For The Love Of It All
Imagine a little 4 year old boy who wanted to go to school with the bigger kids so much that he bugged his parents until they took him to take the tests that would allow him to start kindergarten early. That 4 year old who couldn’t wait to start school is now approaching 40 but hasn’t lost any of the desire to learn that he started with way back then. That little boy is me. As long as I can remember I’ve always enjoyed learning new things. I consider myself extremely fortunate for the opportunity to make a living in a learning profession.
Before heading down the educational technology path I spent 7 years working for a telecommunications company until I finally came to the realization that I really didn’t like going to work each day. That was when I knew I needed a change and took a job teaching adult education classes. Right away I knew that I had made the right decision. That was 1997, which was also when I took my first Ed Tech class (540) with Donn Ritchie and the SDSU Ed Tech seed was planted.
That position led me into software application training which led to some work creating training materials and ultimately to my current position as a learning development consultant. Throughout this progression I always had the intention of continuing in the Ed Tech program and was very excited to pick back up where I’d left off.
Before my time in this program most of my work was based on intuition and the personal preferences I had as a learner. Standing here at the end of the program I’m able to know when to trust, and when not to trust, those intuitions and why. Fortunately, I don’t think my natural intuition was too far off the mark but understanding the relevant learning theories, models and approaches provide a solid foundation for those intuitions and give me a lot more confidence in what I am doing.
To me, having that confidence is invaluable and undoubtedly makes me better at what I do. All the steps I’ve taken on this journey have shown me that I’ve chosen the right path and love what I do.
Lovable Ideas
What I like most about my experience in this program is that the foundational theories and principles I’ve adopted provide instructionally sound guidelines while allowing the flexibility to choose among a variety of the most appropriate tactics for achieving instructional goals.
The ideas I’ve chosen are important to me because they give me an excellent framework within which to design and develop learning materials. They are all complimentary to one another in the fact that they are learner-centric and provide general guidance without limiting the flexibility of specific tactics that can employed.
Although it seems painfully obvious now, exposure to these ideas has led me to see how unique every learning project really is. I now have a much more comprehensive view of how different objectives are best achieved by the use of different instructional methods. No theory illustrated this more to me than the Content-Performance Matrix I experienced early in the program.
Component Display Theory / Content-Performance Matrix
Although I don’t think I ever believed there was any single, cookie cutter method that should always be used to design instruction, seeing Ruth Clark’s content-performance matrix was a real turning point for me. It
made me stop to think about all the potential types of learning and the specific characteristics of each. Of course, teaching someone to remember a fact requires a much different instructional approach than teaching them to apply a principle requires. Prior to studying this I don’t think I ever gave this area the thought it deserved. This matrix stirred me to become much more conscious of what the instructional objectives entail and look at them in much closer detail than I had ever done before.
To me, Clark’s matrix is a simpler, more straightforward presentation of Merrill’s Component Display Theory (CDT) which describes instructional methods that are linked to five specific content types: facts, concepts, processes, procedures and principles and three types of behaviors: remember, use/apply, and find.
Gagne’s Nine Events
Gagne’s Nine Events serves as a great checklist of considerations for what things should take place during instruction regardless of the audience or deliver format. It does this while allowing the designer flexibility in implementing the specific ways of achieving these events. These events are learner focused in the sense that they are mental events that occur when the learner faces various conditions.
- Gain attention: In order for any learning to take place, you must first capture the attention of the student. Keller’s ARCS model discussed below elaborates on this idea further.
- Inform learners of objectives: To initiate the internal process of expectancy and to help motivate the learner, learning objectives should be presented early in the each lesson.
- Stimulate recall of prior learning: By associating new information with existing knowledge the learning process can be enhanced. It is easier for learners to encode and store information in long-term memory when there are links to personal experience and knowledge.
- Present the content: When presenting new content to the learner it should be chunked and organized meaningfully which ties in very well with another of my important ideas – Cognitive Load Theory.
- Provide “learning guidance”: To help learners encode information for long-term storage, additional guidance should be provided along with the presentation of new content. This event is also supported by the research related to Cognitive Load Theory.
- Elicit performance (practice): Allowing the learner to practice the new skill or behavior provides an opportunity for learners to confirm their correct understanding, and the repetition further increases the likelihood of retention.
- Provide feedback: As practice of the new behavior is undertaken it is important for the learner to receive specific and immediate feedback of their performance. Unlike questions in a post-test, exercises within tutorials should be used for comprehension and encoding purposes, not for formal scoring.
- Assess performance: Upon completing instructional modules, students should be given the opportunity to take an assessment without the ability to receive additional coaching, feedback, or hints. This event serves to indicate mastery of the material, or certification.
- Enhance retention and transfer to the job: One approach to this event is to apply learning in real-life situations. For me this last event serves as a reminder that retention and transfer to the job is the ultimate goal of the instruction I am designing.
