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Category Archives: Instructional Design

Working with Cognitive Load

When I first started working as an e-Learning instructional designer I became interested in the learning process and how people learn. I figured that if I knew more about information processing and learning, I could hopefully design more effective courses. I came across a book called Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load by Ruth Colvin Clark, Frank Nguyen and John Sweller. In this book I discovered – among other things – Cognitive Load Theory (CLT) which is based on studies of human cognitive architecture – how we process and organise information.

In our brains, we have two types of memory. One is our working memory, which we use to process new information. The capacity of our working memory is quite limited so it can only handle so much before it becomes overloaded. The second is our long-term memory, which is where we store information from our working memory and where we retrieve that information from later. Within our long-term memory, information is organised into schemas, which are organisational frameworks of storage (like filing cabinets). Not exceeding working memory capacity will result in greater transfer of information into long-term memory.

CLT proposes that there are three types of cognitive load:

Intrinsic: this is the level of complexity inherent in the material being studied. There isn’t much that we can do about intrinsic cognitive load; some tasks are more complex than others so will have different levels of intrinsic cognitive load.

Extraneous: this is cognitive load imposed by non-relevant elements that require extra mental processing e.g. decorative pictures, animations etc. that add nothing to the learning experience.

Germane: these are elements that allow cognitive resources to be put towards learning i.e. assist with information processing.

The three types of cognitive load are additive so according to the theory, for instruction to be effective:

Intrinsic load + Extraneous load + Germane load < Working memory capacity

To assist learners in transferring information from their working memory to their long-term memory, we need to present the information in such a way that it reduces extraneous cognitive load (non-relevant items) and, if possible, increases germane cognitive load (items that assist with information processing). Note: I’ve found that much of the literature tends to focus on reducing extraneous cognitive load.

CLT

Mayer and Moreno (2003) conducted research into ways to reduce cognitive load in multimedia learning. Their research, built on CLT, was based on three assumptions:

  1. Humans possess separate information processing channels for verbal and visual material (Dual Channel).
  2. There is only a limited amount of processing capacity available via the visual (eyes) and verbal (ears) channels (Limited Capacity).
  3. Learning requires substantial cognitive processing via the visual and verbal channels (Active Processing).

They found that designers should do the following to assist learners in processing information:

  • Present some information via the visual channel and some via the verbal channel.
  • Break content into smaller segments and allow the learner to control the pace.
  • Remove non-essential content – this includes background music and decorative pictures that don’t add value.
  • Words should be placed close as possible to the corresponding graphics.
  • Don’t narrate on-screen text.
  • Synchronise visual and verbal content i.e. don’t place them on separate screens.

As instructional designers, we need to be aware of the cognitive requirements our designs impose and ensure that our learners can meet those requirements. We must also ensure that all aspects of our design focus on adding value to the learning experience.

References:

Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load (2006) by Ruth Colvin Clark, Frank Nguyen and John Sweller. Pfeiffer

Mayer, R. E. & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist. 38, (1), 43-52.

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Integrating Motivation with Instructional Design

As an Instructional Designer, motivating learners is an important consideration because in reality learners are not always motivated to learn. They are busy, have other things to do, don’t see the course/session as being important or have had a bad learning experience in the past. I’ve written a couple of posts about motivation – self-determination theory and the motivational pull of video games – which are about satisfying autonomy, competence and relatedness needs of learners. I’ve come across Dr John Keller’s motivational design model known as ARCS and thought it was worth sharing.

The ARCS model comprises four major factors that influence the motivation to learn – Attention, Relevance, Confidence and Satisfaction. It’s described as a problem-solving model and helps designers identify and solve specific motivational problems related to the appeal of instruction. The model was developed after a comprehensive review and synthesis of motivation concepts and research studies. Its also been validated in studies across different education levels.

John KellerDr John Keller

The four categories of motivation variables consist of sub-categories along with process questions to consider when designing:

Attention = Capturing the interest of learners, stimulating their curiosity to learn.

  • Perceptual Arousal: What can I do to capture their interest?
  • Inquiry Arousal: How can I stimulate an attitude of inquiry?
  • Variability: How can I maintain their attention?

Relevance = Meeting the personal needs/goals of the learner to affect a positive attitude.

