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Learning styles in online distance courses – a review of the research

Page history last edited by Dominic Kauffman 7 years, 5 months ago

Learning styles in online distance courses – a review of the research

 

 

Introduction

 

What are learning styles?

 

Learning styles, cognitive styles and learning strategies are used to describe individual differences in how learners approach problem solving and decision-making tasks (Logan and Thomas, 2002).

They affect how an individual perceives, filters, and processes information, and at what rate this can be done (Moallem, 2007). They have been defined as: extending beyond merely cognitive styles, to less academic and more practical and educational purposes (Riding and Cheema, 1991); and going beyond “simple processing of information” to “the act of learning” itself (Logan and Thomas, 2002).

 

How do we measure/categorise learning styles?

 

Several different instruments to measure learning style have been created. Most tend to focus more on the cognitive aspects of learning (Curry and Adams, 1991:250).

According to Kolb's Learning Style Index (LSI), for example, learners rely on four learning strategies: Concrete Experience, Abstract Conceptualisation, Reflective Observation and Active Experimentation (Kolb, 1985).

Witkin measures field-dependence and independence, whereby field-independent learners are those who rely on internal clues to solve problems, and who are more autonomous, while field-dependent learners rely more on others and their external environment (De Ture, 2004:22)

Soloman and Felder (1999) use The Index of Learning Styles (ILS) to categorise learners into: active-reflective, sensing-intuitive, visual-verbal, and sequential-global scales.

 

Certain inventories, however, focus more on the social and environmental aspects of learning (Diaz and Cartnal, 1999). These include Grasha & Reichmann’s Student Learning Styles Scales (GRSLS), which plots learners on a set of three pairs of scales: competitive-collaborative; avoidant-participant; dependent-independent; depending on how they prefer to interact with their learning environment (Grasha, 1996).

Keirsey’s Character and Temperament instrument combines aspects of learning with temperament, classifying students according to two temperamental types – extroverts and introverts – and four learning style groups: sensation/perceiving (SP), sensation/judging (SJ), intuition/thinking (NT) and intuition/feeling (NF) (Neuhauser, 2002).

Curry’s Theoretical Model of Learning Style Components and Effects sets out to combine and correlate social and cognitive aspects by considering levels of task engagement, motivation and cognitive control to describe learning preference (Curry and Adams, 1991).

 

The Growth of Online Distance Courses

 

The number of people joining distance courses increased considerably in the 1990s (Terrell, 2005) and Diaz and Cartnal report that “more online courses will invariably be offered in the future” (1999:132). This remains the case, according to Goodyear et al: “Even allowing for some hyperbole, it is clear that online learning is growing fast” (2001:66). Offir et al claim that: “in recent years, widespread adoption of distance learning options has changed the landscape of the higher education sector” (2007:3).

 

 

Part I

 

Research on learning styles on First to Fourth Generation distance courses

 

Theories of learning and cognitive style have generated a lot of interest because of their potential influence on the effectiveness of teaching (Logan and Thomas, 2002:iii). Taylor divides distance courses into four generations (Taylor, 1995): the first, second, and third generations represent paper-based, audio, or telecourses, and research includes work by: Gee, 1990; Dille and Mazack, 1991; Diaz and Cartnal, 1999.

 

Overwhelmingly, the research on the first three generations of distance courses points to learning styles being of paramount importance for success or completion rates. Gee (1990) and Dille and Mazack (1991) found that students with more conceptual and independent learning styles were more successful on a distance course while dependent and experiential learners were more successful on a face-to-face course. Additionally, Dille and Mazack showed that successful distance students had higher locus of control (more self-directed). They speculated that the success of more abstract thinking and independent learners is due to the fact that this type of telecourse often leads to social isolation and requires greater reliance on independent learning.

 

Comparatively little research has been done on learning styles within highly interactive learning environments – Taylor's (1995) fourth generation of distance course – but this research does include work by: Neuhauser, 2002; De Ture, 2004; Moallem, 2007; Battalio, 2009. Offir et al (2007) claim that development in the field of educational technology has outpaced research on distance learning and learning styles. They point out that most of the research on learning style has been done in ‘totally asynchronous setting and in secondary education’ (ibid:5). As a result, the literature does not present a clear picture on the importance of learning styles in online distance education.

 

Fourth generation online environments are almost entirely computer-mediated, so it is possible that learning success in an online distance environment could relate to how well learners perform in computer-mediated tasks. Kerka (1998) found that field-independents are more efficient in search-and-navigation tasks, while field-dependents are more likely to feel lost and disoriented in computer-mediated or hypermedia environments. Ogle found that field-independents performed better on recall tasks presented in a virtual environment (2002, in De Ture, 2004:23).

