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Learning style preferences and theperceived usefulness of e-learningAlexander Toni Mohr a , Dirk Holtbrügge b & Nicola Berg ca Kent Business School , University of Kent , Canterbury , Kent ,UK , CT2 7PEb Department of International Management , University ofErlangen-Nürnberg , Lange Gasse 20, Nürnberg , 90403 , Germanyc Department of Strategic Management , University of Hamburg ,Von-Melle-Park 5, Hamburg , 20146 , GermanyPublished online: 12 Dec 2011.

To cite this article: Alexander Toni Mohr , Dirk Holtbrügge & Nicola Berg (2012) Learning stylepreferences and the perceived usefulness of e-learning, Teaching in Higher Education, 17:3,309-322, DOI: 10.1080/13562517.2011.640999

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Learning style preferences and the perceived usefulness of e-learning

Alexander Toni Mohra*, Dirk Holtbruggeb and Nicola Bergc

aKent Business School, University of Kent, Canterbury, Kent, CT2 7PE, UK; bDepartment ofInternational Management, University of Erlangen-Nurnberg, Lange Gasse 20, Nurnberg 90403,Germany; cDepartment of Strategic Management, University of Hamburg, Von-Melle-Park 5,Hamburg 20146, Germany

(Received 2 August 2010; final version received 12 September 2011)

This paper uses data gathered from 953 students to investigate in how farindividuals’ preferences for a particular learning style are associated with theperceived usefulness of e-learning. Our findings reveal the effect of individuals’learning styles as well as their gender and professional experience on the perceivedusefulness of different forms of e-learning. The study’s findings enhance ourunderstanding of the usefulness of different e-learning tools from a learnerperspective and thus have implications for curriculum design. The study alsocontributes to the empirical basis on the relevance of learning styles in the designof virtual learning environments.

Keywords: learning styles; e-learning; empirical study; virtual learning

1. Introduction

The rapid development and spread of information technology continues to create

new possibilities for the use of electronic learning in education. E-learning tools such

as video-recordings of lectures, computer-based trainings, tutor-guided online

discussions and virtual document sharing systems have become an integral part of

undergraduate as well as graduate programmes worldwide. Universities need to offer

such virtual teaching environments to meet students’ changed expectations regarding

the way they are taught. In particular, students belonging to the ‘Google Generation’

(Summers 2007) expect to be taught using new technologies given that they have

grown up with, and are therefore used to ‘accessing information on offer whenever

they want to, wherever they want and consuming it in bite-sized chunks’ (Summers

2007, 65). In a similar vein, Goffe and Sosin (2005, 279) reason that for ‘18-year-old

freshmen, personal computers have been around for their entire lifetimes, and the

Internet has existed for over half of their lifetime’.

Technological developments have not only been introduced to support tradi-

tional on-site education, but have also facilitated the introduction and spread of

distance learning programmes at home and overseas. According to data from the

UK Higher Education Statistics Agency (HESA), the number of distance learning

programmes offered by UK universities and the number of students enrolled in such

programmes have increased markedly (HESA 2009). The raised interest in the use

and the implementation of e-learning can be explained by the suggested benefits for

*Corresponding author. Email: a.t.mohr@kent.ac.uk

Teaching in Higher Education

Vol. 17, No. 3, June 2012, 309�322

ISSN 1356-2517 print/ISSN 1470-1294 online

# 2012 Taylor & Francis

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both students and teachers. These include the fact that e-learning can be used to

provide knowledge to an ‘audience in multiple locations and [. . .] over a very short

timescale’ (Bellinger 2007, 30). Yet, anecdotal evidence suggests that users’

satisfaction with e-learning hardly ever reaches the levels hoped for with its

introduction (Bellinger 2007; Westbrook 2006). One of the factors that might

explain the low satisfaction with e-learning among users is a mismatch between the

expectations of learners and what existing e-learning solutions deliver. The

importance of this alignment of delivery mode to individual expectations has been

highlighted in general research on technology acceptance and in relation to the use

of online education (Ong, Lai, and Wang 2004; Zapalska and Brozik 2006). Overall,

one important conclusion from prior research is the need of taking into account

learners’ expectations and preferences to make e-learning ‘learner-seductive, not just

supportive’ (Bellinger 2007, 27). Perceived usefulness, as a key predictor of the

acceptance of new technologies in general and of e-learning methods in particular