ARCS Model
Keller’s ARCS Model which can be linked back to many of Gagne’s Nine Events, is about promoting and sustaining learner motivation. Learner motivation can often make or break an instructional experience. Even the best designed learning is more likely to fail if the participants do not have the motivation or desire to learn. This is a common problem in the corporate environment I work in due to the amount of instruction that is mandated with learners participate mostly against their will because they are told that they have no choice.
ARCS stands for Attention, Relevance, Confidence, and Satisfaction. Just like Gagne’s model, the first and in this case most important aspect of the ARCS model is gaining and keeping the learner’s attention. Keller also provides some strategies for achieving this goal such as sensory stimuli, inquiry arousal, and variability.
Once gained, this attention is not likely to be maintained without the learner thinking that the content is relevant. This provides them with the answer to the important question of, “What’s in it for me?”
The confidence aspect comes into play by giving the participants the sense that they are capable of achieving the objectives and that the results are worth the time and effort required to do so. If they think they are not capable of achieving the objectives or if they think the time and effort required are too high, their level of motivation will go down.
Lastly, learners must get some sort of satisfaction from the learning experience. This satisfaction can take many internal or external forms including verbal reinforcement, rewards, personal attention, feedback, and deliberate avoidance of negative influences such as external performance evaluations.
E-learning and the Science of Instruction
Perhaps my very favorite ideas are those I found in Clark & Mayer’s “E-learning and the Science of Instruction”. This book provides a wealth of practical applications for the research relevant to the design of E-learning. The main things I’ve taken from this book are the research based principles of effective e-learning which heavily incorporate concepts from the Cognitive Load Theory.
The six principles are listed below.
» Multimedia principle: Adding graphics to words can improve learning. The use of graphics including drawings, charts, photographs, animations and others can improve learning. But not all graphics are created equal. The main point here is to use graphics that are consistent with the instructional message. Adding other images for pizazz or entertainment purposes not only doesn’t help but can actually decrease learning.
» Contiguity principle: Placing text near graphics improves learning. This principle that says that graphics and the text related to the graphics should be placed near each other on the screen. Placing visual and related text separate from each other, requires the learner to use extra cognitive resources to integrate them which lowers the amount of cognitive resources available for learning.
» Modality principle: Explaining graphics with audio improves learning. Audio narration in place of textual explanations can substantially improve learning outcomes; particularly when it involves complex animations or visuals of complex and unfamiliar content. For example, trying to read text and watch a related animation at the same time is more likely to cause cognitive overload than if you can listen to the explanation instead.
» Redundancy principle: Explaining graphics with audio and redundant text can hurt learning. With the exception of a small number of situations where there is no graphic on the screen or when readers lack good reading skills, learning is actually depressed when a graphic is explained by a combination of text and narration that reads the text.
» Coherence principle: Using gratuitous visuals, text, and sounds can hurt learning. Adding extra snazzy content or what Mayer calls “seductive details” such as background music, popular movie characters, etc for entertainment value have a negative effect on learning. The inclusion of these types of items distract learners from the key instructional points, disrupt their organization of the information into a coherent mental model and may activate irrelevant prior knowledge. We should distinguish between cognitive interest which stems from materials that optimize learning, and the emotional interest stemming from the addition of extraneous materials.
» Personalization principle: Use conversational tone and pedagogical agents to increase learning. Humans have deeply ingrained social conventions of interaction which tend to have an unconscious effect in human-computer interactions. What this means is that people responded to computers with the same social conventions they follow when responding to other people. The implication for e-learning is that by socially engaging the learner via the use of conversational language or by an informal learning agent we can improve learning.
I appreciate how practical these principles are and that they can also be applied to the design of materials for use with a multitude of delivery mediums.
Cognitive Load Theory:
Cognitive Load Theory (CLT) suggests that learning is most effective when it takes place under conditions that are aligned with human cognitive learning processes that have been proven to result in more efficient instruction. According to CLT, working memory is only limited when you’re learning new information. Once information is in long-term memory, it can be brought back to working memory in very large amounts. Therefore, this theory is primarily concerned with reducing the load on short-term memory to facilitate the desired changes in long-term memory.
The significant limitation of our limited short-term memory can be combated by techniques that support and enhance the construction of schema by the learners. Essentially, a schema is a chunking of information into personally meaningful units that allow us to treat multiple elements as a single element.
Specific recommendations for designing instructional material include:
- Avoid means-ends problem solving approaches that impose a heavy working memory load by using goal-free problems or worked examples.
- Physically integrate multiple sources of information to eliminate the working memory load associated with having to mentally integrate several sources of information.
- Reduce redundancy to eliminate the working memory load associated with unnecessarily processing repetitive information.
- Use auditory channels in conjunction with visual information when both sources of information are important (i.e. non-redundant) for understanding.
This theory is relevant and complimentary to all of my previous outstanding ideas and gives me a great guide for managing any extraneous components of the instruction to maximize learning efficiency. This is particularly true in my current position where I am often working with inherently complex topics related to the operation of coal and gas powered electricity generating power plants.
One particular corollary of this theory that I deal with regularly is the expertise reversal effect which reminds me that instructional approaches that are effective for novices may be ineffective for more expert learners.
