Usage and testing of learning styles in HE business and language programmes

By

Ole Lauridsen & Peder Østergaard

Aarhus School of Business, University of Aarhus

Fuglesangs Alle 4

DK-8210 Aarhus V

Denmark

e-mail: and

Preface

The concept and the idea of individualized "learning styles" originated in the 1970s and have been gaining ground ever since. The basic idea is that most people favour a particular method when interacting, taking in and processing stimuli and information. It has been suggested that teachers should assess the learning styles of their students and adapt their classroom methods to best fit each student's learning style. It has, however, been difficult to convince academics of the reliability and validity of the “learning styles” concepts.

Building Excellence (BE) survey

The Building Excellence (BE) Survey is based on the original and widely used theories of the Dunn and Dunn Learning Styles Model. Coffield et al. (2004) showed that 177 articles have been published in peer-reviewed journals referring to this model, and a web-search in 2008 shows that the model has been attracting more and more attention from academics.

Figure 1 The Dimensions of the Building Excellence Model

The Building Excellence (BE) Survey is a self-directed learning tool. BE is based on the original Dunn and Dunn Learning Styles Model. BE is web-enabled and it takes approximately 20-25 minutes to complete the survey, which contains more that 100 questions. BE provides a systematic approach to identifying 26 critical constructs that promote or obstruct learning and productivity, and affect the way in which individuals concentrate in their immediate environment, make decisions, solve problems, process information, approach and complete tasks, retain new and complex information, develop new skills, and interact with others. The BE results for each individual are presented in a comprehensive Learning and Productivity Style (LPS) Profile report, which includes a one-page graph overview, narrative descriptions of preferences, recommended strategies, and an action planner. A personalized report is available for immediate printing when the BE survey is completed.

Figure 1 shows how the 26 constructs may be classified into 6 overall elements:

  • Perceptual, which is how we use our senses when discovering and processing new information, and how we remember and recall the knowledge acquired through the information. Basically, it is about the extent to which we use visual, auditory or tactile senses for learning in combination with verbal elements, which means learning from talking about information.
  • Psychological elements, which deal with the best waysto treat new and difficult information needed for decision-making and problem-solving
  • Environmental elements are related the learning setting, especially the psychical surroundings, i.e. sound, light, temperature. The factors that have an impact on the ability to concentrate for longer periods of time.
  • Physiological elements are rather similar to the environmental elements in the sense that it is factors that determine the ability to learn and work. Examples include the need forfood intake during learning, the time of day, and the need for movement.
  • The emotional elements are important to the perceptual elements. Depression and stress have a clear, negative impact on the ability to learn. Emotional elements are about motivation, persistence, need for conformity and structure as conditions for the learning process.
  • The social elements are about learning alone or in groups, and especially the size of the group.

Background

As part of its aim to improve learning among new undergraduate, graduate and executive students, the Aarhus School of Business at Aarhus University, Denmark, decided to establish a Learning Style Lab, LsLAB (see to facilitate teacher insight into student learning and thereby improve approaches to teaching and learning. This would also give students more insight into their own learning capabilities and styles, thus giving them tools to improve their learning, based on the idea that students at the business school should to a greater extent be responsible for their own learning. LsLab is a member of the International Learning Styles Network (ILSN), a network of just over 30 learning styles centres worldwide.

AarhusUniversity is a TOP-100 ranked university with approximately 35,000 students, of which the business school accounts for around 7,500. Programmes at the business school may be divided into 6 major groups:

  • Bachelor of Arts (BA) in languages for special purposes, corporate communication and European studies
  • Master of Arts (MA) in languages for special purposes or corporate communication
  • Bachelor of Science in business (BSc(B)) mainly with focus on core business subjectsand to some extent business law and business languages
  • Master of Science in business (MSc(B)), typically specialising in specific business topics such as marketing, management, logistics, finance, auditing, business law, economic consulting, international business and law
  • Different types of executive (EXE) and further education programmes for professionals, covering part-time programmes in Business Diploma, MBA and various master programmes.
  • Master in information technology, communication and organisation (ITKO).

