Facilitating Engineering Students In The Language Classroom: Multiple Intelligences ... · the...

AC 2010-242: FACILITATING ENGINEERING STUDENTS IN THE LANGUAGECLASSROOM: MULTIPLE INTELLIGENCES PROFILES TO IMPROVEFOREIGN LANGUAGE COMPETENCE

Adrian Millward-Sadler, University of Applied Science, Graz

Annette Casey, Joanneum University of Applied Sciences

Frank Newman, University of GrazFrank Newman is a senior lecturer at the Department of Translation Studies at the University ofGraz in Graz, Austria. Frank has been teaching English, mainly writing skills, and Americanculture since 1984. He also teaches English for Engineers at the Graz University of Technologyand was involved for many years in in-service teaching training in Austria and abroad. His currentfocus is using wikis in language teaching.

© American Society for Engineering Education, 2010

5.574.1

Facilitating Engineering Students in the Language Classroom: Multiple

Intelligence Profiles to Improve Foreign Language Competence

This is the first part of a study aimed at improving English language classroom materials for

students of an engineering degree programme at a non-English speaking university. In

technical subjects at tertiary level, 2nd language competence, (in this case English) has become

highly important due to the need for communication in a global workplace. However, as

language learning is usually neither the primary goal nor a particular academic of linguistic

strength of students who have chosen such a degree path, motivation can be low.

In this study, the theory of Multiple Intelligences was applied to ascertain engineering

students’ learning profiles with a view to optimising students’ language learning environment

through the adaptation of course materials. This was done by surveying three separate year-

groups of students from the engineering department over a two year period using a

questionnaire, which was designed to identify these profiles. As a control, a group of students

from a non-technical degree course also received the same questionnaire.

The results of these surveys are published in this paper. Significant similarities among

students from the engineering department were apparent in their Multiple Intelligence

profiles, which contrasted substantially to the profile results created by the control group of

non-technical students. Further study is currently underway, to examine and confirm any

possible relationship of Multiple Intelligence profiles among student from engineering degree

programmes of the same and different types, as well as further control groups from non-

technically oriented degree programmes.

Page 15.574.2

Introduction

This paper presents the preliminary results of a study of 88 students at the FH Joanneum

University of Applied Sciences, Graz, from the department of Vehicle Technology studying

for the degree of Automotive Engineering to determine their intelligence profiles according to

the Theory of Multiple Intelligences proposed by Howard Gardner in 1983. A control group

of 11 students from the Karl-Franzens University’s Institute for Theoretical und Applied

Translation Studies were also analysed for control group purposes.

The intention of the study was to enable instructors in the Vehicle Technology department’s

English as a Foreign Language (EFL) programme to tailor their course materials to the benefit

of the year-groups concerned and thereby increase both student motivation and academic

achievement in an area often perceived by students as either difficult or irrelevant in the

confines of a technical degree.

The paper is divided into three basic sections. Firstly, department of Vehicle Technology and

the degree of Automotive Engineering will be briefly outlined, including its curriculum,

student year-group structure and the EFL programme which is part of the degree’s

curriculum. Secondly, the original intentions and motivation behind the study will be

described, including the situation concerning student motivation to study EFL, the principles

of Gardner’s theory of Multiple Intelligences and finally the reason why it was decided to

employ an analysis tool to identify those intelligences. Thirdly, the results to date will be

presented, including the method of data collection from the students, the findings in terms of

their intelligence profiles and finally an analysis of the findings and their potential

consequences in pedagogic application.

Department Curriculum & Year-Group Structure

The degree of Automotive Engineering offered by the department of Vehicle Technology

covers eight semesters over a four year period for an undergraduate and is based on the

principles of Project Based Learning (PBL), a theme which has been elaborated upon in

previous ASEE conference papers (Bischof et al., 2007; Bratschitsch et al., 2009).

Briefly, the degree’s structure is broken into three distinct project phases, which take place in

the 2nd

and 3rd

semesters (phase 1), the 5th

and 6th

semesters (phase 2) and the 7th

semester

(phase 3). In each phase, the subjects in the syllabus are designed to compliment each other

and motivate student learning, and the English as a Foreign Language (EFL) programme is

designed to support and correlate with each phase including the project objectives defined for

the students.

