Category: Instructional Design

  • The Human Memory System

    This blog post is a slightly modified version of an essay that I recently submitted as part of my Master of Education (Educational Psychology) studies.

    Human Memory System

    The ability of the human brain to process, store and retrieve information has been the subject of much research and debate by cognitive psychologists over a long period of time. Whilst the terminology and functioning of the components of the human memory system (also known as our human cognitive architecture or HCA) have changed based on the findings of research, it is widely accepted that our memory system consists of a sensory memory that receives information from our surrounds, a working memory to process this information and also to retrieve information from our storage area known as the long-term memory.  This essay will discuss the research and findings about how the human memory system operates and furthermore, the application of these findings to learning and instruction.

    As far back as 1890, the human memory system was proposed by William James to be a dual-system comprising of a primary memory (or conscious awareness) and a secondary memory (containing lasting memories). However, it was not until the late 1950’s that evidence and acceptance for the division of the memory into multiple systems began to emerge. Around this time, research by Peterson and Peterson found that unfamiliar information could only be held for a matter of seconds before being forgotten. In addition, Brown proposed that memory traces decay over time and his experiments also demonstrated forgetting occurring over short time periods. This in turn led to the proposal that human memory be separated into short-term and long-term systems. These findings added to the earlier research of Miller (1956) who found that there were limitations to the capacity of information that can be processed by the human memory system, in this case he discovered that only about seven (plus or minus two) pieces of new information could be held at any time.

    During the 1960’s, short-term memory (STM) and long-term memory (LTM), also referred to as the long-term store (LTS), were conceptualised as separate systems and this was reflected in the various models that began to emerge. Of these, Atkinson and Shiffrin’s Modal model (1968) became the most influential depiction of the human memory system. This model “assumes that information comes in from the environment through a parallel series of sensory memory systems into a limited-capacity short-term store (STS), which forms a crucial bottle-neck between perception and LTM. The STS was also assumed to be necessary for recall, and to act as a limited-capacity working memory” (Baddeley, 2000, p.81).

    Despite the influence of the Modal model, two shortcomings became apparent in the early 1970’s. The first the assumption was that if information was held in the STS for a sustained amount of time, there would be an increased likelihood that it would be transferred to the LTS. This did not account for any processing of the information and was contested by Craik and Lockhart in 1972, who incidentally, were not in favour of multi-store models, instead conceptualising memory as being “tied to levels of perceptual processing” in what they referred to as the primary memory. This resulted in the development of their framework of levels of processing known as Type 1 or surface processing and Type 2 or deep processing. The second shortcoming of the Modal model was that the structure of the model itself implies that a person with a damaged STS would therefore experience problems processing information and also long-term learning, however further studies on people with impaired STS found that this was not the case.

    Working Memory
    While the Modal model proposes a single STS, Baddeley and Hitch (in 1974), conceptualised a working memory comprising of three components – two slave systems known as the phonological loop and the visuo-spatial sketchpad both of which are controlled by the central executive – that replace the STS. The term working memory has largely been adopted in preference of the term short-term memory and better reflects the processing activities carried out by this part of the human memory system. The phonological loop is somewhat similar to the conceptualisation of the STS and is comprised of a phonological store that holds “acoustic or speech-based information for 1 or 2 seconds” (Baddeley, 1992, p. 558) and an articulatory control process that, with repetition, circulates information from the phonological store via an inner voice. In addition, the process also converts “visually presented material such as words or nameable pictures” into a form that can be registered by the phonological store (Baddeley, 1992, p.558). The role of the visuo-spatial sketch pad is to process visual as well as spatial information. Coordinating the activities of both slaves systems is carried out by the central executive however, unlike the phonological store and visuo-spatial sketchpad, the empirical evidence demonstrating the existence of a central executive has not been found.

    This model of working memory was modified by Baddeley in 2000 in order to address two concerns arising from the original model. The first concern was in relation to the integration of the working memory components (due to each component coding information differently) and the second was around how the working memory communicates with long-term memory. To address these concerns a fourth component known as the episodic buffer was added to the model was assumed to be “a limited capacity temporary store that forms an interface between a range of systems all having different basic memory codes. It is assumed to do so by having a multi-dimensional coding system” (Baddeley et. al. 2010, p.229). However, rather than the active link between the subsystems, it was found that the episodic buffer was a more passive store of bound information and not responsible for binding coded information.