Methodology and BEtest

It was decided that all new students (approximately 2,200) enrolled at the business school in September 2008 should be encouraged to take the BEtest during the autumn 2008. The results in this paper are based on the results from students taking the test in September 2008. A total of 1,026 studentsparticipated (47% response). All in all, it is expected that around 1,500 student will take the BEtest 2008.

The filled-in questionnaires were sent to ILSN for dataprocessing and calculation of constructs and returned to the students and the business school. The BEscores indicate the patterns of learning for individual students. The answers to the questions are weighted in order to give the values of the 26 constructs, which are transformed into an index in the range from -100 to + 100.

Background of the testing

Enrolment at HE in Denmarktakes place through a public system where the individual university is only allowed to determine a limited set of criteria for enrolment, e.g. requirements regarding pre-entry level of math and languages. However, the universities are normally not in at position to set up grading requirements or other types of requirementsfor enrolment of students. HE in Denmark is free: no tuition feesare paid by Danish or EU citizens to study at Danish universities. The funding for universities comes from the state and is primarily basedon performance in research and student progress and completion rates, providing a strong incentive to improve learning, especially because mass education is fact in Denmark.

Hypothesis and objectives of the survey and research

Under the conditions described, it is important for the business school and the university to learn more about the factors that make the students pass exams in different subject areas, leading to their final graduation. The business school found that a good start for a new student was to give him/her a learning style test, which provides teachers with the learning profiles of the students in their classes. At the outset, the main purpose was to raise awareness on learning among both students and academics. But as students take their exams and their performance is graded, it will be possible to use the scores of the BE to find out whether some of the concepts of learning styles can predict the grading results, which may eventually lead to improvements in teaching and learning in the courses and programmes. The objectives are to find out which types of students drop out or do not complete their studies, to improve student counselling before enrolment and in the early stages of the programmes when drop-out rates are typically high. The first causal analyses will be carried out in December 2008 when the first grading results for the new student become available, and we have observed the first drop-outs among new student intake.

The preliminary findings

An overall look at the results from the n=1,026 students who completed the survey in September 2008 shows the following pattern in student overall learning styles.

Figure 2. Overall average scores on BE constructs (n=1026)

Figure 2 shows that all the perceptual scores are high, which is expected since they are students at higher education, trained in using their senses to discover and process new information and to remember and recall knowledge. The average scores on “Visual Word Score” and “Auditory Verbal” are very high, which means that the students learn best via written texts of all kinds and by discussing information with othersto confirm their understanding of it. Especially the “Auditory Verbal” in general comes out very high in Scandinavian countries.

The psychological elements are rather low, both the “Analytical/Global”, showing whether the student approach to information management is analytical – step-by-step analyses - or amore global search for solutions,whichshows that the studentsare generally analytical in their approach. On the other psychological elements, the students generally tend to be very reflective in their approach to learning.

Two environmental elements differ significantly from zero. Surprisingly the students are sensitive to sound in their learning approach, meaning that their learning is hampered by too much sound, while the fact that seating has apositive impact on their learning is of no great surprise since the students have been seated in most of their previous learning experiences.

There is only one physiological element which shows great divergence from zero,and that is the “Late morning/early afternoon” score, which is high, indicating that studentsin general learn most around noon, which to a large extent can be explained by habit - the typical time of learning in primary and secondary schools is late morning and early afternoon.

The emotional elements are important to the perceptual elements. The students overall show high scores on task persistence, meaningthat in general the students are single-task students and not students who like multi-tasking. They are not conform in their approach, which indicates that students question information, its relevance, and the way of doing things.

The social elements of learning show that students prefer learning pair-wise or in small groups, and that they most certainly do not learn from being part of large groups. The students show a tendency to needing authority and to disliking variety in their learning approaches.

Further results depending on the main programme of enrolment are shown in table 1.

