Different Learning Styles – How to Design Learning to Accommodate our Learners?

People learn in different ways. We all have our individual preference for the ways in which we learn. In this post, we will explore different models to categorize learners into different learning styles. We will also explain how to design learning to accommodate our learners.

Understanding the different learning styles will influence the design and assessment strategies. That’s why it is important to know some models so we can cater for a wider group of learners.

What are learning styles?

There are numerous theories which discuss different learning styles. In the past few decades, educational theorists have expanded the work of psychologists in the area of individual learner differences.

As a result, a number of theories have emerged about how people can be classified into different groups based on how they learn more effectively and their preferences.

Generally most theorists describe learning styles as different aspects of the ways that people learn, process information and respond to environmental stimuli in different dimensions.

Some theorists and researchers have identified the following definitions of learning styles:

• The way that individuals attend to and integrate complex new information determines which style categorizes a learner (Dunn, 1978). Additionally, personal choice and preference play a role in what style one chooses for learning.
• Dunn (1978) indicated that learning styles are approaches to learning and studying.
• Keefe (1982) defined learning styles as characteristic cognitive, affective and psychological behaviors that serve as relatively stable indicators of how learners perceive, interact with or respond to the learning environment.
• Dunn and Perrin (1994) described learning styles as “the way in which each learner begins to concentrate on, process and retain new and difficult information. That interaction occurs differently for each individual” (p. 2).
• Gilbert and Han (1999) and Gilbert (2000) confirmed that learning preferences facilitate the way individuals learn when the learning environment considers the various learning styles of students, thereby impacting the comprehension of materials presented.
• Felder and Spurlin (2005) described learning styles as “characteristic strengths and preferences in the ways they take in and process information” (p. 1). Felder (2005) indicated that learning styles are often reflected in “different academic strengths, weaknesses, skills, and interests” (p. 3). Learning styles are often influenced by heredity, upbringing and current environmental demands. Individuals have a tendency to both perceive and process information differently.

Learning styles are not unique and fixed for each individual. They fluctuate within subject or lesson and each person will have a variety of learning styles and preferences.

There are many frameworks or models to classify learners into specific learning styles. In this article we will explore the following models:

• Multiple Intelligence Theory.
• Split Brain Theory (left-brain, right-brain).
• Global and Analytical Learner Model.

Multiple Intelligence Theory

The Theory of Multiple Intelligence was developed in 1983 by Howard Earl Gardner and it was first published in his book Frames Of Mind: The Theory Of Multiple Intelligence.

Its basis opposes the idea of a single dominant type of intelligence, and instead acknowledges the existence of multiple intelligence, that compose a unique blend in each individual.

The Multiple Intelligence Theory indicates that each type of human intelligence represents a different way of processing information. The different intelligence are:

• Verbal-linguistic: refers to an individual’s ability to analyse information and produce work that involves oral and written language, such as speeches, books and memos.
• Logical-mathematical: describes the ability to develop equations and proofs, make calculations and solve abstract problems.
• Visual-spatial: allows people to comprehend maps and other types of graphical information.
• Musical: enables individuals to produce and make meaning of different types of sound.
• Naturalistic: refers to the ability to identify and distinguish among different types of plants, animals and weather formations found in the natural world.
• Bodily-Kinaesthetic: entails using one’s own body to create products or solve problems.
• Interpersonal: reflects an ability to recognize and understand other people’s moods, desires, motivations and intentions.
• Intrapersonal: refers to people’s ability to recognize and assess those same characteristics within themselves.

Design Strategies for Multiple Intelligence Theory in eLearning

The following picture shows the different multiple intelligence and some strategies that can be used to implement them.

Another set of activities suitable for the different multiple intelligence is shown below:

Source: http://www.mmiweb.org.uk/downloads/mi.html

And finally, more design strategies are listed in the following table:

Split Brain Theory

In the 1960s, neuroscientist Roger W. Sperry discovered that by cutting the corpus callosum, a large bundle of fibres that connects the right and left hemispheres of the brain, he could reduce seizures in patients with epilepsy. The results of these experiments also revealed that the 2 sides of the brain performed different tasks.

Anatomically, the brain is divided into 2 hemispheres; the left and right. Studies have indicated that the 2 hemispheres have different functions and characteristics.

Based on these studies and the lateralization of the brain function, we have the theory of left-brain or right-brain. According to the theory of left-brain or right-brain dominance, each side of the brain controls different types of thinking. Additionally, people are said to prefer one type of thinking over the other.

