In: Higher Education ISBN 978-1-61668-548-5

Editor: Mangus E. Poulsen, pp. © 2010 Nova Science Publishers, Inc.

Chapter 12

THE RELATIONSHIP BETWEEN ENTRY

CHARACTERISTICS, LEARNING STYLE AND

ACADEMIC ACHIEVEMENT OF COLLEGE FRESHMEN

Vincent Donche* and Peter Van Petegem

Institute of Education and Information Sciences, University of Antwerp, Belgium

ABSTRACT

Higher education in Flanders (Dutch speaking part of Belgium) is as many other

countries confronted with a growing heterogeneous student population in the first-year

which is paralleled with an increasing drop-out rate of students in the first-year.

Empirical studies investigating why some students are more successful in their studies in

the first year of higher education and other students not, are needed to better understand

the actual possibilities and boundaries of entry for students in higher education. In this

study we examined the impact of different entry characteristics and learning style

characteristics on academic achievement in first-year higher education. Entry

characteristics concerned in this study are age, gender, prior education, study discipline.

1.039 first-year students from eight different professional bachelor degree programmes of

a college of higher education participated in a survey-study. The study revealed that

approximately one fourth of the variance of academic achievement of first-year students

can be explained by different learning style characteristics and related entry

characteristics. The results underline the importance of assessing students in the

beginning of the first semester of higher education especially in terms of learning style

characteristics which opens also perspectives for student counselling and monitoring

especially for those who can be identified as at risk.

* Corresponding author: Institute of Education and Information Sciences, University of Antwerp, Venusstraat 35,

2000 Antwerpen, Belgium. Email: vincent.donche@ua.ac.be

Vincent Donche and Peter Van Petegem 2

1. INTRODUCTION

Higher education in Flanders (Dutch speaking part of Belgium) is confronted with a

growing heterogeneous student population in the first-year which seems to be paralleled with

an increasing amount of drop-out students in the first-year. Research of students’ learning

style characteristics together with other related personal and contextual factors are expected in

this study to contribute to a better understanding of what kind of entry characteristics are

sound predictors for academic achievement in the first year of higher education. The research

perspective is not only important for practice it is also for theory. Little is known about how

the interplay of different personal and contextual factors together with learning style

characteristics do have an impact upon academic achievement in higher education in

Flanders. Before we present the method, main findings and conclusions of our study it is

important to explain how we approached the concept of learning style in this study and what

personal and contextual factors have been included in our explanatory model.

1.1. Learning Style Characteristics

Over the years, research into learning style characteristics has evolved in many

directions. There are, therefore, studies to be found which investigate the more strictly

cognitive aspects of learning (Kolb, 1984); conceptions of learning and education and specific

learning strategies (Rossum & Schenk, 1984); aspects of self-regulation and metacognition

(Boekaerts, 1997); and motivational aspects (Pintrich, Smith, Garcia & McKeachie, 1993). A

great many of these studies are concerned with looking for connections between one or more

of these areas of attention in order to arrive at different learning style constructs (Jonassen &

Grabowski, 1993). Vermunt’s learning styles model integrates a number of the

abovementioned areas of attention and identifies the following learning style characteristics:

(1) cognitive processing strategies; (2) regulative processing strategies; (3) mental learning

models or conceptions of learning; and (4) learning orientations. Vermunt (1998) defines a

learning style as a cohesive ensemble of learning activities which students habitually deploy;

how they direct their learning processes; their conceptions of learning (mental learning

model); and their motives for studying (learning orientation). Using principal components

analyses of statements which were collected by means of the Inventory of Learning Styles

questionnaire (ILS), Vermunt went on to identify four typical learning styles: undirected,

reproduction-oriented, meaning-oriented and application-oriented (see also Vermunt, 1998).

This learning style model has been extensively used and validated in ILS-research in higher

education in the Netherlands (inter alia Busato, 1999), in Flanders (inter alia Donche & Van

Petegem, 2009) and in other countries (inter alia Boyle, Duffy & Dunleavy, 2003) and is

used in the present study.