Table 1. Results for the main programmes

BA (n=274) / MA (n=68) / BSc(B) (n=474) / MSc(B) (n=127) / EXE (n=60) / ITKO (n=12)
Auditory Score / 12.6 / 15.3 / 16.2 / 7.1 / 1.5 / 17.7
Visual Picture Score / 33.3 / 39.7 / 38.1 / 37.6 / 39.4 / 46.9
Visual Word Score / 53.6 / 50.7 / 43.8 / 43.0 / 33.1 / 38.5
Tactual Score / 40.4 / 39.3 / 32.0 / 37.9 / 41.3 / 43.8
Kinesthetic Score / 35.3 / 31.8 / 34.4 / 31.4 / 46.3 / 30.2
Auditory Verbal Score / 49.1 / 48.9 / 40.7 / 48.9 / 48.5 / 36.5
Analytic Global Score / -23.1 / -27.4 / -24.5 / -29.3 / -26.4 / -26.6
Reflective Implusive Score / -31.7 / -33.6 / -24.4 / -34.0 / -17.1 / -19.8
Sound Score / -41.1 / -46.7 / -34.9 / -51.1 / -31.3 / -5.2
Light Score / 5.7 / 16.5 / 5.3 / 23.5 / 15.2 / -3.1
Temperature Score / -0.2 / -34.0 / 6.2 / -15.6 / -2.1 / -19.8
Seating Score / 20.5 / 25.4 / 29.1 / 34.2 / 24.8 / 46.9
Early Morning Score / -20.7 / -20.0 / -10.2 / -5.3 / 11.0 / 1.0
Late Morning Early Afternoon Score / 39.0 / 40.1 / 31.5 / 32.1 / 25.8 / 24.0
Late Afternoon Score / -9.4 / -12.5 / -6.8 / -10.9 / -19.4 / -4.2
Evening Score / -7.9 / -14.2 / -0.1 / -8.7 / -8.8 / 9.4
Intake Score / 13.2 / 8.8 / 8.8 / -7.3 / 7.1 / 21.9
Mobility Score / -12.0 / -14.0 / -10.0 / -16.9 / 3.5 / -13.5
Motivation Score / -3.1 / -3.9 / -4.2 / -4.7 / -8.8 / -12.5
Task Persistence Score / 39.6 / 35.1 / 35.3 / 27.9 / 27.3 / 6.3
Conformity Score / -47.4 / -42.8 / -44.1 / -37.5 / -42.1 / -33.3
Structure Score / -15.1 / -14.5 / -14.9 / -16.1 / -20.4 / -10.4
Alone Score / 15.1 / 23.3 / 23.1 / 15.3 / 27.9 / -12.5
Pair Score / 39.7 / 34.7 / 34.8 / 38.4 / 26.3 / 50.0
Small Group Score / 32.7 / 14.2 / 23.8 / 17.9 / 19.8 / 34.4
Large Group Score / -57.8 / -68.6 / -53.2 / -54.5 / -53.3 / -20.8
Authority Score / 32.8 / 23.5 / 22.1 / 17.4 / 11.3 / 7.3
Variety Score / -15.8 / -17.5 / -13.6 / -19.9 / -13.3 / -12.5

The differences in scores are tested by way of Analysis of Variance, ANOVA. In case the mean difference in a score is significantly different at a p-value<.05, the scores are in Bold, and within a score with significant difference between the programmes in the scores, the highest score is highlighted by a blue colour and the lowest score by a yellow colour.

First of all, the fact that 14 out of 26 scores proved to be significantly differentacross the different programmes proves that already in the choice of main programmes differences exist. Awareness of this fact could be used to capitalize on these differences in the approaches elected for teaching in the different programmes.

Table 2 shows tests on whether differences exit depending on gender.

Table 2. Test of differences in BEscores depending on gender.