    It must also be mentioned that information enters the working memory from one of two sources, either from our sensory memory through our interaction with the world around us or it is retrieved from our long-term memory.

    Long-term Memory
    Whilst the working memory is responsible for the active processing of information, the long-term memory is the storage area of the human memory system. Our long-term memory “consists of a large, relatively permanent store of information” (Sweller, 2004, p.11). It was the work of De Groot who found that chess grand masters were able to defeat novice players because they held vast numbers of board configurations in their long-term memory. This was demonstrated when grand masters were able to accurately reproduce real game board configurations compared to novice players. This finding was confirmed by Chase and Simon (1973) who found that grand masters could reproduce mid-play board configurations with fewer referrals back to the mid-play board. Interestingly, Chase and Simon also found that grand masters performed worse than novice players when attempting to reproduce random chess board configurations because they were attempting to apply actual configurations to a haphazard setting.

    Information is stored in the long-term memory in knowledge structures known as schemas. Also known as mental models, schemas “permit us to treat a large number of information elements as a single element” (Clark et. al, 2006). The reason that chess grand masters performed better than novice players in reconstructing actual board configurations is because they have many more board configuration schemas stored in their long-term memory that they can access. The number of schemas held is what differentiates experts from novices therefore, the focus of any instruction should be the formation and construction of schemas in the long-term memory.

    Implications for Learning and Instruction
    The study of the human memory system and its components has provided extensive evidence about how humans process and store new and existing pieces of information. This knowledge is essential when it comes to designing instructional activities and account for the processing and storage capabilities of the human memory system. When learning something new, there are three types of cognitive load: intrinsic which is the inherent level of complexity of the content, germane which allow cognitive resources to be put towards learning and extraneous which are irrelevant elements that actually impose extra mental processing. These forms of cognitive load are additive, therefore in order for instruction to be effective and permit transfer to long-term memory, they should not exceed working memory capacity.

    Cognitive load theory is “a universal set of instructional principles and evidence-based guidelines that offer the most efficient methods to design and deliver instructional environments in ways that best utilise the limited capacity of working memory” (Clark et. al, 2006, p.342). Examples of these principles include: the worked example effect – giving novice learners worked solutions of unfamiliar problems to study, the split-attention effect – reducing the need to integrate multiple sources of information in order for it to be understood, the modality effect – presenting information via both the visual and auditory channels and the redundancy effect – not presenting the same information via both the visual and auditory channels. Applying these principles to instructional design will facilitate improved learning outcomes because they incorporate the findings of research into the functioning of the human memory system.

     

    References

    Baddeley, A. D. (1992). Working memory. Science, 255(5044), 556-559.

    Baddeley, A. D. (2000). Short-Term and Working Memory. In Tulving, E., & Craik, F. I. M. (Eds) The Oxford Handbook of Memory, 77-92, Oxford University Press.

    Baddeley, A. D., Allen, R. J., & Hitch, G. J. (2010). Investigating the episodic buffer. Psychologica Belgica, 50(3&4), 223-243.

    Clark, R., Nguyen, F., & Sweller, J. (2006). Efficiency in Learning, San Francisco: John Wiley & Sons Inc.

    Craik, F. I. M., & Lockhart, R. S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11, 671-684.

    Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2), 81-97.

    Ricker, T. J., Vergauwe, E., & Cowan, N. (2014). Decay theory of immediate memory: From Brown (1958) to today (2014). The Quarterly Journal of Experimental Psychology, 1-27.

    Sweller, J. (2004). Instructional design consequences of an analogy between evolution by natural selection and human cognitive architecture. Instructional Science, 32, 9-31.

  • Practice and Sharing: The Keys to Success

    shutterstock_88500487

    These were the two messages that stood out over three days in Sydney at last year’s iDesignX Australian Instructional Design Conference (21st March) sponsored by B Online Learning & Articulate and the eLearning Design Workshops with Tom Kuhlmann and David Anderson (22nd and 23rd March). I was fortunate to attend all the sessions and for me it was a dream come true to not only be in the same room as Tom and David but to hear and learn directly from them (I also got to meet them which was an incredible experience and a real highlight too).

    Practice

    As someone with a keen interest in learning generally, but eLearning in particular, I’m always looking to other experienced people in the learning field to find out how I can improve my own skills and knowledge. While it would be great if there was a magic pill you could swallow and voila! you’d be transformed into an eLearning whizz, the reality is that when you look at anyone who is successful in their field, the one thing they have in common is a commitment to developing their skills over a period of time. Tom and David are no exception to this. Over the years they have worked on many projects but they also make time to experiment and try new things. The speakers at iDesignX also showed that they have put in a lot of effort over the years to get to where they are today.

