We’d all like to get answers to some “eternal questions” in e-Learning. One of the most important questions is, “Why do people drop out of e-Learning?” Is it because of design and delivery problems? Or is it something else?
One possible answer may have to do with human learning styles, and it seems that many organizations are looking into this. In the “Learning Styles in e-Learning Poll” conducted in April 2003, almost two-thirds of the Guild Members who responded said their organizations are designing e-Learning based on the learning styles of target learners. Is this a good approach, and what does it take to use learning styles successfully in e-Learning?
In the traditional classroom setting, a good instructor will adjust teaching style to match the character of the group and the individuals in it, so that everyone is able to meet the objectives. In e-Learning, though, we face a number of challenges. For asynchronous e-Learning, we must build a system smart enough to adjust content and delivery to the needs of the individual. In synchronous e-Learning, of course, the instructor is virtually “present” but is unable to receive many of the little cues that, in a conventional classroom, would make it clear which learners are having trouble and which ones are mastering the skills and knowledge.
One solution is to identify an adaptive strategy that will connect the e-Learning design to the learner’s style. The e-Learning design can then provide logic or options that will deliver information and guidance to a learner in the way best suited to that individual.
Learning styles
Learning is a highly individual experience. Two people who go through the same set of events are very likely to come away with totally different “lessons learned.” Even when a group of people go through the same course, and all of them are able to meet the criterion test requirements at the end, it is probable that each individual will have learned to do the same thing in slightly different ways. To the extent that instruction is based on a sound strategy and design, and to the extent that the delivery of that instruction matches an individual’s learning style, a learner will be more successful in achieving the goals of the instruction. This is the fundamental idea that drives designers to consider learning styles.
Leaving aside the question of sound strategy for the moment, let’s look at learning styles. This is an area in which there are many competing theories and models, and not much compelling evidence favoring one theory over the others. What evidence there is seems in many cases to be contradictory, and even the experts agree that the situation is confusing.
However, many organizations choose to include learning styles as a consideration in design, and to accommodate the use of styles in their e-Learning applications. My purpose in this article is to outline three of the leading style theories, provide information about the research, and suggest ways you might use this in your own work.
A warning
Be aware that, while much of learning theory is young and subject to change, the learning styles arena is possibly the most “gray” area on the map. It would be a very good idea to carefully track what you try and the results you obtain. This “action research,” shared with others and applied carefully, will eventually become your best guide to what works.
What is a “learning style?”
“Learning style” is usually defined as a set of stable characteristics that affect the way a person perceives and interacts with the environment while learning. As such, learning style is an individual difference that can be taken into account when designing the content in any instructional system.
There are many learning style models and theories, and many other psychological measures are used by classroom instructors as the basis for adjusting their teaching to individual learners. For example, the Meyers-Briggs Type Indicator (MBTI) is a well-known measure of psychological type frequently mentioned in the literature, as is the Herrmann Brain Dominance Instrument. Gardner’s Multiple Intelligences theory is another system that has been used to account for different learning styles.
Apart from these broad theories of intelligence and personality type, theory and formal research on learning style have mainly pursued two other ways to characterize learning preferences. One view is based on understanding the learning process, and the other is based on understanding how people take in, store, and retrieve information. These views are not necessarily mutually exclusive, and they are both important to the designer’s understanding. From these two views, three methods have emerged that are designed for the purpose and are in common use world-wide. Many of the other measures and instruments available on the Web are based on these three which are the methods discussed here. In addition, these three methods have been extensively studied, and at least a few well-designed studies of their use in e-Learning (rather than in traditional instructor-led classroom settings) have been completed.
The experiential view
Kurt Lewin, a Gestalt psychologist, was responsible for the origin of many influential ideas about how people learn. One of his most important contributions was a model of the adult learning cycle. (See Figure 1.)
FIGURE 1 Lewin’s adult learning cycle describes four stages.
Lewin suggested that all people learn in four distinct stages, beginning with a concrete experience. After an experience the person reflects, and either derives some general rules or figures out how it relates to their previous experience and theories. Active experimentation then helps the individual identify ways to deal with the next concrete experience. The amount of time it takes to get through this cycle varies, and the cycle can, and in fact often does, repeat.