Due to the rigorous nature of the degree programme curriculum (30 contact hours per week,

plus individual study), students are divided into year-groups according to their starting year

and are presented with a predetermined timetable. Students cannot select when a subject

required for graduation may be taken, rather it is prescribed in the timetable, which is defined

at the beginning of each semester. For example, a student graduating in 2010 would have

begun their study in October 2006 and is therefore assigned to “year-group 2006” and follows

this year-group’s timetable throughout the duration of their studies. A student may be required

to repeat a year if examination results are unsatisfactory, in which case he or she moves into

the subsequent year-group. Flexibility of subject choice is reflected within the projects

undertaken by undergraduates in the 2nd

through 6th

semesters, the industrial placement (in the

7th

semester) and finally in the choice of diploma thesis.

Page 15.574.3

English in the Department of Vehicle Technology

As well as the courses which would be expected in such a degree programme (for example in

areas such as mathematics, computer aided design, strength of materials, thermodynamics

etc.) English as a Foreign Language (EFL) classes also make up a major component, being a

mandatory subject in the syllabus, required in six out of the eight total semesters of study.

The reason behind the choice of EFL classes integrated into a technical degree is clear in the

context of the English language’s status as the lingua franca of the global workplace where

graduates must be able to function. Of course, English is not generally a student’s native

language as most are from an Austrian high school background. The next section of this paper

will briefly outline the EFL programme which has been put in place for students in the

context of the overall Automotive Engineering degree programme curriculum and will then be

followed by an analysis of student motivation to learn a second language (L2) within such a

technical environment.

Major employers of Vehicle Technology department graduates include companies within the

Magna International umbrella, AVL List, Siemens, as well as automotive OEMs, suppliers

and others. Companies such as these, obviously, do not restrict their activities to the German

speaking area of central Europe and with this in mind, the EFL programme has been

specifically tailored to produce graduates with the requisite English skills to function

effectively on such an international stage.

The EFL programme begins in semester 2 of the undergraduate’s study and comprises 2

semester-week-hours (equivalent to 2 ECTS points) or roughly 50-60 hours of invested

student work time, including two 45 minute teaching units per week. The same structure is

used in 3rd

, 4th

, 5th

, 6th

and 8th

semesters. The 7th

semester does not contain any language

tuition, as this semester is reserved for a mandatory industrial placement, supervised both

internally within the department and externally within a suitable company (Bratschitsch et al.,

2009).

The Austrian Ministry of Teaching, Arts and Culture states that any high-school graduate will

possess a B2 level of Englishi according to the Common European Framework of Reference

(CEF), which would enable the speaker to function effectively and ‘present clear, detailed

descriptions on a wide range of subjects related to my field of interest’ (CEF). However, our

experience has found that often students joining us who have a technical focus have neglected

their EFL learning, while being strong in technical areas and therefore need additional

assistance to (re)acquire basic language skills. In addition, the EFL programme is also

designed to teach English in specific subject areas relating to the automotive industry and the

international workplace which were not covered during high school classes.

Due to these facts, the EFL programme is divided into three distinct subsections: General

English (1 & 2) taught in semesters 2 and 3, Professional English (1, 2 & 3) taught in

semesters 4, 5 and 6 and finally Project Work and Presentations, taught in the final semester

(8).

The purpose and concept behind General English is a “refresher” - a review of high school

level materials, with the objective of bringing all students to the B2 level. At the same time, it

also corresponds in content to the 2nd

and 3rd

semester projects, which are based in the

Page 15.574.4

concurrently taught Informatics lectures, where presentations and any documentation

produced must be done in English.

Professional English courses from the 4th

to 6th

semesters of study focus on the global

workplace and examining areas such as corporate structure, report and writing, corporate and

international cultural differences, resume and job application language, meeting and

negotiation techniques, interviewing techniques. In addition, students are prepared for the 6th

semester, where the language of instruction and examination in all subjects on the degree

programme is English.

Project Work and Presentation in the 8th

semester focuses on the students’ academic writing

(for example abstracts) as well as the students’ presentation techniques and language for their

final thesis defence, as this takes place in front of an academic senate and must be in English.