The Relationship between Entry Characteristics, Learning Style … 3

1.2. Personal and Contextual Factors

Previous ILS-research has shown that learning style characteristics are subject to

different personal and contextual factors and are no trait-like characteristics (Donche,

Coertjens & Van Petegem, 2010). Entwistle, McCune and Hounsell (2003) describe a broad

model of influencing factors which they situated both at the level of learning and educational

processes in learning environments as well as at the level of the institutional and disciplinary

context such as, inter alia, characteristics of the curriculum and the evaluation culture in

place. Research into the relationship between personal and contextual factors and learning

style characteristics has indicated that amongst others discipline, age, prior education and

gender can be important influencing factors with regard to learning. Without any attempt at

comprehensiveness, we can point to a number of salient research findings in relation to these

factors. According to Severiens and Ten Dam (1997) and Slaats (1999) learning style

characteristics are influenced by gender. Thus, boys should score higher in comparison to

girls on undirected learning and lower on reproduction-directed learning. It has also been

found that students in secondary education have different learning styles regarding to the type

of secondary education they are following making a distinction on the level of learning styles

between study courses who are more theory or practice oriented (Slaats, 1999). Some

researchers underline age as an influencing factor with regard to learning. In young pupils (11

to 12 years) Klatter (2001) investigated whether differences in learning styles could be

identified. From this study it can be inferred that students in secondary education, unlike

students in higher education, exhibit a more limited range of learning strategies, learning

orientations and learning conceptions. It can be further inferred from this perspective that as

learners get older there is a further development of different learning strategies, learning

conceptions and learning orientations. Furthermore, it is also suggested in the literature that as

age and educational experience increase, application-directed learning develops starting from

meaning-directed learning (Vermunt, 2005). It appears that certain learning styles may be

associated with the study discipline. It has been observed among students in higher education

that meaning-directed learning styles are more frequently exhibited in social sciences, and

reproduction-directed learning styles more in economic sciences (Vermunt, 2005). However,

differences in learning may not only be related to specific learning requirements arising as a

result of differences in study disciplines. There may also be a selection effect. It is thus

possible that students with specific learning styles opt for precisely those study discipline

which capitalize on their learning style characteristics (inter alia Kolb, 1984). Differences in

learning style characteristics may thus be both a consequence and a cause of differences in

choice of study (Slaats, 1999). Gender, prior secondary education, study discipline and age

were found in different contexts of higher education to be related to individual differences in

learning and are therefore included in this study.

1.3. Academic Achievement

In research in higher education investigating the relationship between learning style

characteristics and academic achievement measured by mean exam results (GPA), it has been

repeatedly found that there is a negative relationship between largely unregulated learning

style characteristics and academic achievement (Busato, 1999). Vermunt (2005) found that a

Vincent Donche and Peter Van Petegem 4

positive relationship occurs between meaning-oriented learning characteristics and academic

performance but also that in some academic disciplines this positive relationship also occurs

for reproduction-oriented learning style characteristics. Not only learning style characteristics

are assumed to have an impact on academic achievement. Other personal and contextual

factors have been explored in many different studies in higher education as directly related to

academic achievement such as amongst others gender, age, prior achievement, socio-

economic status, cultural background, quantity and quality of education, study choice and

study load (see also Bruinsma & Jansen, 2007; Jansen, 2004; Tinto, 1993).

1.4. This Study

In this study, we examine the presence of individual differences in learning of first-year

students in eight different non-academic study disciplines and the impact of these differences

in learning on academic achievement. In this study we choose also to concentrate on the

variables gender, prior education, study discipline and age because we assume that these

factors are not only directly related to learning style characteristics (Vermunt, 2005) but also

to academic achievement. The choice for a selected set of personal and contextual factors is

also related to our aim to obtain a parsimonious explanatory research model to predict

academic achievement in the first-year. The research questions central in this study are:

1. How are personal and contextual factors related to learning style characteristics?

2. Which personal and contextual factors and learning style characteristics are related to

academic achievement?

METHODOLOGY

2.1. Data

Data was gathered within the context of a longitudinal research project in an institution of

higher education in Flanders (Dutch speaking part of Belgium) in collaboration with our

research group EduBROn (University of Antwerp). The project investigated the development

of learning patterns as well as the impact of learning patterns and personal and contextual

factors on academic achievement throughout eight three-year professional bachelor education

programmes. Eight study disciplines are involved: orthopedagogics, communication sciences,

journalism, electro-mechanics, hotel management, office management, business management

and teacher education. The quantitative study is characterised by a multi cohort design: the

population of first-, second- and third-year students participated in this study. One cohort of

first-year students has also been repeatedly questioned throughout their specific three-year

professional bachelor education programme. Also the teacher population of the institution

was questioned in this study. A variety of personal and contextual factors were examined in

this research project on the level of student characteristics (e.g. socio-economic status, study

motivation, linguistic ethnic background, personality, gender, age, prior education), teacher

characteristics (e.g. learning conceptions, teaching approach) and learning environment

The Relationship between Entry Characteristics, Learning Style … 5

characteristics (e.g. number of students, discipline, types of learning environments). The data

used in this study concerns one cohort of 1.037 first-year students. 419 of the students were

male (40.3%) and 620 students were female (59.7%). The mean age was 20.3 (SD 1.4). The

overall response rate was 73,6%.