According to the left-brain, right-brain dominance theory, the right side of the brain is best at expressive and creative tasks. Some of the abilities popularly associated with the right side of the brain include:

• Recognizing faces.
• Expressing emotions.
• Music.
• Reading emotions.
• Color.
• Imagination.
• Intuition.
• Creativity.

The left-side of the brain is considered to be adept at tasks that involve logic, language, and analytical thinking. The left-brain is described as being better at:

• Language.
• Logic.
• Critical thinking.
• Numbers.
• Reasoning.

Design Strategies for Split Brain Theory in eLearning

For learners with more tendency to the left hemisphere, some activities would include presenting information in numerical tables, with facts, figures, statistics, showing content in a logical order and sequences.

The assessments would require learners to organize facts, accurate responses involving recall of information and solutions of specific problems.

For right hemisphere learners, I would include activities that involve presenting images, asking for emotions, opinions and feelings in regards to the information presented rather than factual responses. I would include scenarios or case studies to let learners imagine possible responses and suggest different solutions, predict outcomes and apply solutions to hypothetical problems.

Every eLearning module would include activities for both styles.

Global and Analytical Learner Model

In the Global Learner Model, the learner takes in information holistically. Begins with understanding concepts first, with mastery of details to follow. On the other hand, in the Analytical Learner Model, the learner takes in information sequentially, step-by step, preferring to learn a series of facts that lead toward an understanding of a larger concept.

Global learners need to see the big picture first and know the end result before beginning. While the analytical learners like to learn one piece at a time; they enjoy a clear sequence that starts at the beginning and moves to the end one step at a time.

In my workplace, I would design and adapt the eLearning modules to cater both styles as follows:

• Include the learning objectives of the training.
• Present a list of what learners will achieve at the end of the course.
• Start with the basic concepts, one step at a time and then build up the complexity of the content as the learner progresses.

Identify your Own Learning Style

There are different tests and tools you can use to determine your learning style and preferences.

The DISC assessment is an inventory model of learning styles which is composed of four quadrants classified by behavior. DISC is an acronym for Dominance, Influence, Steadiness, and Conscientiousness.

Other tool I have used is called “HBDI – Herrmann Brain Dominance Instrument”. This instrument was developed by Ned Herrmann after investigating split-brain theory and using technologies such as Electroencephalography (EEG), Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI). HBDI allows you to become aware of your thinking preferences in order to use them better in your professional and personal life.

The HBDI test consists of 120 questions and the results and then categorized in four quadrants.

Other test you can do completely free is in this link https://personalitymax.com/

They will also provide with a snapshot on your preferences and your learning styles.

Designing eLearning for All Learning Styles

I believe it is possible in a post-modern world to provide instruction that will serve a wide range of learners without getting too tangled in the maze of learning style theories.

Basically, exposure to different instructional approaches is important. Incorporating a variety of teaching and assessment methods, being aware of cultural differences and providing students with opportunities to reflect on thinking will improve learning outcomes.

The type of instruction needed depends significantly on the discipline, the context and the content of what is being taught (Paschler, McDaniel, Rohrer, & Bjork 2008). A thorough analysis of these elements ensures that instruction will be aligned with expected outcomes.

A geometry course, for example, will likely have a much different presentation than will a writing course. Independently of the course, I would provide learners with plenty of opportunities to engage in critical thinking as a way to deconstruct and reconstruct the desired content in any discipline.

When a learner is exposed to different media in an instructional module, they will develop multiple schemas while working together for mastery of content.

Mayer’s Cognitive Theory of Multimedia Learning proposes that the best instruction includes information presented both visually and aurally, without redundancy or overload. We discussed these multimedia strategies in the post “Richard Mayer on Multimedia Learning”.

For digital environments, designers need to have a sundry toolbox to make the instruction clear and adaptable.

Approaches and Implementation Techniques for Developing Multiple Intelligence Content

The Challenge for Instructional Designers

An instructional designer would find challenging to determine which system of learning-style classification would be most appropriate for a particular project. There are so many style inventories that, in their own right, seem logical and suitable for dividing people into groups for instructional planning purposes.

However, no one model is going to fit or match the potential range of learners.

Instruction that is engaging, flexible and relevant to the intended audience, and that is presented in ways that encompass varied avenues of access is likely to meet the needs of diverse learners, no matter what their current or preferred style may be (Paschler, McDaniel, Rohrer, & Bjork 2008).