Student Motivation

Students of the Vehicle Technology degree programme at the FH Joanneum University of

Applied Sciences can find themselves in an unusual position, when one compares their degree

with other engineering tertiary level study options, even within the same city. This position is

that despite the fact they have chosen to undertake an essentially technically based study path,

they are still confronted with a language learning element, due to the department’s and

institution’s belief that basic competence in the English language is a prerequisite for the

modern automotive engineering graduate. This is not always the case at comparable

institutions.

While motivation for the course, even in the “difficult” subjects such as Mechanics, tends to

be high, very often students pose the question (usually in the first EFL lesson taught) if there

is not a way for them to accredit the course, as they consider other areas to be more central to

their study. Clearly, at this early stage of their academic career, the important role of the

English language has not yet become clear to them – as the language of communication

between international companies and also the language in which the majority of academic

reports are written (and in the student’s own case will have to be written).

And yet, as Dörnyei and Czizér noted (1998), for teaching to be effective it is necessary to

motivate learners and as Robert Gardner noted in a keynote address on motivation, students in

Japan are often highly enthusiastic about learning English – an enthusiasm which wanes and

does not last the first full year of study (Gardner, R., 2001, p.2).

However, in the case of the department of Vehicle Technology, there is often no such luxury

of such initial motivation. It is often first necessary to convince the students that instruction is

indeed something worthwhile pursuing. The insistence, therefore, that learners participate in

the department’s six semester EFL programme can understandably lead to the opposite of

motivation, a reaction perhaps quite reasonable when considered from the student’s own less

aware perspective.

The approach that has been taken towards the issue of low student motivation in the EFL

programme in the department of Vehicle Technology has its grounding in the thinking of

Robert Gardner’s concepts of integrative and instrumental orientations to motivation (in

Dörnyei, 2001, p.49) as well as Zoltan Dörnyei’s concept of the difference between

motivation and motivating (Dörnyei, 2001).

Page 15.574.5

The following are the tasks of the teacher in the EFL classroom, which are combined with

Gardner’s (in Dörnyei, 2001, p.49) orientations:

� instrumental – the pragmatic application of course material, or from the student’s

point of view, the usefulness of the materials for the “real world”

and

� integrative – the inherent motivation on the part of the student to learn the language

due to their desire to become part of the target language’s culture.

The EFL programme’s course content has been created very much with the concept of the

instrumental orientation in mind. Materials and exercises are intended not only to mirror the

“real world” but also be useful during and after the degree course has been completed. For

example, the writing of documentation is practiced when students are required to produce

software manuals for their Informatics projects and student’s are required to put together an

online résumé as they near the end of the degree course, which may benefit their chances of

employment (Millward-Sadler et al., 2009).

Indeed, with the department’s PBL didactic approach and close links with industry,

instrumental motivation in Vehicle Technology students is generally high when these links

are apparent to the students. The integrative orientation, however, is much more difficult to

achieve as it does not come from the student. In fact, very often, the motivation behind a

student’s choice to study Automotive Engineering is entirely unattached with aspirations to

study any foreign language (or indeed any subject from the field of the arts). The culture that

students are interested in integrating into is not a foreign language culture, but rather in many

respects a corporate engineering culture.

Hence it becomes necessary to tailor the EFL course content in such a way that it obviously

reflects its relevance to the students. Additionally, this also puts the onus on the teacher to be

the provider of motivation, or rather to be the motivating factor. According to Dörnyei (2001)

in terms of learning a second language:

[…] motivation can be defined as the dynamically changing cumulative arousal in a

person that initiates, directs, coordinates, amplifies, terminates and evaluates the

cognitive and motor processes whereby initial wishes and desires are selected,

prioritised, operationalised and (successfully or unsuccessfully) acted out.

Motivating, on the other hand, is a pivotal task of the teacher if they are to be effective in the

classroom. To this end, Dörnyei sets out four strategies necessary to be successful in this area

(2001, p.119):

� creating basic motivation conditions

� generating student motivation

� maintaining and protecting student motivation

� encouraging positive self evaluation

It is clear that the EFL curriculum must comprise a multifaceted approach to achieve these

motivational conditions and, to this end, various areas have already been investigated and

addressed in previous ASEE conference papers, such as the use of internationalisation within

the department (Casey et al. 2008) as well as the implementation of web technologies to

Page 15.574.6

compliment the “net generation’s” learning (Millward-Sadler et al. 2009). These strategies

have been employed in an attempt to raise motivation for the EFL programme (while at the

same time not distracting from other subjects).

