By Isac Artzi, PhD
Faculty, College of Science, Engineering and Technology
There are many challenges in the process of designing online instruction that address a students’ unique and diverse learning styles. A main challenge is that learning styles are assumed to be static in most, if not all, assessment tests.
The reason this is a problem is that a one-time learning style assessment does not take into account situational, psychological and educational changes over time. The second part is that learning management systems (LMS) present content without being mindful of individual learning preferences. For example, visual students need more visual content and textual students need the content to be presented as text. When LMS content presentation is based on a one-time learning style assessment, or worse, on the assumption that all learners should receive the same presentation media, this is problematic.
Do Learning Styles Change Over Time?
There is a dissonance between the prevalent notion that learning styles can be assessed once and that all content media types are suitable for all learners, and the notion that learning styles are dynamic and online learners are better served by personalized learning experiences.
Learning styles of online learners may change over time (D. A. Smith & Kolb, 1986) and may be influenced by many external situational and environmental factors (V. L. Reese & Dunn, 2007) like subject matter, time of day, mental abilities, etc.
Presently, there is no efficient algorithm for delivering online educational content that continuously adapts the media types of content to an online learner’s specific and dynamic set of learning styles. In traditional classrooms, in contrast to online ones, one expects (and often finds) a high level of communication between students and instructors – rich in visual, auditory and kinesthetic interaction. This communication occurs during regular class time or other in-person meetings.
Conversely, students engaged in e-learning are deprived by the diversity of communication venues available in traditional schools and are mostly dependent on written interaction. When video content is presented, it is assumed that all students learn equally well by watching a video. If 10 percent of the students prefer a text or interactive version of the content, they are not accommodated. Furthermore, if their preferences for one media type over another differ – or change over time – from one subject matter to another, these preferences are not taken into account.
How Do LMS’s Take Learner Needs into Account?
While various learning systems are discussed in relevant literature, even the adaptive systems are not designed to accommodate changing learning styles. Many such systems are described in MacLaren (2004), Mestre (2006), Moallem (2007), Moos and Azevedo (2009) and Papanikolaou and Grigoriadou (2009). These systems may classify learners in definitive terms as being one type or another, based on assessing learning characteristics at one snapshot in time (Assis, Danchak, and Polhemus, 2005).
For example, learners who are better speakers than writers may fare poorly in such environments. The dominant view of adaptive content focuses exclusively on the subject matter. Non-personalized interaction with online educational content deprives learners of the mode of communication in which they perform best. Some students may miss the opportunity to impress the instructor through positive behavior, verbal ability and instantaneous bursts of high-level cognitive performance. In an e-learning environment, some students may not even have the opportunity to demonstrate superior abilities in verbal communication, leading the instructor to wrongly assume they are similar to their (inferior) writing abilities. Such systems might not re-evaluate the learners and thus be unaware of their potentially changed learning style.
Most online learning environments are limited to simply being non-personalized content repositories (Teo and Gay, 2006). The instructional design community does not yet possess an efficient algorithm that would prescribe how to accommodate dynamic changes in learning styles of online learners or any learners (Rey-Lopez et al., 2008). Such an algorithm and the derived technology would provide instructional designers for online learning with the ability to design educational content and its presentation, in a way that the medium of delivery is continuously optimized and adapted for all types of learners. New technologies for audio and visual communication over the Internet present unprecedented opportunities for designing e-learning environments. At the present time, however, most environments rely on written communication.
How Do Students Learn Best?
Applicants for admission into academic programs are evaluated based on past performance, academic records, references, goal statements, availability of space in a program and other factors that assess one’s suitability for a particular field. One would therefore expect educational institutions to create a personalized program of study for each student. The prospective student should in turn be able to shop for the best match between an institution offering and a specific (custom) program of study.
In reality, the economy of scale suggested by online learning does not accommodate personalization. Furthermore, if learning styles and habits are dynamic, past performance in a traditional environment is not the best indicator of expected performance in an online one.
The absence of any means for accommodating individual learning styles and matching them with individual content and media tailored to individual needs is hindering many students from realizing their full educational achievement potential.
Currently a great deal of effort and research is conducted on methods for assessing individual learning styles and the categorization of learners based on their learning preferences. This creates an abundance of approaches and characterization schemes without providing conclusive insight into how students learn best.
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