The most direct application of Lewin’s cycle to learning design is simply to ensure that every learning experience allows appropriate time and adequate attention for each of the four stages. This approach is most often seen in the design of experiential and collaborative learning, in which learners first participate in an exercise or game that creates concrete experience relevant to the learning goals, followed by “processing” or group discussion of the experience (reflective observation), small group work to identify underlying meaning (abstract conceptualization), and action planning to lay out how the participants will apply the ideas after training is over (active experimentation).
Even in “technical training” it is very common to provide practical work (concrete experience), to facilitate discussion of what has been learned (reflective observation) and ways to apply it (abstract conceptualization), and to have learners write up two or three things they will do back on the job (active experimentation). This accomplishes essentially the same four stages.
At a minimum, according to the Lewin model, it is important to ask questions and make assignments that will cause the learner to actively engage in each of the activities. The questions and assignments can be provided by a live instructor or by an e-Learning application.
Notice that these stages apply to all learners, regardless of their individual learning styles. A learner who does not successfully get through all four stages will not be successful in applying what was taught. Also note that it may take some time for a learner to get through all four stages, and it may be necessary for the learner to go around the cycle more than once in order to meet the goals of the learning experience. Failure to successfully apply the learning on the first attempt provides additional concrete experience that fuels more reflective observation, refinement of understanding, and another attempt at active experimentation. Learning sometimes requires successive approximations, which is one purpose of practice with feedback during training.
Over time, researchers noticed that individuals tend to have strengths in or preferences for each of the stages in Lewin’s model. This led to two closely related learning style theories, each of which describes four learning styles.
Before going on to describe these theories, there are three important points to note. First, there are very few, if any, learners who are only competent in one style. Most people can apply two or three of the styles, and some can apply all four. It is useful for learners to be aware of their dominant learning style, and to receive coaching on applying the others to improve their learning.
Next, for the e-Learning designer, it is critical to provide activities that are matched to all four styles, and then to provide a way to either serve those activities appropriately in the course of the e-Learning, or allow learners to choose additional activities that appeal to them. The designer’s dilemma is that, left to their own devices, people tend to make poor choices from among alternative learning activities, and programmatic choices made by the e-Learning application may be inappropriate. At the same time, it would be unwise to rely totally on the results of any “paper-and-pencil” test of learning style. Currently, none of the available instruments offer sufficient reliability to justify this. Therefore, offering the learner the option to choose a different path to the one indicated by the instrument results is important.
Finally, it is expensive to provide the design and authoring required to enable alternative paths through an e-Learning experience. These are not trivial issues, and they will be addressed at the end of this article.
Kolb
The first of the two closely-related learning style theories I’ll discuss is David Kolb’s learning cycle model. His model and associated terminology are based on Lewin’s (especially regarding the importance of being active in learning), and on ideas from John Dewey (the need for learning to be grounded in experience), Jean Piaget (intelligence as the result of interaction between a person and the environment), and J. P. Guilford (convergence and divergence). Kolb also created the Learning Style Inventory, a paper-and-pencil instrument that identifies an individual’s dominant learning style. The LSI is sometimes called the “KLSI,” for “Kolb’s Learning Style Inventory” to distinguish it from all the other learning style inventories that have been developed, and it is also sometimes shown with a number following it — e.g., KLSI-3 — to indicate which of Kolb’s several revisions it is. See Figure 2. Also see the References, for information on Kolb’s book.) Kolb identified four different ways that people approach learning based on their preferences for the different stages of the learning cycle.
FIGURE 2 Kolb’s Learning Styles explain how individuals specialize in different phases of Lewin’s cycle.
FIGURE 3 Honey and Mumford’s Learning Styles names individual preferences for each stage.
- Diverging: Someone who uses the diverging style (a “diverger”) learns by looking at experience from several points of view and by generating lots of ideas. Divergers are imaginative and open-minded, believe they understand people, and are alert to look for and recognize problems. A diverger would benefit from being able to review case studies that don’t have “cut and dried” solutions, in order to come up with a number of different ways to solve the problem presented.