 

Neuhauser (2002) found significant differences in learning preferences for on-campus students and their online counterparts. However, her research showed that there was no correlation between learning styles and success rate (obtaining grades A–C) when comparing an asynchronous online course and its on-campus equivalent. She also found no difference in course attrition rates (84% in both).

 

Aragon et al (2002), using Curry's theoretical framework, found differences in task engagement levels and cognitive engagement levels between on-campus and online learners. However, those differences did not impact on final grades. They concluded that students can be as successful in online environments as on campus, provided that online courses are developed “using adult learning theory and principles as well as sound instructional design guidelines appropriate to the content and level of instruction” (ibid:243).

 

De Ture (2004) investigated field-dependence/independence and technological self-efficacy, and their effect on student success in a web-based distance course (Blackboard). He found that although field-independent students had higher technological self-efficacy scores than field-dependent ones, the field-independents did not receive higher final grades than field-dependents. He concluded that field-dependence or independence, and level of technological self-efficacy are poor predictors of success in an online course.

 

In contrast to previous studies, both Offir et al (2007) and Battalio (2009) found significant correlation between learning styles and success rate in a distance online environment. Investigating videoconference–based distance courses, Offir found that introverts were much more successful than extroverts. Battalio studied possible correlation between learning styles and success rate in two versions of the same online distance course: collaborative and self-directed. Surprisingly, he found that reflective learners turned out to be most successful in both self-directed and collaborative learning modes, although working alone is a major characteristic of a reflective learning style. He speculated that reflective learners were able to adapt well to either context, and thus be the most successful online learners.

 

Why is there such inconsistency?

 

The research listed above presents inconclusive results. There are different reasons why this may be so. Firstly, distance courses differ significantly in their mode of delivery (videoconferencing, web-based) and instructional design. Some learning environments might favour a certain style of learning, while others might be flexible enough to accommodate different learning styles.

 

Secondly, some research is probably of limited application, owing to the small samples of students involved, and in some cases a lack of control over certain demographic variables such as age, prior educational experience etc. It may also fail to take account of other important factors that could affect learning outcomes, but have little or no connection with learning style: student motivation; student perception of problems – or barriers – to completion.

 

The next section looks at these reasons in more detail.

 

Modes of delivery and transactional distance

 

In an early attempt to develop a theoretical framework to study distance education, Moore (1973) identified key elements that constituted this mode of learning: dialogue, structure and learner autonomy. He proposed the term transactional distance to describe the relationship between those elements. The transactional distance decreases when dialogue increases (student-instructor or student-student interactions) and when structure is less rigid (allowing for more personalised and self-directed modes of study). According to Moore and Kearsley (1996), the transactional distance must be overcome by learners, teachers and educational organisations, if successful learning is to occur (cf Chen and Willits, 1998). The characterisation of distance education according to its transactional distance gave rise to a hypothesis that autonomous learners would be more successful in a distance mode of study. He described an autonomous learner as one that planned and organised their learning; enjoyed questioning, analysing and testing; formed generalisations and looked for principles; and enjoyed working alone and collaboratively. This seems to match the characteristics of Witkin's field-independent student (De Ture, 2004).

 

Some of the contradictions regarding learning styles and success rate in research can be explained through differences in delivery mode of the distance courses and therefore the different transactional distances that students had to overcome. According to Moore's hypothesis the greater the transactional distance on a course, the more learning styles matter to its success rate.

It could be safely assumed that older types of distance learning environments had a higher transactional distance due to limited way that students were able to interact with instructors and other students and rigid structure of the course. In those environments certain types of learning styles would be more successful.

 

Dille & Mezack (1991) investigated the importance of learning styles on a success rate on a telecourse. From their description this course, consisting of pre-recorded video lessons, appears to have had a rather rigid structure and was not individualised. Additionally, interaction between instructor and students is not mentioned, suggesting that it was very limited and not a significant part of the course design. Accordingly, they reported that students who scored low on CE (concrete experience), and who were more self-directed, were more successful on the course. Similarly, Gee (1990) reports that students with more conceptual and independent learning styles were more successful on a distance course; while dependent and experiential learners were more successful on a face-to-face course. They speculated that the greater success of more abstract thinking and independent learning is due to the fact that this type of telecourse often leads to social isolation and so relies more heavily on independent learning.