The remainder of this paper will focus on another study currently in progress, which aims to

identify students’ strengths, or main intelligences, so that course materials may be adapted to

closely match the preferred learning style of the students, thus promoting success and in turn

motivation through the application of the theory of Multiple Intelligences.

As already noted, many students choose to study a technical degree as they are following their

strengths (and not their weaknesses) from high-school. Thus, by presenting these students

with an EFL programme, in many cases the first two points of Dörnyei’s strategy for

motivating learners have already been breached (thereby also negating the other two). If the

basic conditions for motivation are subjects other than a language, then this condition cannot

be met. Likewise, generating motivation in a student for a subject disliked during secondary

education can be a challenge.

The presence of an obligatory attendance requirement for all lessons (made mandatory by the

institution’s exam regulations) both has the effect of demotivating the student, but also

ensuring that the student is physically present. From this point, the instructor has a fighting

chance.

The premise of this study was to find a new way of generating student motivation, by

changing the modes of input of learning materials to suit the students’ preferred learning

styles. By identifying these through the theory of Multiple Intelligences and adapting course

materials, it is hoped motivation can be further increased, thereby also affecting areas such as

willingness to integrate, which would further promote success.

The Theory of Multiple Intelligences

The theory of Multiple Intelligences was first published in 1983 in Howard Gardner’s work

Frames of Mind and since then, numerous updates have been made by Gardner himself to

theory, as well as there having been much conjecture both positive and negative by scholars

and classroom practitioners around the world.

Gardner’s notion of intelligence differs from the traditional 20th

century Western view, in that

it proposes numerous facets that make up the whole, unlike the principle of “g” and general

intelligence (Spearman, 1920, 1927). For each of the intelligences, Gardner proposes a set of

criteria that must be fulfilled in order for the intelligence to be considered a ‘full-blown’

intelligence (Gardner, 2006, p.21). The criteria are as follows:

1. Brain damage may affect and disrupt one intelligence, while leaving the others fully

functional.

2. An intelligence will have its ‘idiot savants’ – those who display prodigious talents in

one area, while remaining idiots in all others.

3. An intelligence will have a core set of operators – an initial talent demonstrated in that

area.

4. An intelligence will develop over time.

5. Generally speaking, an intelligence will be codifiable using a symbolic system – for

example the use of an alphabet to represent language.

Page 15.574.7

In the theory of Multiple Intelligences, there are either seven or eight intelligences which

make up an individual’s composition and although Gardner added the eighth in 1999 and it

still has not been adopted by many paracticioners. The following section briefly details these

eight, which were all included in the current study.

Intrapersonal

This intelligence is reflected in those who work well by themselves and are capable of self-

reflection and self-awareness.

Interpersonal

This relates to a person’s ability to understand and be aware of other people and interact well

with them. Successful negotiators (in a variety of situations) are individuals who possess this

intelligence.

Logical-Mathematical

Often considered to be “scientific” intelligence, those strong in this area make calculations,

quantify and analyse to solve problems. Engineers, scientists and accountants all display

strengths in this category.

Linguistic

This relates to the person’s ability to manipulate words and language and express themselves

articulately through the medium. Typically authors and poets demonstrate linguistic strengths.

Musical

This refers to a person who is capable of “thinking” in terms of melody, pitch and tone.

Composers and conductors are some examples of those with this intelligence.

Visual-Spatial

Often thought of as the “artist’s” intelligence, the visual-spatial intelligence refers to the

ability to see in the mind’s eye and imagine scenarios and even modify them internally. The

painter or sculptor is the obvious candidate who displays this intelligence in abundance,

although this is also relevant in the world of design.

Kinaesthetic

A person who displays strength in this area is well in tune with their body and is able to

control their physical skills. Individuals who excel in sports as well as musicians playing their

instruments (such as a piano) are strong in this area.

Naturalistic

The most recent addition to the bank of intelligences, a person with strengths in this area is

capable of recognising forms in nature and understanding nature’s systems. Agriculturalists

and hunters possess this strength.