(Ong, Lai and Wang 2004), is thus crucial if e-learning methods are to improve

learning outcomes. Perceived usefulness refers to the degree to which an individual

believes that using a particular form of e-learning enhances his or her learning

achievement (Burns 2005). Yet, while acceptance has been highlighted as a

prerequisite for the successful use of e-learning ‘[f]ew studies have examined how

learner and situational characteristics affect technology-delivered instruction’ (Klein,

Noe, and Wang 2006, 667). Existing studies commonly report the perceptions of

students with little attention to the potential reasons behind any variations in their

acceptance, or, if such reasons are identified, they often relate to learners’ general

affinity and access to technology (see, e.g., Webster and Hackley 1997). Based on the

finding that e-learning methods need to be perceived as useful by learners if they are

to be accepted by them and improve their learning outcomes, we think more research

is needed into the antecedents of the perceived usefulness of e-learning methods.

Gregorc (1985) suggests that learning is negatively affected if students’ preferences

with regard to delivery method are not taken into account and that modes of

delivery should be consistent with learners’ learning styles. Although little attention

has been given to the potential of role learning style preferences in this context so far

(Zapalska and Brozik 2006), we suggest that they are an important factor in

understanding differences in the degree to which students consider different e-

learning methods to be useful in their learning. We aim to provide answers to the

question as to what role individuals’ learning style preferences play in explaining

variations in the perceived usefulness of different forms of e-learning. The paper

contributes to research by discussing Kolb’s (1984) learning style concept in the

context of e-learning. In addition, it provides empirical evidence for the degree to

which the use of e-learning is influenced by learning style preferences, taking into

account a number of control variables. The study thus addresses the fact that too

little attention has been given to exploring the new forms of pedagogy made possible

by e-learning (McConnell 2005, 26). The ensuing section discusses the learning style

concept developed by Kolb (1984). This concept will be used to relate different forms

of e-learning to different learning style preferences and to develop six research

hypotheses in Section 3. Section 4 presents our sample and measures. The findings

are presented and discussed in Section 5. The final section concludes the paper by

outlining its main contributions, implications and limitations.

310 A.T. Mohr et al.

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2. Theoretical framework: Kolb’s concept of learning style preferences

Kolb’s (1984) concept of learning style preferences has become one of the most

widely used learning concepts (Drew and Ottewill 1998; Nulty and Barrett 1996). His

classification of learning styles is based on the assumption that individual learning

can be conceptualised as a cyclical process consisting of four separate activity stages

(see Figure 1). This learning cycle starts with the individual making a concrete

experience. This experience is then reflected upon in the subsequent stage in which

the individual learner thinks about possible ways to adequately respond to this

situation (reflective observation). On this basis, the learner develops mental models to

integrate and make sense of the experience (abstract conceptualization). These mental

models are then used to make decisions and solve problems (active experimentation).

The process results in new experiences and reflections on these experiences, that is,

the process starts again.Underlying this learning cycle are two dimensions that Kolb (1984, 31) saw as

necessary for learning: The first dimension ‘grasping’ relates to the way in which

information is acquired during the learning process (see vertical axis in Figure 1).