- Assimilating: This style relies on inductive reasoning (working from examples in order to derive the “rules”). Assimilators like to come up with theories and models and to do planning. They are very patient, and want detailed background information about theory and practice. Give them a problem where they can apply a theory, or where they can come up with a theory about why there is a problem.
- Converging: Convergers are driven to solve problems and make decisions. They rely on deductive reasoning (applying the “rules” to specific instances). A converger will want “hands-on” examples for which there is only one answer, or where they choose the best answer from several possibilities. Give them a lot of facts to sort out.
- Accomodating: These are the risk-takers and leaders who are compelled to get things done, even if (especially if) it involves taking risks. Accomodators like games, particularly if there is a range of payoffs that depend on the skill with which the game is played. They also do well with exercises that involve multiple scenarios and decisions to be made about allocating or assigning resources.
Kolb’s LSI is probably best known and most used in the United States. Designers can order paper copies of the LSI, supporting books, and other materials from http://www.hayresourcesdirect.haygroup.com/Learning_Self-Development/Assessments_Surveys/ Learning_Style_Inventory/Overview.asp. There is also an option there to take the LSI online.
Honey and Mumford
The second of the two closely-related learning style theories I’ll discuss is that of Peter Honey and Alan Mumford, two researchers in the United Kingdom. In 1982 they extended Kolb’s model by defining individual preferences for each stage of Lewin’s cycle. (See the sidebar, References, for information on their book.) Honey and Mumford’s model is best known outside the United States, and is particularly popular in the UK, Europe, and Australia.
Honey and Mumford’s typology gives a name to the learning style associated with individuals who would especially like each of the stages of Lewin’s cycle (see Figure 3). The four styles are:
- Activist: This is a person who has a preference for doing and experiencing, especially where new ideas are concerned, but who gets bored with the details of implementation. In e-Learning, an activist will not be happy about having to work through a lot of screens of explanation or detail, and they may not read the instructions. Activists like simulations and having problems to solve. They may do well in synchronous settings if they can lead discussions, and if the pace is fast, but they will not sit and listen to (or read) long lectures.
- Reflector: The reflector is a person who would rather look at a situation or question “from all sides,” think about it carefully and listen to the ideas of others before coming to a conclusion. Reflectors like analysis and hate deadlines. In e-Learning, a reflector will appreciate the opportunity to read and study different sources of information and different ideas about the topic. They won’t mind writing reports, as long as they have plenty of time to do a good job of analysis. In synchronous settings, they will probably have to be encouraged to participate, and they are likely to be the person who offers to write up their team’s findings (don’t be surprised if they write up their own conclusion rather than the team’s).
- Theorist: A theorist is good at coming up with explanations for things, and enjoys complexity. Theorists like structure, they are analytical rather than subjective, and perfection is their goal. Theorists in an e-Learning course like the structure, and they like to know the objectives. They will enjoy the overview, especially if it involves an explanation of the theory behind what is being taught. In synchronous e-Learning, theorists will want to have a chance to ask questions.
- Pragmatist: A pragmatist is practical and wants to get to the heart of the matter. Pragmatists aren’t terribly patient with theory and are anxious to put into practice what they learn. Pragmatists also like models they can copy, which means they will be looking for videos, graphics, and other examples in an e-Learning program. It may be difficult to get a pragmatist to enroll in an e-Learning program, and when they have learned what they came to learn, they are likely to drop out and go back to work. Give the pragmatist feedback, guidelines, and suggestions, and a chance to practice with the new skills. Find additional information about the Honey and Mumford styles at http://www.campaign-for-learning.org.uk/resources/links.htm. The Honey and Mumford Learning Styles Questionnaire (LSQ) is available for order or for use online at http://www.peterhoney.com/product/brochure;jsessionid=4fqfe0k621 (Editor's Note: As of February 22, 2010, this article appears to have been removed from the Web.) (the shopping basket is available for UK customers only; overseas customers should send an email to orders@peterhoney.com detailing the order, and the company will reply with the total cost including shipping costs.)
The perceptual view
Everything that we experience comes to us through one or more of our physical senses. This includes the concrete experiences in which our learning is grounded, tested, and applied. Obviously, learners use all of the senses that are available to them all of the time, but each individual probably tends to pay more attention to one sense. Some approaches to learning styles are based on a perceptual model of learning, with the basic assumption that each person will have a preference for learning through one sensory channel over the others.
