Mehmet Ugural, Ph.D.1, Salim Akyürek, Assit. Prof. 2, Ecem Tezel, M.Sc.3
Professor Doctor Heyecan Giritli4
Ph.D.Assistant Professor, Dept. of Civil Engineering, Istanbul Kültür University, Istanbul, Turkey.(Corresponding author)
¹Assistant Professor, Scool of Tourism and Hotel Managment, Near East University, Istanbul, Turkey ²Research Assistant, Dept. of Architecture, Istanbul Technical University, Taşkışla Campus, Istanbul Abstract
Knowledge of learning styles plays a vital role in education because, it can enhance the ability of educators to build on student experiences and construct new learning opportunities. This study aimed to understand the learning preferences of students from two distinguishing departments of construction who are educated in foreign language of English. With this aim, following an in depth literature review, the learning style of 170 undergraduate architecture and civil engineering students at Istanbul Technical University were empirically surveyed, using Honey and Mumford’s Learning Style Questionnaire (LSQ). According to statistical tests of the questionnaire data, there is no evidence supporting the hypothetical two bipolar structure of the LSQ. However, the results confirmed different characteristics of students from different disciplines.
Keywords: Learning Style Preference; Architecture Student; Civil Engineering Student; Undergraduate Level.
Introduction
Education research students learn and study in different ways and that students’ learning styles will not only affect their academic performance, but also prepare them for demands and expectations of the business world. As such, understanding the ways in which students learn is a key element to education improvement. Students’ behaviors and performanceduring lectures differ from each other according to their prominent learning style. Obviously, failure to observe individual differences in teaching and learning process would inevitably lead to an impoverishment of education. That is to say that, academic achievement of a learner depends on his/her intellectual ability, as well as his/her preferred learning styles (Kolb, 1984).
A review of a large number of studies into the relationship between learning styles and performance indicates that, learning style has an impact not only on academic performance, but also on work-related performance as well (Furnham et al., 1999; Kozhevnikov, 2007; Hamza et.al, 2018; Akhlaghiet.al, 2018;). There have also been various systematic reviews in this area (Hough, 1998, 1992; Feldman et.al., 2015; Al- Azawe and Badii, 2014; Jouaneh, 2005; Romanelli et.al., 2009). According to these studies, students prefer different learning styles. As such, understanding individual differences in the ways students approach learning is a key element in enhancing learning performance of students but also preparing them for demands and expectations of the business world.
As noted by Kolb (1983), learning styles are not fixed personality traits but rather refer to individuals’ characteristics and behavior explaining their preferred ways of gathering, organizing and thinking about information (Fleming, 2005). Learning theories and models are among the means that aim to account for differences in individual learning (Scott, 2010). However, the myriad of theories of learning styles with their overlaps and inconsistencies have led to many criticisms of their value. An in-depth discussion of theories and models in the field of learning style to date is beyond the scope of this paperand the reader is
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referred to Cassidy (2004),Coffield et al. (2004), Feldman et.al. (2015), Al-AzaweiandBadii (2014) for a thorough treatment of the subject
Students’ learning preferences may be influenced by several factors, including gender, age, major, and sociocultural factors (Sarabi-Asiabaret.al, 2015). Contradictory results regarding these potential influences have been reported in various studies (Al-Saud, 2013; Rahimiet.al.,2008).
Some researchers have investigated the differences in the learning styles of students of different majors. In these researches, it has been suggested that studies comparing majors should focus on discrete majors instead of more generalized headings. Therefore, this study aims to raise concerns for individual differences in learning styles between architecture and civil engineering students. The significance of this paper, apart from its valuable insights about learning process, is to uncover the diversity among architecture and civil engineering students’ approaches to learning at Istanbul Technical University. Learning Style Instruments
A review of the literature reveals various learning style instruments as well as their potential use and limitations, a wide variety of instruments or inventories for measuring learning styles and each has both advantages and disadvantages (Cassidy, 2004). As argued by Hawk and Shah (2007), each instrument has its own format, various number of statements and includes certain complexities. Thus, it may not be possible to explore all of the richness of the nature of learning styles with a single instrument. Many of them suffer from low internal reliability and lack of empirical evidence. The critical question is whether these instruments really just measure studying performance.