Individuals acquire information either through concrete experience or through

abstract conceptualisation. Concrete experience stresses the involvement in experi-

ences, feelings and emphasises the singularity of specific situations (apprehension),

while abstract conceptualisation refers to theorising about experience, using logic

and concepts and being concerned with elements common to many experiences to

arrive at general theories (comprehension). The second dimension ‘transformation’

relates to the way individuals handle information (horizontal axis in Figure 1). Kolb

(1984) distinguishes between active experimentation where the learner stresses

practical applications, that is, ‘doing’ rather than ‘observing’; and reflective

observation where the emphasis is on reflecting on specific experiences and

Transforming

Gra

spin

g

Accomodation

AssimilationConvergence

Concrete Experience

Divergence

Abstract conceptualistion

Reflective Observation

Active experimentation

Figure 1. Learning style dimensions and types (Kolb 1984).

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understanding their meaning. Reflective observation emphasises the use of informa-

tion as a means to increase understanding instead of providing practical applications.

Fundamental to Kolb’s (1984) classification of learning styles was his observation

that individuals do not use all of the activities equally, but for each of the two

dimensions, that is, grasping and transformation, prefer and rely more on one of the

respective activities. Thus, with regard to the ‘grasping’ dimension, individuals will

have a stronger preference for either concrete experimentation or abstract con-

ceptualisation. Regarding the ‘transformation’ dimension, individuals are expected

to prefer either active experimentation or reflective observation. Based on this

preference for specific combinations of activities in their learning, Kolb (1984)

proposed a learning style typology consisting of four distinct learning styles

depending on the combination of learning activities preferred by individuals which

correspond to the four quadrants in Figure 1. Using Kolb’s concept, authors have

presented various recommendation as to how pedagogical approaches should be

adjusted to students’ learning style preferences (Kolb 1984; Smith and Sadler-Smith

2006). While the empirical evidence for the superiority of this matching is still

ambiguous (Hayes and Allinson 1996), various authors have highlighted the

potential negative consequences of a mismatch (see, e.g. Kolb 2000).

3. Learning styles and e-learning preferences

On a general level e-learning is characterised by the fact that it does not require

the physical, corporeal presence of the teacher and the learner in the same

location and at the same time. There is a vast array of instruments that can be

subsumed under the heading of e-learning. A number of authors focus on

individual e-learning tools, ranging from web-based forums, chat rooms, and

information repositories, virtual team work (e.g. team work via instant messa-

ging), tutor-guided online discussions, to the provision of video and/or audio

recordings of lectures (Goffe and Sosin 2005; Proserpio and Gioia 2007). Given

the variety of methods, we classify the main methods into three categories based

on what we suggest are some key differences between the various types of e-

learning. First, while some tools involve the virtually mediated interaction across

learners and/or between learners and teachers(s) (e.g. video-conferencing) and are

thus interactive, other tools do not involve such interaction (non-interactive). This

latter category can further be divided into teacher-centred and learner-centred

methods of e-learning. Learner-centred methods allow active student input, for

example, by answering questions to an online quiz and subsequently checking the

answers. Teacher-centred methods, on the other hand, do not allow the learner to

modify the sequence or speed of the learning process, that is, when watching a

downloaded lecture. We thus distinguish between three forms of e-learning tools:

(1) Interactive e-learning; (2) non-interactive/teacher-centred and (3) non-inter-

active/learner-centred. In the following we describe these three forms in more

detail and develop six research hypotheses of how they might be in line with the

needs associated with the different learning style preferences, assuming that a

match in this regard will manifest itself in high perceived usefulness of the

respective e-learning tool.

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3.1. Non-interactive, teacher-centred e-learning

The first category includes audio and video recordings of lectures as well as their live

streaming over the Internet. The main characteristic of these forms is the use of new

technologies to record traditional lectures and to deliver them to the students in the

form of CD-ROMs or Internet downloads. Thus, information technology is

primarily used to replicate the information delivery function in classrooms (e.g.

Leidner and Jarvenpaa 1995). These forms of e-learning are non-interactive in thesense that students have no opportunity of interacting with either the tutor or other

students. In addition, they neither require nor allow the learner to actively participate

in the process and were thus labelled as teacher-centred.