    Tip: a good place to start practicing your eLearning skills is in David’s Weekly Challenge. You can also learn more about building great eLearning courses at Tom’s Rapid eLearning blog.

    Sharing

    Tom and David are role models when it comes to sharing. Their jobs at Articulate along with their travel schedule must keep them extremely busy. However, they are extremely generous with their time and have a great willingness share what they know, provide advice and help anyone who needs it. It’s something all learning professionals can learn from and do more of.

    So, in the interests of sharing, here’s firstly what I took away from iDesignX:

    “Instructional design is about crafting the appropriate learning experience. We need to reframe content so that it’s meaningful and relevant. Then we need to give learners something to think about and have them make decisions.” Tom Kuhlmann – VP Community at Articulate

    “Tips when using virtual training: prepare and support participants, consider cognitive load, design for different levels of engagement, have learners interact often, support facilitators, pilot the training and test, test, test, test.” Brenda Smith – Medibank Health

    “When using video in learning experiences, authenticity is very important.” Mark Parry – Parryville Media

    “Clean and balance (in graphic design) creates stability and can direct learner focus.” Minh Nguyen – DEEWR “Using curation for learning design > collect, filter, evaluate, arrange, present, distribute.” Anne Bartlett-Bragg – Ripple Effect Group

    “Before you gamify your eLearning course, make sure it meets the learning objectives.” Ruth McElhone – B Online Learning

    “Learning experiences should be meaningful, memorable and motivating.” Ruth McElhone – B Online Learning

    “Using video for manual or process tasks shows the correct way to do something.” Tony Nye – Australian Red Cross Blood Service

    “Pictures clarify words and stories add context to content.” Blair Rorani – Ever Learning

    “What makes an industry pro? Experience; Skills (practice your craft); Authority and Luck.” Tom Kuhlmann – VP Community at Articulate

    “Luck is where opportunity and preparation meet.” Tom Kuhlmann – VP Community at Articulate

    “You need to be proactive and look for opportunities. Sharing expertise creates opportunities.” Tom Kuhlmann – VP Community at Articulate

     

    And from the workshops with Tom and David:

    On designing an eLearning course:

    Ask yourself:

    1. What content needs to be in the course?
    2. What is the right look and feel?
    3. What is the learner supposed to do?

    Be intentional, stick with a consistent design and don’t settle for defaults (colours, fonts etc.)

     

    On eLearning makeovers:

    Review the five common components of eLearning courses:

    1. Text – should be from the same font family
    2. Elements – the goal is unity not uniformity
    3. Colours – use colour for contrast and emphasis
    4. Background – it should contribute to the visual and not dominate
    5. People – if you use characters maintain unity

     

    On interactivity:

    Interactivity connects the user to content. There are two types of interactivity:

    • Touch – the learner interacts with the screen (by clicking, dragging or hovering)
    • Decision – the learner interacts with the content.

     

    On Learning Objectives:

    When thinking about learning objectives, ask yourself:

    • Who is the learner?
    • What is the situation?
    • What do you want them to do?
    • How can they prove it?

     

    On building interactive eLearning:

    • Know your tools – don’t build clunky courses
    • Create relevant content
    • Use stories for learning especially if there’s a lot of content
    • Remember the 3 C’s:
    1. Challenge the learner
    2. Give them choices
    3. Have consequences for decisions

     

    There were at least a couple of hundred people at the conference and about 80 people each day at the workshops. If everyone incorporates just one or two of the things they learned into their eLearning courses the quality would certainly improve. But if everyone also shared what they’ve learned with others in the field, it would help to improve even more courses and contribute to building a strong community of learning professionals!

    All in all it was a great three days of learning from the best in the field and also chance to meet lots of people who I’d only known via Twitter and make the physical connection. Let’s follow Tom and David’s example by practicing our skills and sharing what we know so that we can develop ourselves as well as others.

    Footnote: This post originally appeared on my old blog site “Learning Snippets” and the B Online Learning blog in 2014.

  • Working with Cognitive Load

    When I first started working as an eLearning 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 and materials. 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.

    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 word-for-word.
    • 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.

  • 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 previously about motivation – Motivation and eLearning – which was 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. It has also been validated in studies across different education levels.

    Dr 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.