I love how each new project is like solving a puzzle. I love puzzles. crossword puzzles, jigsaw puzzles, sudoku puzzles and most of all, the puzzles that must be solved in order to create effective learning experiences. Every project presents its own unique set of challenges all of which must be considered in conjunction with the others. In addition to the great number of inputs the number of possible solutions is also quite high, ranging from simple one page job-aids to totally immersive virtual simulations.
These ideas help me successfully solve the learning and performance puzzles I am faced with every day. They provide guidance for matching all of the relevant factors with each other to arrive at the best possible solution for each particular situation.
What attracts me to these standout ideas is the fact that they provide a proven, effective set of guidelines around which to construct learning solutions. The fact that they are largely complimentary with one another and allow for a good deal of implementation flexibility even furthers my attraction to them. I love the process of combining my understanding of all the theories and systems at work with a healthy dose of creativity and ingenuity. As Merrill himself has stated, these principles are “deliberately general and their implementation can take many forms.”
Although not an idea or theory, I can’t overlook the energy and passion of the instructors and fellow students I’ve encountered who have been very inspirational and energizing to me. It has been a pleasure making connections with like-minded people who love what they do as much as I do.
Cha Cha Change
I believe the real value of an instructional designer is knowing when to apply what instructional strategies to what type of content. What constitutes the best application of strategy today is likely to be different than what constitutes the best application of that same strategy tomorrow. Perpetual maintenance of my personal knowledge base is essential to my continued success in this field.
Managing the future trajectory of these ideas and how I practice them comes down to two things. First is keeping up-to-date on the latest research and practical results within our field. Recent technologies and services have made discovering and keeping track of this information much easier than ever before. To me that is the easy part. The second and more difficult thing is personally interpreting what information is relevant to my environment, deciding when it merits a change and how to go about making that change.
One thing I need to be careful of is not to over emphasize technology at the expense of effective instruction. Being naturally attracted to the latest technologies I need to always remember that it is not the technology itself I should be concerned with, but what the technology allows us to do. After all, snazzy high-tech productions without sound instructional principles are less likely to succeed than soundly designed paper and pencil versions.
When it comes right down to it we still need to communicate the same basic information when designing learning regardless of delivery medium or which instructional strategy we employ. Who? What? When? Where? and Why? Basically, “What’s in it for me?” and ” Why should I care?” To me the stand out ideas that I have identified above can ultimately be boiled down to guidance for addressing these questions in the most effective, efficient way.
Among the recent trends that I’ve personally noticed is the increasing use of rapid development tools and processes to speed up the instructional design process. It is becoming more common to adapt conventional approaches in various ways to save time and resources. This is often achieved by shortening or combining some activities and relying heavily on existing data and materials to create a leaner version of the material with the intent that it will be continuously improved over time.
The collaborative nature of web 2.0 technologies can also help shorten development cycles by facilitating the collaboration and review of instructional material. This allows more input and feedback from more sources faster than ever before.
Another factor that contributes to speeding up the process is the existence of an increasing amount of Subject Matter Expert 9SME) and learner generated content created via easy to use rapid development and web 2.0 tools. As this type of content becomes more and more common we will have a wider selection of existing material to include in our instructional packages.
However, this can be a double-edged sword since most SMEs and learners have no idea how to design learning effectively and the signal-to-noise ratio will undoubtedly decrease. As others become more involved in the process of creating content I can see the need for me to serve as a filter for that increasing level of noise. As a colleague and I often say about poorly created content, “Just because you can, doesn’t mean you should.”
In my experience it is not always the best idea to depend on SMEs too heavily for developing content because they are usually very busy doing their real job and not always good at articulating what they do anyway; particularly to novices. Depending on the complexity of the topic, interviewing them may be more efficient and effective approach in the end.
As I move forward into the future my goal is to continuously improve my understanding and application of the relevant available knowledge to most effectively solve the many challenging instructional puzzles I will encounter.
“As the mind learns to understand more complicated combinations of ideas, simpler formulae soon reduce their complexity.” – Antoine-Nicholas de Condorcet (1794)
References:
Clark, R. (1999). Developing Technical Training: A Structured Approach for Developing Classroom and Computer-based Instructional Materials , 2nd Edition. Washington D.C.: Pfeiffer.
Clark, R., & Mayer, R. (2007). e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning. Washington D.C.: Pfeiffer.
Dempsey, J., & Reiser, R. (2006). Trends and Issues in Instructional Design and Technology (2nd Edition). Alexandria, VA: Prentice Hall.
Gagne, R. M. (1985). The conditions of learning and theory of instruction (4th ed.) NewYork: Holt, Rinehart and Winston.
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38, 23-31
Merrill, M.D. (1983). Component Display Theory. In C. Reigeluth (ed.), Instructional Design Theories and Models. Hillsdale, NJ: Erlbaum Associates.
Sweller, J. (1988). Cognitive load during problem solving. Cognitive Science, 12, 257-285.