With regard to learning style preferences we would expect that the non-

interactivity combined with passivity is particularly appealing to students preferring

abstract conceptualisation with regard to the grasping of information as the learner

is not exposed to concrete experiences (as in interactive forms discussed below). Non-

interactive, teacher-centred forms of e-learning also provide students with timeneeded for abstract conceptualisation and offer students a greater degree of control

over the learning process. Similarly, it can be argued that non-interactive, teacher-

centred methods of e-learning also facilitate reflective observation instead of active

experimentation with regard to the transformation of information (e.g. Hedberg

2008). Listening to or watching recordings of lectures also gives the learner

considerable control over the learning process which is more conducive to a

preference for reflective conceptualisation. Based on these considerations we

propose:

Hypothesis 1a: Non-interactive, teacher-centred forms of e-learning will be perceived asmore useful by individuals with a preference for abstract conceptualisation than bythose with a preference for concrete experience (grasping).

Hypothesis 1b: Non-interactive, teacher-centred forms of e-learning will be perceived asmore useful by individuals with a preference for reflective observation than by thosewith a preference for active experimentation (transformation).

3.2. Non-interactive, learner-centred e-learning

Our second category includes e-learning methods that are non-interactive, but

require and allow the student to actively participate in the learning process. The e-

learning tools in this category allow for independent learning by working throughmaterials, (multiple choice) quizzes, web-assignments (Goffe and Sosin 2005),

simulations or games (Prensky 2001) provided to learners on CD/DVD or through

the Web. For example, many textbooks come with support material on the Web,

which allows learners to study and check their knowledge independently. These are

non-interactive ways of learning in the sense that there is no direct interaction with

the tutor and/or fellow students.

With regard to the learning style preference we suggest that based on the lack of

interactivity of the methods in this second section, they will be perceived as useful inparticular by individuals preferring abstract conceptualisation when grasping

information when learning (Toth 2004). This line of reasoning is thus the same as

for the passive methods of e-learning discussed in the previous section. However,

with respect to the transforming dimensions unlike the learner-centred methods of

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the previous section, teacher-centred e-learning methods allow for or might even

require active experimentation with material provided to the individual. Therefore,

non-interactive/active methods seem to be more useful for individuals preferring

active experimentation over reflective observation. Overall, we thus propose:

Hypothesis 2a: Non-interactive, teacher-centred forms of e-learning will be perceived asmore useful by individuals with a preference for abstract conceptualisation than bythose with a preference for concrete experience (grasping).

Hypothesis 2b: Non-interactive, teacher-centred forms of e-learning will be perceived asmore useful by individuals with a preference for active experimentation than by thosewith a preference for reflective observation (transformation).

3.3. Interactive e-learning

Interactive e-learning as the most complex form allows and/or requires students to

interact with the tutors or with fellow students. In this category we include methods

that allow synchronous as well as asynchronous interaction, such as web-based

forums, chat rooms, online communities, course-specific online forums, tutor-guided

online discussions and virtual team work (e.g. via emails and/or instant messaging).

So far, only some of these methods have been investigated in detail. Researchers have

investigated the dynamics of small virtual student groups (McConnell 2005), the

content of computer mediated interaction among students (Light et al. 2000), or

online versus face-to-face tutoring support (e.g. Richardson 2009). While some

researchers highlight the benefits of methods allowing for synchronous interaction

and regard them as more likely to foster sense-making than asynchronous or text-

based venues since they ‘emulate face-to-face meetings’ (DeSanctis et al. 2003, 568),

other researchers highlight the benefits of asynchronous methods (Goffe and Sosin

2005).

