In a recent blog post published by the New York Times, Anna North noted that the concept of “learning styles” is still prevalent among educators, even though there is little empirical research to support it. In fact, students preparing to be teachers are often taught about “learning styles” in their courses.

Why do we still speak of people (or ourselves) as having a “visual” or “auditory” (or “tactile” and so on) learning style? Let’s take a look at what the research literature has to say on “learning styles” and how they apply to instruction.

Overview of Learning Styles

When we take a look at the research, we find that the term learning style has varying definitions (see, for example, Wang, Wang, Wang, & Huang, 2006; Franzoni & Assar, 2009; Kinshuk, Liu, & Graf, 2009; Wu & Alrabah, 2009). For Cuthbert (2005), a learning style is an individual’s preference for understanding experience and transforming it into knowledge. In a similar vein, Slack and Norwich (2007) state that a learning style describes how someone usually approaches learning. Zacharis (2011) describes learning styles as the ways in which learners “perceive, process and conceptualise information” (p. 790).

As a general statement, then, we can say that a learning style is a set of dispositions or preferred methods of learning. In other words, let’s replace “learning style” with “learning preference.” Especially for designers of instruction for adults, this is an important distinction to keep in mind. As we discussed in an earlier article, adults learn for specific reasons, and they bring unique talents and abilities to the table. They also bring their own preferred methods of learning.

So, how do we figure out our (or our learners’) learning preferences? Just as the notion of “learning style” has no single definition, there is no single instrument used to measure it (Miller, 2005). As a matter of fact, in a comprehensive review of learning style inventories, Coffield, Moseley, Hall, and Eccleston (2004) examined a whopping 71 learning style models and provided detailed analysis of 13 major models.

Several models have been offered to define and explain learners’ preferences for learning (Chang, Kao, Chu, and Chiu, 2009). Let’s look at just a few.

Kolb’s Learning Style Inventory

Kolb’s (1981) Learning Style Inventory (LSI) is a nine-item questionnaire in which respondents describe their own learning behaviors. The LSI measures the individual’s learning behaviors against four learning abilities:

• Concrete Experience (feeling)
• Reflective Observation (watching)
• Abstract Conceptualization (thinking)
• Active Experimentation (doing)

The LSI also provides two scores that indicate the extent to which the individual emphasizes abstractness over concreteness and the extent to which the individual emphasizes action over reflection (Kolb, 1981).

Vermunt’s Inventory of Learning Styles

Vermunt’s (1994) Inventory of Learning Styles (ILS) uses questions to elicit information on four learning components: cognitive processing strategies, metecognitive regulation strategies, conceptions of learning, and learning orientations (Vermunt, 2005). Based on these responses, learners are placed into four “learning styles”:

• Meaning-Directed (personal interest, self-regulation of learning, critical thinking; learning is directed at finding personal meaning)
• Reproduction-Directed (memorization of facts and figures, external [i.e., instructor-driven] regulation of learning, working toward credentials such as certificates or degrees)
• Undirected (not very regulated, passive, ambivalent toward learning)
• Application-Directed (knowledge gained must be useful and applicable to a specific job)

Gardner’s Theory of Multiple Intelligences

Some researchers have used Howard Gardner’s (1993, 2004) theory of multiple intelligences as a complement to the idea of “learning styles.” Multiple Intelligence (MI) theory is an attempt to expand the traditional notion of intelligence (as measured by IQ tests) to include additional abilities. While IQ tests remove individuals from their natural learning environments and have them do unfamiliar, isolated tasks, Gardner suggested that intelligence is more closely related to the capacity for “solving problems and…fashioning products in a context-rich and naturalistic setting” (Armstrong, 2000, p. 1).

MI theory groups abilities into eight categories or “intelligences”: Linguistic, Logical-Mathematical, Spatial, Bodily-Kinesthetic, Musical, Interpersonal, Intrapersonal, and Naturalistic. A ninth intelligence, Existential (the ability to deal with large, universal questions of meaning) has also been proposed (Armstrong, 2000; Gardner, 2004).

It’s important to note that each person does not have only one of these intelligences. Rather, as Thomas Armstrong (2000) points out, everyone possesses all eight of them, and the intelligences work together in complex ways. Armstrong writes that most people are “highly developed in some intelligences, modestly developed in others, and relatively underdeveloped in the rest” (Armstrong, 2000, p. 9). Further, as Matt Aborn (2006) notes, educators have used the theory to recommend that instructors be aware of the learning strengths and challenges of each of their students, and within each unit there should be at least one lesson that includes all of the intelligences.

OK, now what?

The question of whether learning preferences should play a role in the design of instruction is a topic of debate in the literature (Lu and Chiou, 2010). For example, in the view of some researchers, such as Goorha and Mohan (2010), it is crucial that educators understand student learning preferences and provide appropriate strategies to meet those preferences. Likewise, in a cross-cultural study of 250 Taiwanese and Kuwaiti freshman-level university students, Wu and Alrabah (2009) concluded that a greater awareness of student learning styles and multiple intelligences can help educators both develop teaching techniques that accommodate cultural differences, and help students expand their repertoires of learning behaviors.