BE-scores / Gender
Female (n=535) / Male (n=480) / Z-test
diff
Mean / std. dev. / std. err. mean / mean / std. dev. / std. err. mean
Visual Word Score / 56.2 / 28.1 / 1.2 / 34.8 / 33.2 / 1.5 / 11.02
Tactual Score / 42.3 / 30.0 / 1.3 / 29.3 / 32.6 / 1.5 / 6.56
Authority Score / 29.7 / 40.9 / 1.8 / 17.0 / 37.9 / 1.7 / 5.15
Early Morning Score / -5.2 / 62.0 / 2.7 / -18.9 / 56.5 / 2.6 / 3.66
Auditory Verbal Score / 47.6 / 28.9 / 1.2 / 42.0 / 28.1 / 1.3 / 3.17
Motivation Score / -2.1 / 25.4 / 1.1 / -6.8 / 24.6 / 1.1 / 3.03
Late Morning Early Afternoon Score / 37.8 / 46.5 / 2.0 / 29.3 / 45.3 / 2.1 / 2.94
Intake Score / 12.3 / 54.7 / 2.4 / 3.3 / 48.8 / 2.2 / 2.76
Light Score / 11.1 / 45.3 / 2.0 / 6.6 / 44.0 / 2.0 / 1.60
Task Persistence Score / 35.8 / 44.5 / 1.9 / 33.5 / 44.1 / 2.0 / 0.85
Alone Score / 20.9 / 45.2 / 2.0 / 18.7 / 44.9 / 2.0 / 0.79
Pair Score / 35.9 / 40.0 / 1.7 / 36.6 / 38.9 / 1.8 / -0.29
Variety Score / -15.5 / 32.3 / 1.4 / -14.8 / 32.6 / 1.5 / -0.34
Structure Score / -15.8 / 31.0 / 1.3 / -14.9 / 29.7 / 1.4 / -0.47
Conformity Score / -44.8 / 33.6 / 1.5 / -42.8 / 32.4 / 1.5 / -0.98
Small Group Score / 23.1 / 47.3 / 2.0 / 26.4 / 44.9 / 2.0 / -1.14
Auditory Score / 11.0 / 41.2 / 1.8 / 15.7 / 39.5 / 1.8 / -1.84
Seating Score / 24.4 / 45.0 / 1.9 / 30.2 / 40.6 / 1.9 / -2.15
Reflective Implusive Score / -30.5 / 36.6 / 1.6 / -24.6 / 36.9 / 1.7 / -2.57
Visual Picture Score / 34.2 / 38.4 / 1.7 / 40.2 / 33.7 / 1.5 / -2.64
Large Group Score / -59.0 / 36.1 / 1.6 / -51.0 / 41.7 / 1.9 / -3.25
Analytic Global Score / -27.6 / 21.7 / 0.9 / -22.3 / 23.8 / 1.1 / -3.70
Kinesthetic Score / 30.4 / 40.7 / 1.8 / 39.6 / 37.6 / 1.7 / -3.77
Sound Score / -45.0 / 49.6 / 2.1 / -31.9 / 50.4 / 2.3 / -4.17
Mobility Score / -16.2 / 42.0 / 1.8 / -5.1 / 42.2 / 1.9 / -4.20
Late Afternoon Score / -15.8 / 54.1 / 2.3 / -1.6 / 49.0 / 2.2 / -4.38
Evening Score / -16.9 / 65.1 / 2.8 / 9.0 / 60.8 / 2.8 / -6.55
Temperature Score / -12.5 / 50.5 / 2.2 / 10.2 / 48.5 / 2.2 / -7.31

The table is sorted by the Z-score, which indicates the extent to which differences exist in male and female scores. Z-score within +/- 1.96 do not represent significant differences. The scores on red background show the elements where female students have a significantly higher score compared to male students. In cases where male scores are significant higher, they are on blue background. The many significant differences in scores again underline the need for differences in learning approaches for male and female students.

Finally table 3 shows how the scoring correlates with age in general and age depending on the gender of the students.