As many e-learning methods combine synchronous and asynchronous interac-

tion, the main characteristic that distinguishes this third category of e-learning

methods from the previous two categories is the possibility for (virtual) interaction

among learners and between learners and tutors. With respect to learning style

preferences we, therefore, expect these methods to be perceived as comparatively

more useful by students preferring active experimentation and concrete experience

over abstract conceptualisation and reflective observation. The relational nature oflearning in virtual teams, for example, and the associated greater scope for making

experiences, is likely to be of more use to students preferring concrete experience over

reflective observation with regard to the ‘grasping’ dimension of the learning style

concept (Jarvenpaa and Leidner 1999). The interactivity of these methods also means

that students are offered more room for active experimentation in their learning

(Alavi, Yoo, and Vogel 1997). Receiving feedback, for example, during tutor-guided

online discussions is likely to be of particular importance for individuals with a

preference for active experimentation (e.g. Richardson 2009). Students preferring

abstract conceptualisation, on the other hand, might not be interested in exploiting

the interactivity to the same extent. We thus expect that students preferring active

experimentation will find interactive e-learning methods as more useful than

students with a preference for abstract conceptualisation with regard to the

‘transforming’ dimension of learning. Based on these considerations we propose:

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Hypothesis 3a: Interactive forms of e-learning will be perceived as more useful byindividuals with a preference for concrete experience rather than by those with apreference for abstract conceptualisation (grasping dimension).

Hypothesis 3b: Interactive and passive forms of e-learning will be perceived as moreuseful by individuals with a preference for active experimentation rather than by thosewith a preference for reflective observation (transformation dimension).

4.Methods

4.1.Sample

To examine the link between learning style preferences and the perceived usefulness

of different types of e-learning empirically, we carried out a questionnaire survey of

students of business administration at universities in Germany, the UK, the USA,Russia, the Netherlands, Poland, China and the United Arab Emirates. The

questionnaire was distributed during normal class activities. Participation in the

survey was voluntary and anonymous. Because all students were enrolled in

programmes that were taught in English, we assumed a good level of English

proficiency among respondents and thus used an English language questionnaire at

all locations.

In total, we collected questionnaires from 1044 individuals over the period 2007

to 2008. Ninety-one questionnaires could not be included in our analysis as parts ofthe questionnaires were not filled in correctly. This was the case in particular for the

items used to measure Kolb’s learning style preferences which required respondents

to rank optional sentence endings (see below). After eliminating questionnaires that

were not filled in correctly or completely we had responses from 953 individuals that

could be used for empirical analysis. The students in the sample come from 74

different countries with a majority of 39.7% being German, followed by British

(9.5%) and Indian (9.4%) students. The sample consists of 515 (54.2%) male and 45.6

(45.7%) female students. Almost half of the students (399 students or 42.2%) wereclassified as exchange students, that is, students that were foreigners in their current

country of residence. The age of respondents ranged from 17 years to 50 years, with

an average age of 25 years and a standard deviation of 5.2 years. Only few

participants were older than 30 years. On average, the respondents had 2.4 years of

professional experience, ranging from no professional experience at all to a

maximum of three years (SD 4.4 months). Four hundred and fifty-seven (52%) of

the individuals were enrolled in undergraduate programmes, while 496 (48%) studied

for a post-graduate degree.

4.2. Measures

4.2.1. Independent variables

To measure the four dimensions of Kolb’s framework we used the Learning Style

Inventory (LSI). This was originally developed by Kolb (1976) and after being

criticised for its low internal consistency of scales and limited test-retest reliability(see, e.g. Sewell 1986), a revised LSI was suggested where short phrases substituted

the original single word responses to increase reliability of the measurements.

Because then there has been a third modification of the instrument which further

increased its validity and reliability (Mainemelis, Boyatzis, and Kolb 2002). The LSI

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consists of a number of sentences and four potential endings for each sentence that

the individual ranks in line with his/her preferences. Through combination of the

scores for sentence endings the researcher calculates scores for concrete experience,

reflective observation, abstract conceptualisation and active experimentation.

Subtracting the value of concrete experimentation from the value of abstract

conceptualisation (AC-CE) reflects the student’s preference with regard to the

acquisition of information (grasping). Positive scores indicate a comparatively

stronger preference of the individual for abstract conceptualisation than for concrete

experimentation, whereas negative scores indicate the opposite. In a similar vein,

subtracting the value of reflective observation from the value of active experimenta-

tion (AE-RO) reflects the student’s preference with regard to the processing of

information (transformation). High positive scores indicate a comparatively stronger

preference of the individual for active experimentation than for reflective observa-

tion, whereas negative scores indicate the opposite.