2.2. Measures

Learning style characteristics were measured using a selection of scales of the Inventory

Learning Styles (ILS) (Vermunt, 1998). The ILS is a 120-item self-report questionnaire and is

developed for use in higher education. The validity and reliability has been well-documented

in previous research (Boyle, Duffy & Dunleavy, 2003; Donche & Van Petegem, 2009;

Vermunt, 1998). In this study we selected 10 out of 16 ILS-scales to measure aspects of

student learning (see table 1). We were particularly interested in students’ learning

conceptions (or mental models of learning), regulation and processing strategies.

Learning conceptions were measured by means of four scales representing different

mental models of learning, that is, construction of knowledge (e.g., "If I have difficulty

understanding a particular topic, I should consult other books of my own accord"; 9 items);

intake of knowledge (e.g., "To me, learning means trying to remember the subject matter I am

given"; 9 items), use of knowledge (e.g., "The things I learn have to be useful for solving

practical problems"; 6 items) and stimulating education (e.g., "Teachers should encourage me

to compare the various theories that are dealt with in a course"; 8 items). The items are

scored, ranging from (1) ‘I completely disagree’ to (5) ‘I completely agree’.

Regulation strategies were measured by means of three scales representing three different

regulation strategies, that is, self-regulation (e.g., "To test my learning progress. I try to

answer questions about the subject matter which I make up myself"; 11 items) external

regulation (e.g., "I study according to the instructions given in the course materials"; 11

items) and lack of regulation (e.g., "I notice that it is difficult for me to determine whether I

have mastered the subject matter sufficiently"; 6 items). The items are scored, ranging from

(1) ‘I never or hardly ever do this’ to (5) ‘I (almost) always do this’.

Processing strategies were measured by means of three scales representing three different

processing strategies, that is, deep processing (e.g., "I try to combine the subjects that are

dealt with separately in a course into one whole"; 11 items), surface processing (e.g., “I

memorise lists of characteristics of a certain phenomenon”; 11 items) and concrete processing

(e.g., “I pay particular attention to those parts of the course that have practical utility”; 5

items). Both the surface and deep processing scales are further divided into two related

subscales (see table 1). The items are scored, ranging from (1) ‘I never or hardly ever do this’

to (5) ‘I (almost) always do this’.

Information about students’ age, gender, study discipline, and prior education in higher

education were obtained from enrolment data gathered by the student administration office.

The indicator of prior education is the type of secondary education students have followed in

the fifth and sixth year of secondary education. In Flanders secondary education is provided

for young people aged 12 to 18 in four branches: ASO (general), TSO (technical), KSO

(artistic) and BSO (vocational), each divided into three 2-year periods. Pupils come into

contact with as many subjects as possible during basic education. From the third and fourth

year of secondary education, pupils can opt for a certain branch of study within ASO, TSO,

Vincent Donche and Peter Van Petegem 6

KSO, or BSO. In the fifth and sixth years of secondary education pupils are offered either

occupational training or higher education training. Students in our sample are spread across 3

categories: ASO (general), TSO (technical) and BSO (vocational).

Table 1. ILS-scales, internal consistency, means and standard deviations

Alpha Mean SD

Processing strategies

Deep processing .83 2.91 .69

Relating and structuring .81 3.11 .77

Critical processing .67 2.57 .79

Surface processing .78 3.06 .65

Memorising and rehearsal .73 3.21 .84

Analysing .67 2.93 .68

Concrete processing .68 3.13 .72

Regulation strategies

Self-regulation .78 2.53 .64

External regulation .68 3.18 .55

Lack of regulation .72 2.74 .74

Learning conceptions

Intake of knowledge .74 3.72 .58

Construction of knowledge .72 3.43 .53

Use of knowledge .75 4.01 .59

Stimulating education .86 3.31 .74

Academic achievement. The mean exam results (GPA) students obtained at the end of the

first year was taken as an indicator.