 

On the other hand Neuhauser (2002) and De Ture (2004) both investigated a web-based distance environment, to find that learning styles had no influence on success rate on students. Modern online courses have usually much smaller transactional distance: the emergence of a variety of synchronous and asynchronous communication tools (Facebook, Skype, Canvas, wikis) allows for easier and more intense student-student and student-instructor communication.

 

Moallem's research (Moallem, 2007) appears to demonstrate that the quality of interaction can help students overcome the apparent mismatches between their learning styles and the task types. She suggests that factors such as student level of engagement in the learning process; level of student-student and student-instructor interactivity; and student perception of social presence (the social and human qualities of online learning), may play a more significant role in ensuring the success of online learning than learning styles and preferences that match instructional design.

 

Motivation

 

In a longitudinal study, Terrell (2005) found that none of age, gender, minority status, or learning style affected the attrition rate on a doctoral distance course. However, students in this study scored very highly on a locus of control suggesting high intrinsic motivation. Terrell speculated that such motivation might effectively compensate for any disadvantage suffered due to any unsuitability of preferred learning style to distance study.

 

Perception of barriers to course completion

 

Gibson and Graff (1992) investigated individual students’ perceptions of situational, institutional, and dispositional barriers to completing a distance course. They examined the completion rates of certain BA degree distance courses between 1983 and 1986. According to the research, only a “small amount of variance in barriers” can be explained in terms of learning styles (ibid). Non-completers often felt isolated, or that they hadn't had enough guidance and support, and struggled with the self-directed mode of study and lack of face-to-face contact, suggesting that learning style was of some importance to completion of a course. However, the most statistically significant barriers were, for example: level of motivation to complete, level of education at time of enrolment, years of college prior to enrolment, and years since last college credit course.

 

Some researchers have also found that students select distance courses (online and other) for personal or professional reasons, rather than for any pedagogical factors, including preferred learning style (Battalio, 2009; Mupinga et al, 2006).

 

Differences that some researchers perceive between distance and face-to-face students are becoming less pronounced (Latanich et al, 2001), and any proposed distance student “profile” might be eroded by the sheer number and variety of students enrolling, and the fact that their learning style may have little do with the decision to do so. The continuing enthusiasm for online distance learning inherently increases the potential variety and complexity of learning styles that course providers will face. If distance course providers are to maximise learning success for all their prospective students, they perhaps need to assume a wide variety of learning styles will be present, and apply their knowledge of these to the courses themselves as instructional design (Baldwin and Sabry, 2003).

 

 

PART II

 

How should course designers use knowledge of learning styles to ensure more effective learning for online distance students?

 

The research cited in Part I correlating learning style with online distance course success is inconclusive. Some research suggests that all learning styles can be successful, and that other factors – motivation and student perception of barriers to completion – may be at least as important. Maybe learning styles are a poor predictor of success rate of online distance courses (De Ture, 2004). But they could be used to improve instructional design, and the effectiveness of the learning environment.

 

Two broad paths seem to emerge from the literature on ensuring effective online learning while working with diverse learning styles and teaching methods. The first is based on the principle of matching teaching style with learning style, the idea being that such a match ensures the best chance of learning success (Pask and Scott, 1972). For this to occur, several researchers suggest designing distance courses to be able to accommodate a wide variety of learning styles, so that all students will be served as equally as possible by the course.

 

The second path is based the aim of developing the students themselves: by designing courses to expose students to both preferred and non-preferred teaching styles, in order to encourage them to adopt methods they might not normally favour, and in doing this, to develop their general learning capacity and capability to use whatever learning style was most appropriate. Grasha termed this mismatching (Grasha, 1996:172).

 

First path: adapt teaching methods to fit student learning style

 

In distance courses where the teacher is not always present, pedagogical materials tend to be presented more uniformly than in face-to-face courses, as the opportunity to optimise material for students may be far less. One option, then, is to increase the number the delivery methods used in online courses, to the point where we can accomodate as many learning styles as possible, in order to be able to offer a student with any learning style a pedagogically appropriate teaching style (Batalio 2009).

 

Ford and Chen (2001) presented students, measured as field-independent, dependent, or intermediate, with a version of a web design course that differed in the order in which information was presented. Matching cognitive learning style with the presentation order that was supposed to suit that style resulted in significant benefits for learning outcomes, especially for men, it seemed. Moallem reports that a great deal of research suggests that: “learners whose learning styles match with the given teaching or instructional style tend to retain information longer, apply it more effectively, and retain more positive attitudes toward the subject of the course than those who experienced clashes in teaching/learning styles” (2007:217). Gunawardena and Boverie (1993) also found that it greatly increased student satisfaction with course. Yet Moallem indicates caution: “there still remains much discussion about...whether it is more effective to match or mismatch learning style with instructional style” (ibid:218).

