way that the relationships between textual components and graphical components are clearly indicated.

10. Worked example effect: Use worked examples to replace some practice. Students learn by studying worked examples. Make sure the examples and practice items are presented in an alternative sequence: example type 1, practice type 1, example type 2, practice type 2. This strategy is highly effective for teaching math based content (Cooper, 1998).

Example (Cooper, 1998): Compute for 'a'

Example	1	c(a+b)	=	d
		(a+b)	=	d/c
		a	=	d/c-b
Practice	1	g(a+m)	=	k
Example	2	ac + e	=	g
		ac	=	g-e
		a	=	(g-e)/c
Practice	2	ab + f	=	h