2. RESULTS

2.1. Personal and Contextual Factors

In order to investigate the relationship between the predictors gender, age, prior education

and study discipline and learning style characteristics, we used multiple regression analyses.

The most important findings from table 2 are given below.

Gender

Female students are likely to use more surface processing strategies in learning and also

apply in a lesser extent critical processing strategies compared to male students. External

regulation occurs more often among female students. The intake of knowledge is more

underlined in their learning conceptions compared to male students.

Age

Older students use more deep and concrete processing strategies and are more self-

regulated in their learning. Older students attach more importance to the construction of

knowledge in education. .

Table 2. Predictivity of learning style characteristics: Beta values for prior education, age, gender and study discipline, explained

variance and significance (F)

Predictors

ILS-scales

Prior

education

Age Gender Discipline R2 F

ASO BS

O

OP JO EM HM RP OM LO total

Processing strategies

Deep processing

Relating and structuring .09** .06* -.10* -.15*** .04*** 3.6

Critical processing .08* -.11** .07* .04*** 3.6

Surface processing

Memorising and

rehearsal

.07* .18*** .04*** 3.8

Analysing .14*** -.16*** .03 3.0

Concrete processing .08* .13** .07* .11* .08*

Regulation strategies

Self-regulation .09** .09* .10** .04*** 3.5

External regulation .06* .06* -.21*** -.07* .18*** .08* .09*** 9.4

Lack of regulation -.16*** .16*** .06*** 6.3

Learning conceptions

Intake of knowledge -.08* .19*** .10* .04*** 4.2

Construction of

knowledge

.11** .15*** .07* .05*** 4.5

Use of knowledge .13** .15*** .18*** .08* .05*** 4.6

Stimulating education

* p < 0.05; ** p < .01; *** p < 0.001. OP: Orthopedagogics; JO: Journalism; EM: Elektro-mechanics; HM: Hotel management; RP: business management; OM:

Office Management; LO: teacher education. The reference group are male students communicationmanagement with a TSO prior education.

Vincent Donche and Peter Van Petegem 8

Prior Education

Students with prior education ASO are in comparison with students with a prior

education BSO or TSO less unregulated in their learning. They process learning content in a

more deep approach and have less traditional learning conceptions. Students with prior

education BSO apply a more surface approach to processing learning contents and are more

externally regulated in their learning

Discipline

The analyses show that differences in learning style characteristics can be related to

different study disciplines. Differential effects of study discipline are present on the level of

learning conceptions, regulation and processing strategies. Most of the differences are

situated on the scales ‘use of knowledge’, ‘external regulation’ and ‘concrete processing’.

2.2. Relationship between Learning Style Characteristics and Academic

Achievement

In order to examine the relationship between learning style characteristics and academic

achievement Pearson correlations were calculated (table 3). Results show that mainly

unregulated learning characteristics are negatively correlated with academic achievement.

Several reproductive and meaning oriented learning style characteristics are both positively

correlated with academic achievement.

Table 3. Correlations between learning style characteristics and mean exam results

(correlations ≥ -.05 en ≤ .05 are omitted)

GPA

Processing strategies

Deep processing

Relating and structuring .15***

Surface processing

Analysing .19***

Concrete processing

Regulation strategies

Self-regulation .11**

External regulation .15***

Lack of regulation -.29***

Learning conceptions

Intake of knowledge -.11**

Construction of knowledge -.08*

Use of knowledge

Stimulating education -.08*

*p < 0.05; **p < .01; ***p < 0.001

The Relationship between Entry Characteristics, Learning Style … 9

Table 4. Relationship between learning style characteristics and mean exam results

(GPA) and personal and contextual factors

Predictors GPA

Person and context

Age -.10**

Prior education ASO (general) .22***

Discipline

- Electro-mechanics .22***

- Orthopedagogics .10**

Learning style characteristics

- Surface processing (analysing) .18***

- Concrete processing .11*

- External regulation .12**

- Lack of regulation -.22***

- Intake of knowledge -.13**

R2 .24***

F 10.4

*p < 0.05; **p < .01; ***p < 0.001

To investigate the combined effects of personal and contextual factors and learning style

characteristics on academic achievement multiple regression analyses were carried out.

Gender, age, prior education, study discipline and the different learning style characteristics

were set as predictors for academic achievement. In table 4 only the significant effects are

presented.