Considering the dozens of developed learning style models, five of the models have taken partin engineering education literature. These models are Myers-Briggs Type Indicator (MBTI), Kolb’s Learning Style Inventory (LSI), Felder and Silverman’s Index of Learning Styles (ILS), Dunn and Dunn’s Learning Styles Inventory Visual, Auditory and Kinesthetic (LSI-VAK) and Honey and Mumford’s Learning Styles Questionnaire (LSQ). Even Kolb’s Learning Style Inventory (LSI) and Honey and Mumford’s Learning Styles Questionnaire (LSQ) have been widely used in researches about learning styles of students in various disciplines, there are few published studies that have systematically examined the learning styles of architecture students (Khorshidifard, 2014; Demirbaş&Demirkan, 2003).
In this study, Honey and Mumford’s (1992) Learning Style Questionnaire (LSQ) was selected for use for a number of reasons. First, it has been widely applied to educational settingsin different countries(Duff & Duffy, 2002).Second, despite Duff and Duffy (2002) revealed concerns about LSQ’s reliability and validity, it does not appear to have lower status than other inventories addressing learning styles (Sadler- Smith, 2001; Honey & Mumford, 1992; Allinson& Hayes, 1988). Third, items included in the LSQ are found relatively easier to understand (Duff & Duffy, 2002). Additionally, LSQ can be completed in a shorter time since it has fewer items compared with some of the instruments.
Honey and Mumford (1986; 1992) extended David Kolb’s theories into a psychological framework of four basic learning styles: activists, reflectors, pragmatists and theorists. These four styles correspond approximately to those suggested by Kolb’sELT (Experiential Learning Theory): Active Experimentation (Activist), Reflective Observation (Reflector), Abstract Conceptualization (Theorist), and Concrete Experience (Pragmatist). Activists prefer to learn by experience and tend to act first and consider the consequences later. Reflectors are more likely to learn from reflective observation. They tend to be cautious and keep a low profile. Theorists are able to learn from logically sound, coherent theories, exploringobservations. They emphasized the importance of perfectionism and analytical thinking. Pragmatists focus on learning by doing or trying things with practical values. They have the capacity for making practical decisions and solving problems.
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Although the LSQ shares the same theoretical basis with Kolb’s LSI, researchers found that the LSQ has better psychometric properties than the LSI (Allinson& Hayes, 1988). This study will seek to use Honey and Mumford’s LSQ to understand the learning style preferences ofarchitecture and engineering students and draw comparisons with previous studies.
Competing Ideas About Learning Style Preferences
Learning preferences may be defined as the favoring of one particular style of learning over another. Additionally, style is an important part of learning, because it determines an individual’s preferred way of learning. However none of the studies has been determined the optimal learning style so far. A belief expressed in the field of learning styles is that, one learning style is neither preferable nor inferior to another, but is simply different, with different characteristic strengths and weaknesses (Felder & Brent, 2005).
Previous research on the concept of learning style preferences highlighted some controversial views. Some educators focused on whether learning style preferences should be considered as bipolar (e.g. mutually exclusive) or orthogonal (e.g. combinatorial). Kolb’s learning style inventory measured student preferences in two bipolar dimensions as active-reflective and abstract-concrete. In Kolb’s opinion, for example, students may show a preference for active experimentation or they prefer to think about their experiences by reflective observation (Lynch, 2002).
Parallel with Kolb’s findings, Dörnyei (2005) proposes that learning styles represent two extremes, each of which has its own potential advantages and disadvantages, and argues that an individual who falls on a middle ground between these opposite models is value neutral. However, current research findings lend little empirical support for a dual-factor structure (Kayes, 2005; Yahya, 1998; Brew, 1996). Whereas some studies failed to support Kolb’s hypothesized bipolar dimensions of style (Wierstra & de Jong, 2002; Geiger, 1992; Cornwell et al., 1991; Ruble & Stout, 1990), others found mix support. For example, De Ciantis and Kirton (1996), in their study, presented evidence for two bipolar style orthogonal dimensions, which are not consistent with Kolb’s configurative opposites. In two studies on small samples of British and Indian managers, researchers did not reproduce the hypothesized two orthogonal bipolar factor structure.
Based on original ideas by Kolb (1984), Honey and Mumford’s work has also postulated that different situations demand and reinforce the application of different learning styles. That is, individuals change their learning style depending on the context of their environment (= style flexibility). A lack of this flexibility results in learners struggling. This raises the question of whether learning flexibility is a function of balancing learning modes.