  • Goal Orientation: How can I best meet my learner’s needs? (Do I know their needs?)
  • Motive Matching: How and when can I provide my learners with appropriate choices, responsibilities and influences?
  • Familiarity: How can I tie the instruction to the learners’ experience?

Confidence = Helping the learners believe/feel that they will succeed and control their success.

  • Learning Requirements: How can I assist in building a positive expectation for success?
  • Success Opportunities: How will the learning experience support or enhance the learners’ beliefs in their competence?
  • Personal Control: How will learners clearly know their success is based upon their efforts and abilities?

Satisfaction = Reinforcing accomplishment with rewards (internal and external).

  • Natural Consequences: How can I provide meaningful opportunities for learners to use their newly acquired knowledge/skill?
  • Positive Consequences: What will provide reinforcement to the learners’ successes?
  • Equity: How can I assist the learners in anchoring a positive feeling about their accomplishments?

The following link is to a YouTube video where Dr Keller discusses the ARCS Model, some background in its development and the addition of volition to the model.

ARCS: A Conversation with John Keller

Apart from the motivational aspects of the model, what I really like about ARCS is that it puts the learner at the centre of the design process.

After all, that’s how it should be.

References:

arcsmodel.com

Keller, J. M. (1987) Strategies for stimulating the motivation to learn. Performance and Instruction. 26 (8), 1-7.

 
 

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From Learning Outcomes to Performance Outcomes

In most, if not all e-Learning and classroom courses, one of the first things mentioned are the Learning Outcomes. After all, they’re the purpose of the course. Unfortunately, in many cases, they appear to be slapped on with very little thought put into them. Here are three that I dislike seeing:

By the end of this course, you will:

  • Understand something, or
  • Be aware of something, or
  • Know something.

The problem with these outcomes is that they are too vague. Yet they are used all too often to set the scene for an online or face-to-face learning experience. Sure, understanding, awareness and knowledge are part of the learning process. You could even argue they are learning outcomes because hopefully by the end of a course, learners will understand, be aware and know something that they didn’t know before. The problem is these outcomes don’t go far enough. How can you tell if a learner understands, is aware or knows something?

They’ll be able to DO something.

As someone who works in “Learning and Development” my goal is to change behaviour and ultimately improve the performance of the employees in my organisation. There are many ways to do this both formally and informally but focusing on what will be learned i.e. the content, its stopping short of the ultimate goal of behaviour change and performance improvement.

For example, if I’m designing a course about our organisations Code of Conduct, a learner is aware of, and knows that, the code exists – just by participating.

So, an outcome of the course isn’t really:

You’ll be able to understand the requirements of the Code of Conduct.

It’s only part of what learners are able to do. A real outcome is:

You’ll be able to make ethical decisions while working at our organisation.

See the difference? The first one is content focused – what the code says to do, where the second is performance focused – making decisions based on what the code says to do. So why don’t we call them Performance Outcomes? Surely, by moving away from the term Learning Outcomes and calling them Performance Outcomes, we can focus on the desired performance required from learners and not what content is to be covered during the course?

A performance focus should also guide us through the analysis and design of the course resulting in an improved outcome for learners who are participating and the organisation as a whole.

What’s your view?

10982789-performance-word-in-white-chalk-handwriting-on-blackboard

 
5 Comments

Posted by on September 22, 2013 in Instructional Design

 

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A Blueprint for Design (part 2)

In my last post, I looked at the fundamentals of cognitive load theory. So, to assist learners in transferring information from their working memory to their long-term memory, we need to present the information in such a way that it reduces extraneous cognitive load (non-relevant items) and increases germane cognitive load (items that assist with information processing).

Several techniques can help to achieve this purpose. While many of them are relevant to technology-based instruction, but I believe they could also be adapted for classroom learning depending on the content to be learned. These effects have been studied over the years so are supported by research. Some effects apply to novice learners while others are relevant for more experienced learners. Also keep in mind that depending on the material/task to be learned, not all of the effects will apply.

Worked Example Effect: Novice learners should study worked solutions of unfamiliar problems to reduce the amount of cognitive processing. This will provide a foundation upon which they can build their expertise. So throwing learners in at the deep end isn’t a good idea.