The way in which the senses are defined varies from one perceptual model to another. For example, while sighted persons use vision to read, should reading be considered visual, auditory (many people “hear” words as they read them), or something else (usually “verbal,” meaning that reading is processed differently in the brain than is looking at pictures, and that listening to spoken information is processed differently than is listening to music)? Is the somatic feeling one gets when one answers a question correctly the same as the haptic sense (touch) or the proprioceptive sensation that tells a gymnast on the uneven bars when her limbs are in the correct position for a given move? These are important distinctions that bear directly on preferences and learning styles, and each perceptual model handles them in different ways.
The interest among educators in perceptual models and sensory channels probably originated in the 1980’s with the neurolinguistic programming (NLP) movement, which is primarily a therapeutic model, and with accelerated learning models. Much successful work has been done on the basis of perceptual learning styles in athletic coaching, in communication skills training, and in sales training. These successes are worth noting, not so much for their direct relevance to e-Learning design, as for the fact that perceptual models and styles may deserve more credibility than many designers with technical backgrounds are willing to give them. There is a great deal more to this than can be addressed in this article, and I highly recommend that designers do some background reading and research on the Web, using Google.
There are several learning style models based, either partly or completely, on studies of perception but the most often cited is frequently referred to as the “VAK” model.
VAK
VAK stands for Visual (seeing), Auditory (hearing), and Kinesthetic (touch and movement). According to this model, one of these senses will tend to dominate the way a learner takes in and processes information, and the way in which that information is represented when stored in memory, with the other senses serving in auxiliary capacities. It is also possible that an individual may use different dominant senses and different combinations of senses for different learning tasks.
In general, instruction, including that delivered by e-Learning, should attempt to present information in all three sensory modes as much of the time as possible. This would give the visual learner something to look at, the auditory learner something to listen to, and the kinesthetic learner something to do. Ideally, a designer could attempt to provide some of the content in visual form (including written materials as well as graphics or photos), in audio form, and in a way that would require use of the kinesthetic mode, and give the learner an opportunity to choose the one he or she is most comfortable with. If it is possible to identify the learner’s preferences and provide that information to the system, an e-Learning application could be developed that would automatically provide information primarily through the learner’s dominant channel.
One challenge with VAK is that it is sometimes misunderstood or misapplied in a way that rigidly categorizes learners. It is important to provide information and instruction to learners in more than one mode, and to do it in a way that does not overwhelm their working memory.
Other models
In addition to the experiential and perceptual models, there are some learning styles approaches that have been developed for particular purposes, and that may combine elements of both experiential and perceptual learning styles.
One such learning style system is the Index of Learning Styles, or ILS, developed by Richard Felder and Linda Silverman in 1988. This model is designed specifically to address the learning styles of engineering students, as measured along dimensions thought to be important to education in that field. If this is relevant to your work, Dr. Felder has assembled a complete set of reference materials online at http://www.ncsu.edu/felder-public/ILSpage.html. Individuals may access the online ILS instrument there as well.
No matter which of the models a designer chooses, a point made earlier is worth repeating here. Most people have strengths in two or more of the styles of any model. Thus it is useful to help learners understand their style, coach them in effective ways to use the other styles appropriately, and build your e-Learning applications with particular activities that are compatible with each of the individual styles.
Earlier, I said, “To the extent that instruction is based on a sound strategy and design, and to the extent that the delivery of that instruction matches individual learning styles, a learner will be more successful in achieving the goals of the instruction.” Let’s go back now and pick up the question of sound strategy and design, as it relates to individual learning styles.
From strategy to style
In a paper published on the Web, M. David Merrill at Utah State University has this to say about the relative importance of learning style in determining an appropriate instructional strategy for a given instructional goal:
“There are known instructional strategies. The acquisition of different types of knowledge and skill require different conditions for learning (Gagné, 1985). If an instructional experience or environment does not include the instructional strategies required for the acquisition of the desired knowledge or skill, then effective, efficient, and appealing learning of the desired outcome will not occur.”