Scanning the literature also demonstrates that there has been much debate over the stability of learning style among researchers. Some researchers and educators acknowledge that learning styles are fix and stable, or at least are very difficult to change. To defend this belief, for example, Dunn and Griggs (1989) argue that learning style is a “biologically and developmentally imposed set of characteristics that make the same teaching method wonderful for some and terrible for others’’. Similarly, Claxton and Ralston (1978) indicated that learning style is stable and Cornett (1983) proposed that, the core of the learning style of an individual remains unchanged despite qualitative changes may happen. On the other hand, the evidence about students’ learning style preference changes over time provided by Geiger and Pinto (1991) seemed weak and inconclusive. Contrarily, others support the idea that learning styles are not fixed modes of behavior, but are influenced by the situation (Oxford, 2011; Reid 1987). For example, Pinto et al. (1994) found the learning style preferences of students to be susceptible to change over time. Furthermore, Kolb (2000) indicated that learning style is not a fixed trait, but a differential preference for learning, which changes slightly from situation to situation.
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In summary, researchers were divided in their findings as to whether students change their learning style in response to different situations. However, much of learning research to date remain both confusing and controversial.
According to Herman Witkin’ smodel in 1978, a distinction may be drawn between students who are fixed in their use of a situation-dependent or situation-independent learning style and those who has flexibility in learning (Anderson & Adams, 1992; Griggs, 1991; Hvitfeldt, 1986). Following this theory, one can go further and suggest if learning styles are not fixed, but can change and adapt to different situations and learning contexts, emphasis should be given to not only identifying the learning styles of students, but also encouraging a balanced approach to learning. This may be attributed to the fact that, a student’s preferred (or most comfortable) learning style is not a signal of that student’s ineffective learning in other styles. Contrarily, the student’s flexibility to perform different learning styles according to the requirement of the situation is an undeniable advantage compared with others who prefers only a single learning style (Brunton et al., 2016). This is also the case for the workforce.
Methodology
Sample and Data Collection
The sample was composed of undergraduate students from Departments of Architecture and Civil Engineering. The students were divided into two groups. Group 1 consisted of 91 undergraduate students of the final year class of Civil Engineering Department, while Group 2 consisted of 79 undergraduate students of the final year class students of Architecture. Both groups were sampled using convenient sampling technique.
Data of this study were collected using a questionnaire that consisted of two parts. First part includes questions designed to determine the respondents’ demographic information. Second part of the questionnaire consists of the 80-item LSQ developed by Honey and Mumford (2000). Respondents were asked to indicate their agreement of the 80 questions (20 questions for each of the four learning styles). The answers of each student was analyzed and used to measure their tendencies towards a particular learning style.
Measurement
This study investigates differences in learning style preferences of architecture and civil engineering students. Each respondent’s cognitive complexity among the four learning styles is based on the assumption that all individuals develop and practice a balanced mixture of styles in response to situational demands. Nevertheless, some individuals may heavily be dominated by one learning style, or are just particularly weak in one style.
The balance among the four learning frames was operationalized in this study by creating three learning orientation that indicated the degree to which perceptions of the behaviors of students reflected their balanced (or unbalanced) use of the four learning styles.
1. Fully balanced learning orientation: Students in this category scored above the norm-based mean scores for all four learning styles.
2. Moderately balanced learning orientation: Students in this category scored above the norm-based mean scores for any of three of the four learning styles.
3. Unbalanced learning orientation: Students in this category scored above the norm-based mean scores for only one or two of the four learning styles.
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Results and Discussion
Means, standard deviations, inter-correlations and Cronbach’s alpha reliability coefficients were calculated for the sample as a whole (see Table 1). The coefficients indicate that, the scores produced by the LSQ have modest internal consistency reliability: Activist, 0.74; Reflector, 0.77; Theorist, 0.68 and Pragmatist, 0.64.
For the data analysis and interpretation of the results, Honey and Mumford’s scoring norm in the UK (1992) was used as the main reference. If the score of the students in LSQ survey appeared higher than the average, it is likely to indicate that the students highly prefer that particular learning style. Otherwise, it is likely to indicate that the students are having low preference in that style.
Table 1. Means, standard deviations, ranges, reliability estimates and inter-scale correlations for the LSQ inventory subscales
Learning
Styles Mean SD Range Activist Reflector Theorist Pragmatist
Honey & Mumford Norm (1992) Activist 12.19 2.71 7-18 (.74) 9.3 Reflector 14.12 3.12 4-19 -.093 (.77) 13.6 Theorist 13.18 2.82 6-19 -.146 .582** (.68) 12.5 Pragmatist 12.42 2.64 6-20 .100 .114 361** (.64) 13.7 Cronbach’s alpha values are shown in parentheses on the diagonal.