4.2.2. Dependent variables

To measure the perceived usefulness of the different methods of e-learning, we

provided students with a list of methods in each category and asked them to

evaluate in how far they consider the respective method as useful in their learning.

We used 5-point Likert-type scales, with 1 for ‘not at all relevant’ and 5 for ‘very

useful’ as anchors. The perceived usefulness of non-interactive, teacher-centred e-

learning methods was measured by asking individuals to evaluate three items:

audio recording of lectures, video recording of lectures and live streaming of

lectures. The second item-battery used the same question for two items reflecting

non-interactive, learner-centred e-learning methods (‘material provided on CD-

Rom/DVD’, ‘materials provided over the internet’). The third category contained

four items: ‘Web-based forums, chat rooms, online communities’, ‘Course,-specific

online forums and document sharing systems, ‘Tutor-guided online discussions’

and ‘Virtual team work (i.e. via emails and/or instant messaging, e.g., MSN,

Skype)’.

4.2.3. Control variables

Research has suggested a range of factors that influence the acceptance of new

technologies in the field of education (see, e.g. Gorard and Selwyn 2005;

Holtbrugge and Schillo 2008). We have, therefore, included gender, age and

professional experience as some of the most frequently suggested factors in our

analysis. Gender was measured using a dichotomous variable coded as 0 for male

and 1 for female students; age and professional experience were measured in

years. As individual preferences for learning styles might also depend on the

environmental factors, in particular, the ‘digital readiness’ of the students, we also

take into account the Internet penetration of the learners’ country of residence.

The Internet penetration rates were taken from the ‘Internet World Stats’ website

(http://www.internetworldstats.com/) and were calculated as Internet users divided

by overall population for the year 2008.

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5. Results and discussion

Table 1 presents the mean values, standard deviations and coefficients alpha for the

three groups of e-learning in our study. The mean values for the perceived usefulness

of the different methods range from 2.2 to 2.8 on our 5-point Likert-type scale with

‘Course-specific online forums and document sharing systems’ (mean 2.2) being

considered the least useful method and the ‘audio recording of lectures’ (mean 2.8)

the most useful method among our 953 respondents. All constructs showed an

acceptable level of reliability above the 0.6 level suggested by Nunnally (1978). When

the mean evaluations for the items in our three groups are averaged, the non-

interactive, teacher-centred learning methods are perceived as the most useful

although the differences between the mean values for the three categories are

comparatively small.

To investigate the influences of learning style preferences on the perceived use of

different forms of e-learning and to test our research hypotheses we used regression

analysis. For each of the three dependent variables we computed one model that only

contained the control variables and another model in which the learning style

preferences were added into the model. As Table 2 shows, our three models with the

preferences for different facets of e-learning as dependent variables are all statistically

significant at the 1%-level. With the exception of the first model, the addition of our

learning style variables increased the percentage of explained variance in the

dependent variables.

Our first model investigates hypotheses 1a and 1b regarding the relationship

between learning style preferences and the perceived usefulness of non-interactive,

teacher-centred forms of e-learning. As can be seen from Table 2, none of the

variables reflecting individuals’ learning style preferences enter on a statistically

significant level. The variance of the dependent variable in this model, reflecting non-

interactive, teacher-centred forms of e-learning is not associated with any facet of

individuals’ learning style preferences as conceptualised in Kolb’s model. Rather, the

results in Table 1 show that only the level of professional experience shows a

Table 1. Descriptive statistics.