He goes on: “An examination of much of the available training material demonstrates that much of our current training materials include instructional strategies that are inconsistent with the goals of the instruction. Inconsistent instruction is ineffective instruction regardless of learner style.” (“Instructional Strategies and Learning Styles: Which takes Precedence?” Found May 1, 2004 at http://www.id2.usu.edu/Papers/5LearningStyles.PDF) (Editor's Note: As of February 22, 2010, this article appears to have been removed from the Web.)
In a previous article (“Storyboards Tailored to You: Do-It-Yourself Magic Arrows,” in Learning Solutions Magazine , May 3, 2004), I outlined the first steps of the instructional design and development process (See Figure 4).
FIGURE 4 The instructional design process leading up to development of content and materials includes making an accommodation for learning styles while planning the learning progression and laying out the flowchart.
Making provisions to adjust the delivery of instruction to the learner’s style is an important element in the last steps of design. Specifically, planning the provisions takes place during development of the learning progression and the flowchart. There are four steps involved in connecting an e-Learning design to learner style, beginning with the large instructional goal or content to be delivered through e-Learning. (See Figure 5.) Ruth Clark has identified many of the details of these steps, and I will summarize them very briefly here along with references to Dr. Clark’s work and the work of others.
FIGURE 5 Successful use of learning style information is the result of a progressive design process, with adaptation to learning style as the final step.
Content types
At the point where the performance approximation and the learning objectives are identified, it is possible to classify the type of content that is involved for each of the objectives, and the level of performance the learner will be required to demonstrate. Based on M. David Merrill’s taxonomy (The Content-Performance Matrix), Ruth Clark characterizes the basic content types in her book Developing Technical Training. These are:
- Fact
- Concept
- Process
- Procedure
- Principle
Each of these content types (except Fact) can be taught to either the “Remember” level (the learner simply recalls or recognizes the content exactly as it was presented) or the “Application” level (the learner applies the information the way it will be used on the job). Fact can only be taught to the “Remember” level.
Identifying the nature of the content, and the level to which the learner must perform, is the key to identifying the most appropriate instructional strategy.
Instructional strategy
There is, for each content type at each level of performance, an instructional strategy that researchers have found works best. The best strategy depends on the content or goal, not on the learner’s style or preferred mode of learning. Clark provides detailed outlines and the fundamental components of these strategies as they can be applied to reference materials, to classroom instruction, and to e-Learning.
Some of the strategies are very simple and direct; for example, the strategy for teaching a procedure is to get the learner to the application level quickly. Provide follow-along demonstrations and exercises that require the learner to actually perform the procedure. On the other hand, the strategies for teaching processes and principles are more involved and require the learner to solve a problem and make an inference.
Instructional architecture
Depending on the content type, and somewhat on the instructional strategy, the designer has a choice of instructional architectures, or instructional styles that will affect the e-Learning design. It is at this point that design begins to strongly interact with learning style.
Clark identifies four basic architectures:
- Receptive: The goal is information acquisition — to inform the learner. The learner gets lots of information, and not much of an opportunity to practice.
- Directive: The goal is to build and strengthen the learner’s response — to teach a procedure, usually. The learner responds to a tutorial, receives feedback, and repeats the cycle until the response meets the criterion the designer has specified.
- Guided discovery: The goal is to help the learner apply principles. The learner receives realistic problems and resources and solves the problems.
- Exploratory: The goal is to help the learner develop expertise. The learners have access to information, examples, demonstrations and exercises and can select the resources that best match their needs and models.
It is easy to see how the various learning styles will match some of these architectures more readily than they will match others. For example, the receptive and directive architectures might be more comfortable for learners with a converging style or those who would fall into the activist or pragmatist categories. On the other hand, assimilators and theorists would probably feel right at home with the level of detail provided in an exploratory architecture.
Does this mean that an activist has no hope of developing expertise through exploratory learning? No, but learners with styles mismatched to the architecture will need additional support if the skill being taught requires a particular architecture.
Learning model
e-Learning’s appeal, if not its effectiveness, is based in large part on flexible multimedia delivery of information. In e-Learning and the Science of Instruction, Ruth Clark and Richard Mayer point out the differences between the information delivery theory of multimedia and the cognitive theory of multimedia.