Differences in learning style characteristics were found to be directly related to academic

achievement. Lack of regulation or unregulated learning as well as traditional learning

conceptions were found to be negatively related to academic achievement. Aspects of surface

and concrete processing were found to be positively related to academic achievement as well

as aspects of external regulation. Active or versatile learning seems to be positively related to

academic achievement. Self-regulated and deep learning has no relationship with academic

achievement in first-year higher education. The addition of personal and contextual factors to

the research model increased the proportion of explained variance from 16% to 24%. Age is

negatively related to academic achievement which indicates that older students obtain in

general lower mean exam results than younger students. Prior education ASO (general) is

positively related to academic achievement. Academic achievement varies among different

study disciplines. No gender effect was found on academic achievement in this study.

3. CONCLUSION

The results presented in this chapter have shown that first-year students have different

learning style characteristics and that these characteristics can be measured in a reliable way.

Differences in learning style characteristics were found to be associated with several

measured personal and contextual factors. These results are in general in line with previous

Vincent Donche and Peter Van Petegem 10

research in which learning style characteristics were found related to prior education and age

(Vermunt, 2005), gender (Severiens & Ten Dam, 1997) and study discipline (Vermunt,

2005). However our research was carried out in the specific context of three-year professional

bachelor programs of a college of higher education, and adds to more knowledge of which

factors also in this educational context of higher education do have a valuable explanatory

power. In line with former research in the context of university education, our results showed

a negative impact of unregulated learning on academic achievement (Busato, 1999; Vermunt,

2005) as well as a positive but very weak correlation between meaning oriented learning

characteristics and academic achievement which has also been found in previous research

(Busato, 1999; Boyle et al., 1998; Vermunt, 2005). If we look at the results of the relationship

between regulation and academic achievement we have comparable results with other studies

claiming that the way students regulate their learning is less important than the fact that they

regulate their learning (Busato, 1999; Vermunt, 2005). A lack of regulation is in this respect a

far more better predictor for lower academic achievement than scales measuring self

regulation and external regulation in first-year higher education. Not only individual learning

characteristics were found to be predictive for academic achievement. Factors such as prior

education, age, study discipline explain to a certain extent variance in academic achievement

but to a far lesser extent than learning style characteristics (8% versus 16%). Especially

young first-year students who followed the ASO-track (general) in secondary education seem

to be more better prepared for first-year higher education. However, we are aware that among

the distinguished groups of for instance ASO (general) or TSO (technical) students there is

still a lot of variation possible within the types of ASO or TSO training students have

followed based on for instance the amount of mathematics or sciences lessons included.

The combined explanatory model used in this study predicts 24% of the variance in

academic achievement. The fact that the model used in this study is restricted to a limited set

of variables can be both interpreted as a strength in terms of parsimoniousness and power but

also as a weakness in terms of non-integration of other important predictor variables claimed

for in the literature. We are aware that still 76% of the variance of academic achievement has

not been predicted, which also raises important questions for future research. A possible

explanation of a not strong link between many learning style characteristics and academic

achievement can be found on the level of how we operationalised our dependent and

independent variables in this study. We used self-report questionnaires to measure students’

learning style characteristics. The scope of the selected questionnaire was restricted and it is

possible that other research methods or the use of additional questionnaires could present a

more refined view about how students usually learn. We are also aware that a strong link

between learning style characteristics and mean exam results (GPA) cannot always be

expected as an average mark for a subject or a number of subjects provides only a limited

vision of the quality of a learning process. A link between learning style characteristics and

mean exam results can also reflect the form of learning which is positively valued by the

evaluation system or by the instructors in a specific educational context (Vermunt, 2005).

Thus, the found positive relationship between for instance reproduction-oriented learning

style characteristics and mean exam results may as well as point to the fact that reproduction

oriented learning is deemed necessary in some disciplines of first-year professional bachelor

education and is therefore positively upheld by a higher academic outcome.

This study revealed valuable insights regarding to students at risk in the first year in

higher education. It has been made clear that not only student background characteristics are

The Relationship between Entry Characteristics, Learning Style … 11

important indicators but to some extent also their habitual way of learning. In future research

we aim to look for a better understanding of the variance in mean exam results by

investigating the explanatory power of other personal and contextual factors included in the

present data collection such as students’ academic motivation, study choice motives, study

delay, socio-economic and linguistic-ethnic status. These insights are important to understand

also why students learn the way they do and may also give additional perspectives for student

counselling and monitoring on the level of learning styles especially for those learners at risk.

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