Mean SD Alpha

Non-interactive, teacher-centred e-learning 2.66 0.878 0.76

Audio recording of lectures 2.80 1.099

Video recording of lectures 2.54 1.081

Live streaming of lectures 2.64 1.015

Non-interactive, learner-centred active e-learning 2.49 0.851 0.60

Materials provided to students on CD/DVD 2.47 1.035

Materials provided to students in the Internet 2.51 0.983

Interactive e-learning 2.54 0.699 0.64

Web-based forums, chat rooms, online communities 2.77 1.074

Course-specific online forums and file sharing systems

(e.g. Blackboard)

2.23 0.889

Tutor-guided online discussions 2.52 0.992

Virtual team work (i.e. via emails and/or instant

messaging, e.g. Skype)

2.66 1.062

N�953.

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statistically significant negative association with the degree to which individuals find

this form of e-learning useful (�0.168, p50.01). This finding is interesting given

that the provision of recorded lectures/seminars is common on many distance-

learning programmes targeted at individuals with professional experience, who

cannot combine their work responsibilities with attending on-site lectures (see, e.g.

Arbaugh 2000). Yet, our results indicate that the most likely target group of these e-

learning methods perceives them to be the least useful in their learning.

The second model shows a statistically significant association between the degree

to which individuals perceive those active forms of e-learning that do not require/

allow interactions with others as useful and their preference for abstract conceptua-

lisation over concrete experience (grasping) (0.76, p50.05). Thus, there is support

for hypothesis 2a. On the other hand, no significant coefficient for the transforma-

tion dimension is found. Table 1 shows that two of our controls are statistically

significant in our second model. With regard to gender our analysis indicates that

female students find this type of e-learning less useful than male students (�0.102,

p50.01). Female students might prefer more interactive, social forms of (e-)learning,

whereas male students might be happier with independent, solitary forms (Ong and

Lai 2006). Similar to our first model, there is again a statistically significant negative

influence of the level of individuals’ professional experience on the perceived

usefulness of this type of learning (�0.162, p50.01). This highlights once more the

potential conflict in using e-learning methods to reach non-traditional learners who

might find such methods of very little use in their learning.

Our third model uses interactive learning as dependent variable to investigate

hypotheses 3a and 3b. This model also shows a number of statistically significant

relationships. As proposed in hypothesis 3a, the coefficient for AC-CE is negative

and statistically significant, implying a negative association between a preference for

abstract conceptualisation over concrete experience and the perceived usefulness of

interactive e-learning methods (�0.069, p50.01). In other words, individuals who

prefer concrete experience consider interactive e-learning as more useful than

Table 2. Regression analysis (standardised coefficients).

E-learning methods

(1) Non-interactive,

teacher-centred

(2) Non-interactive,

learner-centred (3) Interactive

Learning style preferences

AC-CE � 0.018 � 0.076* � �0.069**

AE-RO � �0.039 � 0.011 � 0.044*

Control variables

Gender �0.028 �0.030 �0.102** �0.102** �0.009 �0.009

Age 0.037 0.037 �0.070 0.074 �0.042 �0.050

Professional

experience

�0.166** �0.168** �0.163** �0.162** �0.057 �0.051

Internet penetration �0.056 �0.056 �0.008 �0.038 0.070* 0.070*

F 4.555** 3.361** 3.315* 3.480** 2.688* 3.115**

R square adjusted 0.19 0.19 0.13 0.15 0.11 0.20

N�953.*p50.05, **p50.01.

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individuals with a preference for abstract conceptualisation when grasping informa-

tion. Our data also support hypothesis 3b concerning the transformation of

information. The respective coefficient shows a relationship between individuals’

preference for active experimentation over reflective observation and the degree towhich they perceive interactive e-learning methods as useful in their learning (0.044,

p50.05). From among the control variables, only the level of Internet penetration

enters the model on a statistically significant level. The coefficient is positive (0.070,

p50.05), implying a positive association between the level of Internet penetration

and individuals’ preference for interactive e-learning methods. This could be

explained by the fact that high levels of Internet penetration are associated with

the spread of more advanced, interactive types of virtual communication which has

allowed individuals in such countries to use and become more comfortable with suchinteractive methods of virtual communication. Gender, age and individuals’

professional experience are not related to the degree to which interactive e-learning

methods are considered to be useful.