If this is so, how can we as instructional designers use the research on learning preferences? Can it help us improve the learning we design?

In most cases, it wouldn’t be practical to administer an instrument like LSI to determine our learners’ preferences and tailor learning for them. The best way to accommodate a variety of learning preferences is to pay close attention to one of the basic steps in the ID process: Analysis.

Know your audience. Learn who they are, where they work, what their demographics are, what they do, what they need to learn to do. And with this solid foundation in place, develop a blend of strategies to suit a varied group of learners.

Example: Using Multiple Intelligences in Designing eLearning

We noted earlier that some educators recommend using all of Gardner’s multiple intelligences. Using all of them might not always be feasible, but try to use as many as possible. Here’s an example of how you could do that.

The linguistic intelligence is typically the one most accommodated in eLearning, which usually has a textual element. Using “closed captions” is not only valuable for hearing-impaired learners. All learners can read the audio narrative text, providing additional benefit for the linguistic intelligence. Next, by providing realistic simulations or virtual environments, you can accommodate many (if not all) of the other intelligences.

For example, suppose you’re designing a course to teach learners how to install a particular piece of equipment. You can address the spatial intelligence by allowing learners to “measure” the area in which the equipment will be installed and by providing schematics and other visual specifications for the equipment. To address the logical-mathematical intelligence, you can ask learners to calculate dimensions and determine space requirements. Accommodate the bodily-kinesthetic intelligence using a simulation for manipulating the tools required and practicing assembly of the components. You can engage the naturalistic intelligence by providing an activity in which learners gauge the environmental impact of the equipment (perhaps when waste water is discharged). To address the interpersonal intelligence, ask learners to reflect on the challenges of working with other personnel at the work site, as well as collaborating with fellow installation personnel.

References

Aborn, M. (2006). An intelligent use for belief. Education, 127(1), 83-85.

Armstrong, T. (2000). Multiple intelligences in the classroom. Alexandria, VA: Association for Supervision and Curriculum Development.

Coffield, F., Moseley, D., Hall, E., & Eccleston, K. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review. London: Learning and Skills Research Centre. Retrieved March 3, 2012, from https://crm.lsnlearning.org.uk/user/order.aspx?code=041543

Cuthbert, P. F. (2005). The student learning process: Learning styles or learning approaches? Teaching in Higher Education, 10(2), 235-249.

Franzoni, A. L., & Assar, S. (2009). Student learning styles adaptation method based on teaching strategies and electronic media. Educational Technology & Society, 12(4), 15-29.

Gardner, H. (1993). Frames of mind: The theory of multiple intelligences. New York: Basic Books.

Gardner, H. (2004). Audiences for the theory of multiple intelligences. Teachers College Record, 106(1), 212-220.

Goorha, P., & Mohan, V. (2010). Understanding learning preferences in the business school curriculum. Journal of Education for Business, 85(3), 145-152.

Kinshuk, Liu, T.-C., Graf, S. (2009). Coping with mismatched courses: Students’ behaviour and performance in courses mismatched to their learning styles. Educational Technology Research & Development, 57(6), 739-752.

Kolb, D. A. (1981). Experiential Learning Theory and the Learning Style Inventory: A reply to Freedman and Stumpf. The Academy of Management Review, 6(2), 289-296.

Lu, H.-P., & Chiou, M.-J. (2010). The impact of individual differences on e-learning satisfaction: A contingency approach. British Journal of Educational Technology, 41(2), 307-323.

Miller, L. M. (2005). Using learning styles to evaluate computer-based instruction. Computers in Human Behavior, 21(2), 287-306.

Slack, N., & Norwich, B. (2007). Evaluating the reliability and validity of a learning styles inventory: A classroom-based study. Educational Research, 49(1), 51-63.

Vermunt, J. D. (1994). Inventory of Learning Styles (ILS) in higher education. Tilburg: University of Tilburg.

Vermunt, J. D. (2005). Relations between student learning patterns and personal and contextual factors and academic performance. Higher Education, 49(3), 205-234.

Wang, K. H., Wang, T. H., Wang, W. L., & Huang, S. C. (2006). Learning styles and formative assessment strategy: Enhancing student achievement in Web-based learning. Journal of Computer Assisted Learning, 22(3), 207-217.

Wu, S.-H., & Alrabah, S. (2009). A cross-cultural study of Taiwanese and Kuwaiti EFL students’ learning styles and multiple intelligences. Innovations in Education and Teaching International, 46(4), 393-403.

Zacharis, N. Z. (2011). The effect of learning style on preference for web-based courses and learning outcomes. British Journal of Educational Technology, 42(5), 790-800.