**p<0.01 (two-tailed test)
As shown in Table 1, correlation analysis revealed a positive relationship between the Theorist learning style and the Reflector and Pragmatist learning styles. That is, students who were more Theorist in their learning styles also were more Reflector and Pragmatist in their approach to learn. This result did not support the hypothetical two bipolar structure of the LSQ as theorist and reflector learning styles are considered opposites. However, it conforms to the findings of the researchers who failed to support the two bipolar dimensions of the LSQ (Duff & Duffy, 2002; De Ciantis & Kirton, 1996).
Table 2 represents the learning style orientation of respondents. Initial investigation of the data revealed that, 16.5%of respondents have highly balanced learning orientation type. The percentage of respondents whose learning style type is moderately balanced is 30.6%. The remainder of 52.9% encompasses unbalanced learning style preference. In respect to educational specialization, 7.1 percent of the undergraduate architecture students and 23.2% of the civil engineering student prefer a highly balanced or multi-modal learning style within the classroom. Unbalanced learning styles are abundant among the students enrolled in civil engineering and architecture with 49.5% and 57.7%, respectively.
Table 2. Learning style orientation
Learning Styles Total Civil Engineering Architecture
n % n % n %
Highly balanced 28 16.5 23 23.2 5 7.1
Moderately balanced 52 30.6 27 27.3 25 35.2
Unbalanced 90 52.9 49 49.5 41 57.7
Total 170 100 99 100 71 100
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A series of paired t-tests were carried out to examine learning styles preferences of students in the architecture and civil engineering groups, respectively. Paired t-test was calculated on pairs of learning styles with statistical significant interaction effects within the data sets for each group and shown in Table 3. Results indicate that reflector style was the most preferred learning style of students majoring in architecture. The next preferred styles for the architectural group were activist and theorist styles (no difference between styles). Finally, the pragmatist style was the least frequently performed by the architectural group.
Table 3. Paired samples test
Learning styles Mean SD t df Sig. (2-tailed)
A rc hitectur e st ude nts Activist-Reflector -1.479 4.306 -2.894 70 .005 Activist-Theorist -.606 4.331 -1.178 70 .243 Activist-Pragmatist .944 3.714 2.141 70 .036 Reflector-Theorist .873 3.061 2.404 70 .019 Reflector-Pragmatist 2.423 4.087 4.994 70 .000 Theorist-Pragmatist 1.549 2.787 4.685 70 .000 Civil E ngi nee ri ng st ude nt s Activist-Reflector -2.263 4.325 -5.205 98 .000 Activist-Theorist -1.273 4.075 -3.107 98 .002 Activist-Pragmatist -1.081 3.263 -3.296 98 .001 Reflector-Theorist .990 2.481 3.971 98 .000 Reflector-Pragmatist 1.182 3.609 3.258 98 .002 Theorist-Pragmatist .192 3.181 .600 98 .550
From the research findings, it can be concluded that the students majoring in civil engineering preferred the reflector style the most, followed by the theorist and pragmatist styles (no differences between two styles). By far, the least preferred style was the activist style.
Independent t-tests were carried out to compare students’ learning styles between the architecture and the civil engineering groups. Results of the independent t-test showed that, there was one significant difference in students’ learning styles between the two student groups. Students in the civil engineering group reported the use of pragmatist more often than those in the architecture group (t=2.460, p<0.005). In addition, gender difference was found only in the pragmatist style where men have a higher mean score than women (t=-4.812, p<0.001). This result is inconsistent with Honey’s study, which revealed no obvious gender differences in learning style preferences. The reminder of this section will discuss these findings in the light of educational specialization culture and gender.
According to the findings of this study, there was no difference between the most preferred learning style of the two student groups. The results showed that the most preferred learning style was the reflector, regardless of the respondents’ major. Yet, the reported evidence is in contrast with the findings of those
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who found that engineering students tend to show higher preference in the active dimension when compared to the reflective dimension (Lee & Sidhu, 2013; Kolmos & Holgaard, 2008; Mills et al., 2005; Felder & Silverman, 1988). This finding of the present study, combined with the findings of similar studies, raises the question of the impact of culture on the learning process.
There is reason, based on the literature, to believe that cultural differences tend to influence learning preferences (Hofstede, 1997). Hofstede’s cultural dimensions may be a good way for understanding of how culture affects learning styles. In his research with 116.000 employees of IBM in 72 countries,