A person’s thinking about and attention to their individual learning preferences may be useful in developing a metacognitive awareness of how best to approach a particular learning goal, which is highly dependent on the discipline, the content and the context of the task.

But it is not useful to lock oneself into a learning style that limits participation in engaging learning experiences offered through a variety of well-designed instructional strategies.

It is important not to label a learner within a preferred style but to challenge them to try using different learning materials. Also encourage your students to use a range of activities in an eLearning environment to cater for all styles.

Learners stereotyped as having one particular learning style may find themselves locked into one kind of learning experience, discouraging experimentation with other learning approaches.

Rather, all learners will likely benefit, over time, by being exposed to and using a varied toolbox of cognitive strategies to access content knowledge.

In Summary

To face these challenges of multiple intelligence and learning styles I would suggest two practices.

Firstly, I would focus on promoting among the learners metacognition strategies for learning effectively such as elaboration, comprehension monitoring, and mnemonics.

And secondly, I support the idea of designing learning experiences that incorporate a variety of methods to cater for all learning styles and that promote the development of multiple intelligence.

When designing instructional systems I would keep in mind the use of technologies to support different learning strengths. For example, there is in the market software to stimulate verbal/linguistic intelligence, or calculus activities to develop logical/mathematical intelligence, logic puzzles and games, or drawings and artwork to promote visual/special intelligence.

Technology can be used in the classroom along with traditional activities to help students develop their intelligence (Bennett & Tipton, 1996).

In the online environment, on the other hand, as internet technologies become more user-friendly and accessible, instructional designers can create learning experiences that incorporate activities that appeal to the different intelligence and thus increasing learner responsiveness (Osciak & Milheim, 2001).

Each strategy in the learning experience will benefit learners in a different way, and learners will get out more or less from a particular strategy, but the important point to keep in mind is to include a variety of instructional methods and encouraging learners to use effective learning habits.

The key message at the end is “to offer variety and learners will be happy” 😀

I hope you liked this post. Feel free to leave your comments below and tell me what are your preferences and learning styles?

See you next time,

Thais 🙂

References

Bennett, C.K. & Tipton, P.E. (1996). Using technology to develop multiple intelligence in the classroom. In B. Robin et al. (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference 1996 (pp. 976-978). Chesapeake, VA: Association for the Advancement of Computing in Education (AACE).

Dunn, K. & Dunn, R. (1978). Teaching students through their individual learning styles: A practical approach. Reston, VA: Reston Publishing.

Dunn, R., Dunn, K., & Perrin, J. (1994). Teaching young children through their individual learning styles. Boston, MA: Allyn & Bacon, Inc.

Felder, R. M., & Spurlin, J. E. (2005). Applications, reliability and validity of the Index of Learning Styles. International Journal of Engineering Education, 21(1), 103-112.

Gilbert, J. E., & Han, C. Y. (1999). Adapting instruction in search of a significant difference. Journal of Network and Computing Applications, 22(3), 149-160.

Gilbert, J. E. (2000). Case based reasoning applied to instruction method selection for intelligent tutoring systems. Workshop Proceedings of ITS’ 2000: Fifth International Conference on Intelligent Tutoring Systems, Montreal, CA.11-15.

Keefe, J. (1982). Student learning styles and brain behavior: Programs, instrumentation, research. Reston, VA: National Association of Secondary School Principals.

Kelly, D. Brendan, T. (n.d.). A Framework for using multiple intelligence in an ITS.

Khalsa, G. (2013). Should Instructional Designers Accommodate Learning Styles?. In R. McBride & M. Searson (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference 2013 (pp. 1350-1355). Chesapeake, VA: Association for the Advancement of Computing in Education (AACE).

Osciak, S. & Milheim, W. D. (2001). Multiple intelligence and the design of web-based instruction. International Journal of Instructional Media (pp. 355-361). Retrieved from http://www.acdowd-designs.com/sfsu_860_11/MI and design of web-based instruction.pdf

Pappas, C. (2015). Multiple Intelligence in eLearning: The Theory and its impact. [Web]. Retrieved from https://elearningindustry.com/multiple-intelligence-in-elearning-the-theory-and-its-impact

Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Sciences in the Public Interest, 9(3), 103-119.

Share on Facebook

Tweet

Follow us