Table 3. Correlation between BE-scores and age of students

Correlations with age / All (n=1026) / Female (n=535) / Male (n=480)
r / p-value / R / p-value / r-male / p-value
Light Score / 0.089 / 0.005 / 0.010 / 0.812 / 0.196 / 0.000
Auditory Verbal Score / 0.083 / 0.008 / 0.038 / 0.386 / 0.145 / 0.001
Tactual Score / 0.077 / 0.015 / 0.025 / 0.571 / 0.144 / 0.002
Seating Score / 0.077 / 0.015 / 0.063 / 0.147 / 0.097 / 0.033
Variety Score / 0.070 / 0.025 / 0.015 / 0.730 / 0.143 / 0.002
Early Morning Score / 0.069 / 0.029 / 0.031 / 0.481 / 0.124 / 0.006
Alone Score / 0.052 / 0.099 / 0.079 / 0.067 / 0.016 / 0.731
Kinesthetic Score / 0.037 / 0.241 / 0.000 / 0.998 / 0.090 / 0.048
Evening Score / 0.031 / 0.329 / 0.043 / 0.319 / 0.015 / 0.740
Visual Picture Score / 0.024 / 0.438 / -0.003 / 0.948 / 0.065 / 0.152
Large Group Score / 0.018 / 0.558 / -0.009 / 0.838 / 0.050 / 0.272
Mobility Score / 0.010 / 0.757 / -0.032 / 0.462 / 0.064 / 0.159
Reflective Implusive Score / 0.004 / 0.911 / 0.018 / 0.679 / -0.015 / 0.742
Analytic Global Score / -0.016 / 0.603 / 0.003 / 0.945 / -0.040 / 0.385
Auditory Score / -0.030 / 0.343 / 0.037 / 0.392 / -0.121 / 0.008
Late Afternoon Score / -0.035 / 0.269 / -0.015 / 0.737 / -0.065 / 0.157
Conformity Score / -0.037 / 0.239 / -0.067 / 0.120 / 0.004 / 0.928
Sound Score / -0.044 / 0.164 / -0.048 / 0.266 / -0.039 / 0.395
Visual Word Score / -0.059 / 0.061 / -0.099 / 0.022 / -0.024 / 0.607
Intake Score / -0.070 / 0.027 / -0.039 / 0.366 / -0.115 / 0.012
Temperature Score / -0.073 / 0.019 / -0.120 / 0.005 / -0.014 / 0.758
Pair Score / -0.074 / 0.019 / -0.108 / 0.012 / -0.028 / 0.546
Motivation Score / -0.085 / 0.007 / -0.060 / 0.164 / -0.119 / 0.009
Late Morning Early Afternoon Sco / -0.097 / 0.002 / -0.091 / 0.036 / -0.106 / 0.020
Task Persistence Score / -0.098 / 0.002 / -0.080 / 0.065 / -0.124 / 0.007
Small Group Score / -0.112 / 0.000 / -0.188 / 0.000 / -0.008 / 0.868
Structure Score / -0.121 / 0.000 / -0.120 / 0.006 / -0.123 / 0.007
Authority Score / -0.135 / 0.000 / -0.190 / 0.000 / -0.060 / 0.186

Table 3 shows Pearson correlation coefficients between values of scores and age of the student. A negative r means that the scores decrease if the age of the students increases and a positive r means an increase in scores when age increases. Significant correlation coefficients are given a coloured background.

Overall, there are number of scores that depend on the age of the student, but the most interesting finding is that when digging into analyses of the coefficient depending on gender, it seems that the overall correlations are very different for the two sexes.

Conclusion

Both students and academics have shown great interest in the test at AarhusUniversity, and there has already been a lot of positive feedback. The preliminary results in this paper, together with a number of other analyses not shown here, clearly indicate that the BEtest has great potentials in predicting choice of study programme in business schools, and that age and gender are very important factors to consider when designing programmes. The overall results have great face validity and give reason to believe that transparency motivates students and academics as certain factors influencing learning become highly visible.

References:

Coffield. F.. Moseley. D.. Hall. E.. Ecclestone. K. (2004). Learning styles and pedagogy in post-16 learning. A systematic and critical review. London: Learning and Skills Research Centre.

Dunn. R. S.. Dunn.K. J.; Gary E.. (1981) Learning Style Inventory. Lawrence. Kansas: Price Systems.

Dunn. R. S.. Griggs. S.A. (2000) Practical Approaches to Using Learning Styles in Higher Education. Westport. Connecticut: Bergin& Garvey

Kogan. J. (2007) Introduction to Clustering Large and High-Dimensional Data. CambridgeUniversity Press.

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