Split-Attention Effect: This occurs when multiple sources of information must be integrated before they can be understood. For example, a diagram along with text to explain different parts of the diagram is being used; the text should be integrated or placed near to the relevant part of the diagram rather than having the learner try to move back and forth from one source of information to another.

Modality Effect: Working memory has both a visual processor and an auditory processor. As a result, using both processors can effectively expand the size of working memory if the cognitive load is distributed across both processors. This can be achieved when some information is presented visually (e.g. words and images) and other information by using sound (e.g. narration).

Redundancy Effect: Redundant information is any information not relevant to the learning experience. This effect occurs when the same information is presented in different forms e.g. narrating on-screen text or using text that repeats information contained in a diagram. It also includes using decorative pictures, background music or cartoon images that don’t add value.

Expertise Reversal Effect: As expertise increases, previously essential information becomes redundant. Including information that is needed for novice learners in courses for learners with more expertise would place higher levels of extraneous cognitive load on the experienced learners.

Guidance Fading Effect: The level of assistance provided to learners should be reduced as expertise increases. For example, instead of complete worked examples learners would be presented with partially complete problems that need to be solved.

Imagination Effect: Asking learners to imagine procedures or concepts assists with the transfer into long-term-memory. This technique should be used with learners who have sufficient experience in the area being studied (not really suitable for novice learners).

Element Interactivity Effect: Element interactivity is determined by the number of interacting elements that must be considered simultaneously in order to understand the material. More complex material is likely to have higher levels of element interactivity.

Isolated Interacting Elements effect: Where element interactivity is very high it may be too difficult for learners to understand the material because of the large amount of interacting elements i.e. working memory capacity would be exceeded. It may then be necessary to present the information as individual elements and ignore their interaction. As the individual elements have been learned, their interactions can then be emphasised.

So what do these effects mean for instructional designers and trainers?

Firstly, we need to be mindful of the processing capacity our learners and apply a learner-centred approach in the design of training materials and courses. Secondly, we should also take into account the experience level of learners and design courses accordingly. Finally, we need to strip away information that does not add value to the learning experience (this can sometimes be easier said than done!)

References:

Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load (2006) by Ruth Colvin Clark, Frank Nguyen and John Sweller. Pfeiffer (publisher).

Handbook of Research on Educational Communications and Technology, (2008) 3rd ed. Chapter 31. Spector, Merrill, van Merrienboer and Driscoll (editors). Taylor and Francis Group (publisher).

 
 

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A Blueprint for Design (part 1)

A little over a year ago while reading Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load by Ruth Colvin Clark, Frank Nguyen and John Sweller, I came across an interesting instructional design theory called Cognitive Load Theory (CLT). It’s based on knowledge of human cognitive architecture – which is how we process and organise information.

If we can better understand the human cognitive process, we can apply principles of CLT to design better learning instruction resulting in improved outcomes. Plus there is research behind these claims too!

In our brains, we have two types of memory. One is our working memory, which we use to process new information. The capacity of our working memory is quite limited so it can only handle so much before it becomes overloaded. The second is our long-term memory, which is where we store information from our working memory and where we retrieve that information from later. Within long-term memory, information is organised into schemas, which are organisational frameworks (like filing cabinets).

Not exceeding working memory capacity will result in greater transfer of information into long-term memory. CLT proposes that there are three types of cognitive load:

Intrinsic: this is the level of complexity inherent in the material being studied. There isn’t much that we can do about intrinsic cognitive load; some tasks are more complex than others so will have different levels of intrinsic cognitive load.

Extraneous: this is cognitive load imposed by non-relevant elements that require extra mental processing e.g. decorative pictures, animations etc that add nothing to the learning experience.

Germane: these are elements that allow cognitive resources to be put towards learning i.e. assist with information processing.

The three types of cognitive load are additive so according to the theory, for instruction to be effective:

Intrinsic load + Extraneous load + Germane load < Working memory capacity

Where possible, we need to increase germane cognitive load and reduce extraneous cognitive load when we design and deliver training/education/learning. Everything we include in a course needs to have a purpose – it needs to add to the learning experience in some way.

Some questions that I have that I haven’t been able to find answers for yet:

Is each person’s working memory capacity the same?

Does intelligence play a part?

If working memory capacity is not exceeded, how long can someone keep processing information?

Next time I’ll look at some of the CLT effects and how learning can be improved.

 
 

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