 

Logan and Thomas noted that, as learners could have either collaborative or independent learning tendencies, so online courses should offer collaborative activity (such as fora) and more opportunities to work alone, in order to meet all needs more equally (Logan and Thomas, 2002). Offir et al describe a need to understand student-related variables such as learning styles, in order to get away from ”adopting a “one-size-fits-all” approach to designing DL environments” to “ meet the diverse needs of different students instead” (Offir et al, 2007:4). Critically, their ideas relate to the different ways in which students perceive phenomena as problems, e.g. they found that extroverts' motivation was affected by being unable to speak spontaneously with the lecturer during videoconferences, because of the need to listen; while introverts' motivation was affected by the tension resulting from having to listen intensively to hear all the lecturer's words. The same situation was perceived in terms of a different problem, depending on the student's learning style. They proposed to “compensate” each type of learner: extroverts by carefully balancing the videoconferencing with collaborative group or pairwork, etc; and introverts by offering learning strategies to overcome the reported anxiety (ibid:17).

This links with Baldwin and Sabry, who noted that the way information is presented is significant to engage learners in appropriate cognitive processing to promote effective learning (2003:327).

 

Second path: adapt teaching methods to modify learning style

 

Grasha (1972) claimed that students often change their learning styles in response to the demands of a course, a particular teaching method, or an assignment type. Learning style was not immutable, but was actually adaptive to classroom procedures such as choice of teaching methods and changing environmental factors (1996:171).

 

He suggested a different path to increasing learning success, by designing courses to intentionally expose students to methods of learning that they did not prefer. The result would be a more flexible, prepared student, familiar with a wider variety of teaching styles and able to draw on different learning styles as necessary. It would also remedy an overemphasis on matching student and teacher styles, which: “up to a point provides satisfaction for both parties. Unfortunately, when carried to an extreme, matching styles can led to boredom and satisfaction with the status quo” (ibid:151).

 

The idea that cognitive and social learning styles are changeable, not static, seems to be supported by research in both traditional and online learning environments. Hofstede (1986) believes that the environment in which we interact determines our cognitive development.

 

Moallem (2007) suggests that in a learning environment where social interaction, collaboration and problem-solving are highly emphasised, students' perception of the quality of learning experience increases their motivation and willingness to adjust their learning styles.

 

Battalio (2009) held that online presence may alter learner personality. Palloff and Pratt (2007) also suggested that introverts may be able to establish social presence more easily online and thus become more extroverted.

 

Grasha went on to describe ways in course providers could design to develop their students. One method was to use “creative mismatches” to push students into using non-favoured learning techniques in a non-threatening environment to develop them (1996; 1996b). Diaz and Cartnal supported this: “Designing collaborative assignments for independent learners, or independent assignments for dependent or collaborative learners, is appropriate and even necessary” (1999:135).

 

It is still not clear to what extent learning styles can truly be changed, and therefore, how valuable intentional and structured mismatching of learning and teaching styles is, in course design. Patterson (2001) mounted a research project designed to increase the Independent learning style of nine freshman-level students. The results showed little increase, but perhaps surprisingly showed a decrease in Collaborative and Competitive styles. This was a small-scale piece of research and seems to call for a lot more work in this area.

 

Final critique of the research

 

Curry identified reoccurring problems with research into effects of learning styles on educational outcomes:

(1) confusion in definitions;

(2) weakness in reliability and validity of measurement;

(3) identification of the most style-relevant characteristics in learners and instructional

settings (Curry, 1991:248)

 

The terms, such as learning styles, cognitive styles and thinking styles are often used interchangeably in the literature, and “a bewildering confusion of definitions” has been created (ibid). There exists a myriad of learning style inventories that measure different aspects: social, cognitive or environmental. These instruments often seem to be applied without considering how relevant those aspects are to the type of course or institutional setting (ibid). Additionally, results from different learning style inventories are rarely compared within one population (ibid). Some research fails to take other variables into account, such as: demographics, type of course, learners’ prior knowledge, etc. that might distort the outcomes (Neuhauser, 2002:100). All these issues have considerable consequences for the validity and applicability of the research we have into: how learning styles affect online distance course performance; to what extent these learning styles can or should be changed; and how important learning styles really are to student success.

 

 

Word count: 3495

Contributors: Maja Balcerzyk and Dominic Kauffman

 

 

References

 

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