Instructional designers who adopt the VAK learning styles model often express the opinion that, in order to accommodate both visual and auditory learners, words should always be delivered in both spoken and printed form. This may be the result of a belief by the designers that learning happens because the learner receives information. Therefore, delivery of information through multiple routes is preferable to delivery through only one. This is the information delivery theory.
Clark and Mayer are proponents of the cognitive theory of multimedia. Cognitive processes — how memory works — affect learning. Concurrent cognitive activity, created by multiple streams of information, can easily overload working memory. For example, presenting animation on screen with text — all visual information — in a fast presentation is very likely to overload the working memory of learners who are not familiar with the content. This is a consequence of what Clark and Mayer call the Redundancy Principle, which Ruth Clark presented in “Six Principles of Effective e-Learning: What Works and Why” in the September 10, 2002 issue of Learning Solutions Magazine, along with information about exceptions to the Redundancy Principle.
There are other possible interactions between the VAK learning styles model and the cognitive theory of multimedia. Designers should be aware of these and take the cognitive theory into account. The research support for the cognitive theory and for the principles Clark and Mayer present is stronger than the research support for VAK learning styles, so careful consideration of the interactions would be a good idea.
Connecting to style
At this point, the design information for an e-Learning application will include:
- The instructional strategy and the fundamental components required by the content type;
- The instructional architecture, including decisions about the amount of guidance to be given for each of the objectives, the size of the steps in the design, and the amount of practice and feedback;
- The interactions between the learning style model chosen and the cognitive principles which apply to use of multimedia.
The next step for the designer is to decide how to implement an adaptive strategy that will adjust the delivery of the presentation components and options, and that will fine-tune the instructional style to be compatible with each individual learner. This sounds like a tall order, but it is a problem that designers have solved many times in the past. As long ago as 1988, David Jonassen published Instructional Designs for Microcomputer Courseware, in which he outlined adaptive strategies to adjust the difficulty of sample problems. Even though this work is dated in language and in some of the details, the adaptive strategies themselves are still valid and applicable to adjusting e-Learning to accommodate learning styles. Jonassen and Barbara L. Grabowski have also provided useful design guides for accommodating individual differences in a newer book, Handbook of Individual Differences, Learning, and Instruction. Both of these are available online through Amazon and other booksellers, and I recommend them to designers whether learning styles are a concern or not.
To allow an e-Learning application to adapt to an individual’s learning style, there are several options for the designer to consider. The designer can vary:
- The content. Some learners may skip or augment some topics, based on learning style. For example, activists may take a route around the theoretical content.
- The sequence. Some learning styles benefit from seeing the big picture first, while others need to move from the specific instances to a larger theory. There is no reason why everyone must take the same sequence.
- The amount of detail. Some learning styles require more detail, more examples, or more explanation than others in order to be effective. There is no reason why any learner should feel either overwhelmed by detail or under informed by the lack of examples.
- The instructional methods. Some learning styles will get more out of simulations than others. Other styles will learn more from case studies or from scenarios. By assessing learning styles and making the necessary adjustments in what each learner experiences, the use of different methods can be balanced.
Control of these options can be provided exclusively by the e-Learning application based on the designer’s decisions, exclusively by the learner’s choice, or through a combination of the two. Ruth Clark, in Developing Technical Training, suggests the third option, which she calls adaptive control: the options are managed by the computer, based on learner need. Learner need is established by the pattern of learner responses to questions or problems. The computer judges the responses and changes content, sequence, detail, or method — or offers the learner feedback, advice, and a choice.
Clark suggests that the designer consider some additional factors at this point. Consider what the learners already know. If they have a lot of experience, or if they will find it easy to learn the content or skill, give the learners more control over the content, sequence, level of detail, or the method. If the learners are less experienced, or if they will have trouble with the content, let the e-Learning application make the choices. Also consider whether the lessons depend on each other. If skills really need to be learned in a certain order, do not offer the option to change the sequence or to skip skills. Finally, if the learner will have to pass a criterion test (“go/no-go,” certification, pass/fail), give the learner the option for additional practice or alternative presentations, along with some advice from the program.
