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Becoming an eLearning Professional

A few weeks ago I was contacted by Christopher Pappas via e-mail and he asked if I would be interested in contributing to an eBook he was putting together. I’ve been fortunate that Christopher’s eLearning Industry website has published a few articles that I’ve submitted over the past few months. At the time I didn’t know who else had been asked to contribute but I was thrilled that he asked me and of course I said yes!

I’d never been asked to do anything like this before so I put some time into thinking about the question, which was:

What are the most effective uses/tips to become an eLearning pro?

My tips are based on my experiences and what I’ve learned during my career to date as well as advice that I’ve been given that has served me well. I was also really pleased to see another Aussie, Ryan Tracey the E-Learning Provocateur giving some great advice in his post. In fact, all of the contributors have something to offer and while I didn’t know all of them there were several who I look up too and it was an honour to be included in this eBook with them.

I realise I still have lots more to learn but it’s good to know that through my blog and being involved with projects like this, I can share what I know, learn from others in the field and also assist new designers who are keen to improve their own practice.

To find out what I submitted, click on the image below which will take you to the eBook:

How to Become an eLearning Pro

Here are the links to my other posts published on the eLearning Industry website:

5 Ways to Reduce Cognitive Load in eLearning

15 Tips to Improve Learners’ Motivation for eLearning Courses

20 Resources for New eLearning Professionals

25 Tips for Successful Online Course Facilitation

I hope you find some value in the eBook and the posts.

I’d also like to thank Christopher for asking me to participate in the eBook and for publishing my posts, I appreciate the support.

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Posted by on November 17, 2013 in Resources

 

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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.

References

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.

 
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Posted by on November 10, 2013 in Theories

 

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Improving Learner Motivation for eLearning

Motivation has been and continues to be a widely studied area across many of life’s domains. Motivation is the energising force that initiates and sustains behaviour and ultimately produces results. Many motivation theories focus on the amount of motivation, with a larger quantity said to result in improved outcomes. However, as educators we shouldn’t focus on generating more motivation from our learners but instead focus on creating conditions that facilitate the internalisation of motivation from within our learners.

Self-determination theory (SDT), an empirical theory of motivation by Edward Deci and Richard Ryan, focuses on the degree in which behaviour is self-motivated and self-determined. According to SDT, engaging in activities for their inherent satisfaction and that are enjoyable are said to be intrinsically motivating. Unlike other motivation theories, extrinsic motivation (engaging in activities that lead to a separable outcome) is not a single construct; it exists in four distinct forms according to the extent to which the motivation for the behaviour emanates from one’s self. In other words, extrinsic motivation can be viewed by the degree to which it is controlling of one’s behaviour (external) or allows one’s behaviour to be more autonomous (internal).

SDT Continuum

Ryan and Deci, 2000.

Organismic Integration Theory (OIT), a sub-theory of SDT, places the four types of extrinsic motivation along a continuum of relative autonomy, depending on the level of control or autonomy. As can be seen in the diagram (from left to right), the types of motivation along the continuum relate to increasing levels of internalisation and autonomy and lower levels of control. Progression along the continuum is not necessarily linear and is subject to contextual factors. However, greater internalisation “is critical for effective psychological and academic functioning at all education levels” (Niemiec and Ryan, 2000, p.138).

How can we help learners to internalise their motivation?

SDT proposes that all humans require the satisfaction of three basic psychological needs, namely:

  • Autonomy (a sense of being in control and having freedom),
  • Competence (a sense of being able to do something i.e. being competent), and
  • Relatedness (a sense of being associated or connected to others).

Contexts that satisfy all three basic needs will help support people’s actions, resulting in more sustained motivation over time and positive outcomes. Learners are not always motivated to complete eLearning modules/courses – it might be a requirement of their job or as part of a qualification. But, if we can use strategies to support their competence, autonomy and relatedness needs we can assist learners to internalise their motivation of these types of externally regulated activities.

What do these support strategies look like in practice?

Here are some strategies that you can apply to your eLearning that can help improve learner motivation by satisfying their basic psychological needs:

Autonomy:

  1. Allowing learners to make meaningful choices that have consequences
  2. Providing learners with more than one way to reach their end goal
  3. Allowing learners to customise their environment e.g. choosing a character
  4. Encouraging learners to take risks and be creative during the eLearning module/course

Competence:

  1. Making the rules and goals for learners clear and structured
  2. Allowing multiple opportunities to complete parts of the eLearning module/course to allow learners to build their competence
  3. Requiring learners to frequently make decisions to keep the eLearning module/course moving forward
  4. Measuring learner performance in multiple ways
  5. Increasing the difficulty as the learner progresses through the eLearning module/course
  6. Linking progression (the reward) to learner competence
  7. Providing learners with constant and varied feedback and support
  8. Allowing learners to review or replay earlier parts of the eLearning module/course
  9. Recognise learner achievement e.g. experience points or badges

Relatedness:

  1. Providing space/areas for learner interaction and discussion e.g. forums
  2. Providing opportunities for learner collaboration e.g. a group quest or challenge

Motivation plays an important role during eLearning experiences and our challenge is to create eLearning that our learners want to engage in. As educators, we have an opportunity to assist learners with the internalisation of motivation in the way we design and deliver learning experiences. While it’s not always easy, we need to use strategies that help satisfy the competence, autonomy and relatedness needs of our learners if we want to improve their motivation towards the module or course they are completing.

References:

Deci, E. L. & Ryan, R. M. (2008) Facilitating optimal motivation and psychological well-being across life’s domains. Canadian Psychology. 49 (1), 14-23.

Kapp, K. M. (2012) The Gamification of Learning and Instruction. Pfeiffer/ASTD

Niemiec, C. P & Ryan, R. M. (2009). Autonomy, competence and relatedness in the classroom: applying self-determination theory to educational practice. Theory and Research in Education. 7 (2), 133-144.

Ryan, R. M. & Deci, E. L. (2000) Intrinsic and extrinsic motivations: classic definitions and new directions. Contemporary Educational Psychology. 25, 54-67.

Also, check out this website for more information on self-determination theory: http://www.selfdeterminationtheory.org/

A similar version of this article originally appeared in Training & Development magazine, October 2013 Vol 40 No 5, published by the Australian Institute of Training and Development and eLearn Magazine, October 2013.

 

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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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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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