Overall, the empirical results show that learning style preferences and the control

variables included in our model explain up to 20% in the variance of individuals’

perceived usefulness of different methods of e-learning. While they seem to have an

influence on the level to which individuals regard interactive e-learning activities and

non-interactive, learner-centred activities as useful, they do not seem to be related tothe degree to which individuals consider non-interactive, teacher-centred forms of e-

learning to be useful in their learning. In general, our analysis reveals that the more

advanced and complex the form of e-learning, the greater the influence of different

learning style preferences on the perception of their usefulness.

These results also point towards other factors that might be of greater

importance in explaining variances in the level to which individuals consider

different e-learning activities as useful. The rather low coefficients in our results

might indicate that e-learning does not require students to ‘prefer’ specific learningstyles as it allows students to fully exploit the learning in all four stages of Kolb’s

learning cycle. This is in line with authors who suggest that students should be

exposed to a variety of teaching methods (Zapalska and Brozik 2006). While Internet

penetration, professional experience and gender seem to play a role in learners’

preference for at least some of the e-learning methods, individuals’ age was not

statistically related to any of the forms of e-learning and thus does not seem to be

important in determining preferences for different types of e-learning on the basis of

our results. With regard to previous studies (e.g. Koohang 2004; Morris andVenkatesh 2000) a higher degree of perceived usefulness of e-learning among

younger students could have been expected. Yet, this assumption is not borne out by

our empirical data, that is, younger students do not show greater preferences for e-

learning methods than older students.

6. Contributions, implications and limitations

While the focus of this study has been on e-learning, research suggests that theoverall learning outcomes of learners can best be achieved by using blended

e-learning, that is, approaches that combine e-learning with traditional forms of

learning (Bielawski and Metcalf 2003; Hughes 2007). Yet, given developments in

information and communication technology as well as student preferences we suggest

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that various forms of e-learning are becoming more and more important and already

constitute an integral part of learning ‘ecosystems’ (Bellinger 2007, 28) that are

conducive to high levels of learning. Understanding in how far learners perceive

different forms of e-learning to be useful in their learning allows educators to adjust

the choice of e-learning tools in their teaching to make sure that learners fully benefit

from e-learning.

Although our study contributes to the explanation of variation in the perceived

usefulness of e-learning methods, some limitations have to be taken into account.

One limitation relates to the limited test-retest reliability of the LSI which has been

highlighted by Freedman and Stumpf (1978). This refers to the possibility of

unexplained variation in the four learning measures if an individual subject is tested

and retested within a short period of time (McKee, Mock, and Ruud 1992). Yet, this

criticism has been raised with regard to several other widely used learning style

instruments as well, and is thus not specific to the LSI (Sewell 1986). Furthermore,

the LSI gathers information about individuals’ preferences for learning rather than

their actual learning styles. While the common assumption is that these two variables

are closely related, this cannot necessarily be taken as a given. Establishing the

actually used learning styles of students would require different methodological

approaches and information not only from individuals, but potentially also from

their teachers.

Future research might address the relationship between learning styles and the

perceived usefulness of e-learning methods by identifying and including additional

variables. Ong, Lai, and Wang (2004), for example, stress the role of computer self-

efficacy and perceived ease of use. In this study, we proxied these factors using the

Internet penetration rate, but this might have been too general a measure, and

individual-level measures might lead to better results. For example, it could also be

expected that students studying for a technology degree have a greater affinity to

using and thus consider virtual learning as more useful in their learning than

traditional forms of learning. Research also suggests that disciplinary background

might affect the preferred learning styles of students. Using Kolb’s concept of

learning styles, Drew and Ottewill (1998), for example, highlight potential differences

in the learning styles required from students studying languages as compared to

business. Finally, Holtbrugge and Mohr (2010) reveal that cultural values, study

level, exchange student status and gender are related to individuals’ learning style

preferences. Investigating these and other factor seems to be a worthwhile extension

of research in this area.

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