Conclusion
There are a number of issues relating to learning styles and e-Learning:
- Most of the learning styles studies done to date have involved traditional instructor-led delivery or distance learning based on written materials. There are few studies available to help guide the designer in applying learning style theory to e-Learning. As I have suggested, designers would do well to track what they have tried and how well it worked. It would also be a good idea to constantly read, look for newly-published research, and attend conference presentations, looking to benefit from the experience of others in applying learning styles to e-Learning.
- The validated versions of all the learning styles instruments are proprietary, and require payment of royalties or peruse fees every time a learner completes an instrument.
- The validated versions of the learning styles instruments are either print-only or available online only from the instrument owners; this means that actually getting the style information into a form that would be useful to an e-Learning application will most likely require human intervention — the information will not be available automatically to your LCMS or LMS. The web sites for the LSI and for the LSQ do not address the possibility of negotiating a license that would permit placing the online version onto your own server.
- There are many non-proprietary learning styles instruments available on the Web, but none of them have been validated or shown to be reliable, and many of them provide no information about the learning models on which they are based; this means you could have problems if you based adjustments to your e-Learning presentation on them.
Applying learning styles to e-Learning involves adding development time and cost. A purist might say that it is always worthwhile to do everything possible to maximize e-Learning effectiveness. Unfortunately, purists are almost never the ones writing the checks. A designer will always have to consider the tradeoffs and optimize for value.
It seems to me that a designer who would like to take learning styles into account has three basic options. These options are not necessarily mutually exclusive — more than one may apply to any given e-Learning program.
- Design the e-Learning application so that it applies the principles behind the Lewin model, taking the learner through all four stages of learning. Use short tests to check for progress at the end of each stage or at the end of each cycle. Either programmatically repeat individual stages or the complete cycle, or offer the learner the option to repeat a stage (e.g., to choose to repeat a simulation, to see the examples again or to see more examples, to review the conclusions the learner reached, or to repeat the practical application stage). This option would not require the explicit use of any learning style model or instrument, but the design could become quite complex.
- Have all members of the target population (or a statistically significant number of them) complete a learning style instrument and design the e-Learning application so that it matches the style of the majority. This could possibly be a solution if you suspect that your target population is going to be pretty homogeneous with respect to their learning style (all of them are members of a single profession, for example). There are at least two disadvantages with this approach. One is that there may be a significant minority of members who don’t “fit the pattern” and so will be mismatched. It could also be the case that the learning style instrument may turn out not to measure an important dimension (for example, if you used Kolb’s instrument but it turned out that most of your learners were strongly auditory in the way they like to receive information). Depending on the instrument and the license conditions, this could be an expensive option as well.
- Have those learners who are going to take the e-Learning complete a style instrument, and enter the results into a profile that would be available to the e-Learning application. The application would then adjust the presentation programmatically so that each learner received a presentation matched to his or her style. It would be a good idea to also offer learners some options that they control, since no instrument is going to be perfect in its assessment of style. Theoretically, this would provide the best possible match of presentation and learning style for each individual.
There are some practical issues. For example, getting all the learners to complete the instrument and report the results in a timely fashion could be a problem. There is also the question of what to do about the learners who don’t complete the instrument or report the results. In any case, getting the results entered into the profiles is a task that will need to be handled in the implementation planning. Finally, there may be some privacy issues — learning style may not be a hugely sensitive matter, but it is still someone’s personal information and it needs to be protected.
Because of the potentially negative interaction between sensory preferences and the need to avoid overloading the learner’s working memory, I recommend giving precedence to Clark and Mayer’s Modality, Redundancy, and Coherence Principles when applying any learning style model that involves visual, auditory, verbal, or kinesthetic adjustments to content.
Learning styles may be an important individual difference that will affect the results of your e-Learning applications. I have laid out some of what seem to me to be the leading theories in this area, and I have summarized some of the thinking of design experts that bears on use of these theories. I want to wish you well in applying these ideas to your designs, and to encourage you to write up your results and to share them with other practitioners. It’s really the way forward for all of us!
Reference
Clark, Ruth Colvin. Developing Technical Training: A Structured Approach for the Development of Classroom and Computer-Based Instructional Materials. Pearson Addison Wesley, 1989. ASIN 0201149672
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