Tag Archives: instruction

Getting Into the Swing of Using Video

I’ve recently started to play golf. It’s not the first time I’ve ever tried it, I’ve played the game a few times in the past but found it to be a frustrating experience. This time though I plan to stick with it, practice and not just rely on visualisation and thinking positive thoughts!

When I went for my first coaching session (I received four sessions as a gift), the golf pro Andrew, brought out his iPad and asked me to hit a ball while he filmed me. Then we sat down and had a look at the footage. He drew a green line on the screen to see how I was positioned as I hit the ball (which turned out to be not too bad). He was also able to break down my swing into chunks – setting up, backswing, coming down and follow through and give me pointers about each. After some time spent practicing, he filmed me again. Then we talked about how it was different.

After the session Andrew e-mailed me the following summary of what we discussed:

Set up:

1) Left thumb down the centre of the grip.

2) Right thumb relaxes over.

3) Feet together.

4) Little step with your left then big step with your right.


1) Position 1 (as shown below).

2) Position 2 (as shown below) thumbs to the sky.

3) Position 3 (as shown below) right heel up.

Keep running through your routine over and over to achieve consistent results.


Even though you can see the direction of the ball, I can tell you that it went perfectly straight! What I find particularly useful about this approach was that it didn’t just rely on someone telling me what needed correcting but that he could show me where I needed to improve and I could also see it for myself.

Afterwards, I was thinking how this approach might be useful for workplace learning and an obvious application would be a task that requires a particular technique, for example lifting something safely. You could do something similar to what Andrew did, film a before and after with some instruction in-between along with some follow-up afterwards.

A few years ago I went to a training session on facilitation skills and one of the remarks that has stayed with me is ‘people can’t argue with their own data’. In that context it was referring to having the people in the room generate content/ideas during the session that you can come back and refer to later. Filming me as I hit a ball also provides content that I can’t argue with. I can see what I’m doing correctly and incorrectly and make the necessary adjustments with some coaching.

While you can watch others using video, say in a scenario or for compliance, it’s also possible for you to be the star of the show.

How do you use/have you used video in your workplace?


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Letting go of Learning Styles

I’ve been thinking about writing this post about learning styles for a while now. It’s an area that I’m sure everyone in the training and learning industry has had contact with at some point. The idea of learning styles has been around for 40 years and I first came across them when completing my training and assessment qualification back in 2007. We discussed visual, auditory and kinaesthetic (VAK) learners and also Kolb’s learning styles. To me it made sense that people would have a preference for the way in which they like receive information. It’s equally logical that if we matched instruction to learning styles, it would result in better learning.

This all changed when I came across a journal article that said this:

LS Quote

How could this be after all this time? I was surprised, so I investigated further. I found that at last count there were over 70, yes 70, different learning styles models. These have been used in schools, higher education, vocational education and the workplace to categorise people as a particular type of learner. The popularity of learning styles shows no signs of slowing down. It seemed that the more I looked for evidence that supports learning styles, the more I found that the research just doesn’t support the theory. On reflection, there was a definite lack of critical thinking on my part.

I can see the appeal of learning the style movement:

  • It sounds logical so it’s easy to understand
  • It’s easy to teach
  • It’s been marketed and sold very well

I like Steve Wheeler’s description of the learning styles myth as a convenient untruth.

What I also find troubling is that in Australia, the minimum qualification for trainers and assessors and many learning and development professionals is the Certificate IV in Training and Assessment. Units within this qualification still refer to having knowledge of learning styles. This means that subsequent generations of learning practitioners are learning about something that has no evidence to back it up.

Yoda quote

Given that learning styles isn’t helpful, we should as Jane Bozarth wrote, unlearn it. While it may be harder than learning, learning styles is something we need to unlearn. Yes, learners have different characteristics but we need to focus on evidence-based methods of instruction. Take Will Thalheimer’s Decisive Dozen as an example. These 12 factors are based a synthesis of years of research undertaken in learning and instruction.

We shouldn’t focus on things that sound logical or are popular or are just accepted. If we want to be taken seriously as learning professionals we need to use theories, methods and techniques that are grounded in research and actually get results.


Riener, C & Daniel Willingham, D. (2010): The myth of learning styles. Change: The Magazine of Higher Learning, 42 (5), 32-35.

Rohrer, D, and Pashler H. (2012) Learning styles: where’s the evidence. Medical Education, 46. 630-635.

Scott, C. (2010) The enduring appeal of ‘learning styles’ Australian Journal of Education, 54 (1), 5-17.

Vorhaus, J. (2010) Learning styles in vocational education and training. Vocational Education and Training – Teaching and Learning, 376-382.


Posted by on November 10, 2013 in Theories


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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.


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


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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!)


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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