Page 15.574.8

In addition to these eight intelligences, the so-called “candidate” intelligences of spiritual and

existential intelligence were not included in this study, as these have not been fully endorsed

by Gardner, due to their failure to fulfil all the criteria to be considered intelligences (Gardner,

2006).

One criticism of the theory of Multiple Intelligences has been that these eight areas do not

represent intelligence(s) at all, but rather some of them represent talents or abilities. However,

in this case, it is a moot point, as the agenda underlying this exercise is the facilitation in

instruction of students so that they may better themselves. Should this be achieved through the

application of a theory using the terminology of “intelligences” or “talent” is quite irrelevant,

should the original aim be successful.

The application of the theory of Multiple Intelligences for classroom use is not new and

indeed, practitioners were applying the theory to entire schools as early as the mid 1980s

(ibid.). There is a plethora of material available in textbook format, which aides the teacher in

modifying their lessons to suit the intelligence they have chosen and it was conceived that the

theory’s introduction into Automotive & Railway Engineering classes would follow the same

blueprint.

Why Multiple Intelligences?

It must have been noted by every teacher who has crossed the threshold into the classroom

that very often some students do not share the same level of enthusiasm for their subject as

they do. Furthermore, in many of cases, we hear that enthusiastic teachers pass on their

enthusiasm to the students, thereby motivating them and inspiring them to progress and

succeed in their subject. Those students who do not succeed are very often considered to be

unsuited to that particular subject, or perhaps the deflated, unmotivated teacher is to blame.

However, as Puchta and Rinvolucri (2007) note in their recent textbook for teaching Multiple

Intelliegnces in the classroom, a survey of language teachers showed that only approximately

20% of them were good students of maths at school. This leaves four fifths of language

teachers who must have suffered at the hands of uninspiring, deflated, poor mathematics

teachers – unless of course, other underlying reasons for the language teachers’ deficiency at

mathematics can be identified.

Clearly, a central argument of the theory of multiple intelligences is that each individual

differs in their cognitive make up, thus each having their own very distinct strengths and

weaknesses in each of the eight intelligences. Naturally, in light of the theory, it is easy to

suggest that language teachers are strong in linguistic intelligence, while weak in the logical-

mathematical area. Is it then the case that we are all predetermined by our basic cognitive set

up to be restricted to a certain field of inquiry and work? Or could it be possible to, through an

adaptation of learning materials and/or curriculum to make mathematics more accessible for

the poor language teacher? This paper argues that the answer to the latter question is ‘yes’, if

the proper intelligence profiles can be identified for the students, in this case not language

teachers, but rather engineering students (or theoretically the mathematical adept) who are

required to learn a foreign language to an advanced level.

By engaging such students through their strengths rather than their weaknesses, it was

believed that the application of Multiple Intelligences to course material creation would not

only increase student motivation in an area which may be perceived as irrelevant or daunting,

but also improve their language skills both in terms of examination grades and also practical

usage outside the classroom in the “real world”, while also reducing their language anxiety.

Page 15.574.9

Indeed, research carried out under the auspices of Harvard University’s Project Zero into

Multiple Intelligences potential in adult education suggests that in terms of motivating

students, the application of Multiple Intelligences in classroom instruction had a positive

effect and engaged students, in some cases with significantly higher incidences of attendance

(Kallenbach et al., 2002). This only served to back up the earlier findings of Project Spectrum,

that the application of Multiple Intelligences theory increased motivation of students (Gardner

et al., 1989).

Certainly, as Dörnyei and Csizér (1998) note ‘skills in motivating learners are central to

teaching effectiveness’ and therefore in a subject area where student motivation can quite

understandably be low, it is a very necessary obligation on the part of the course instructor to

carefully consider the means of imparting course content to students.

It should not be forgotten, that English language instruction is a required element of the

engineering degree programme and students cannot avoid the subject (and examination)

should they want to graduate. As such, it would therefore seem prudent to ensure, as far as

possible, that the curriculum for English language tuition is presented in a way that students

of engineering find palatable. As Howard Gardner (2006) notes, schools tend to employ

uniform methods when teaching their pupils and this is generally seen as fair, as all pupils are

treated in exactly the same way. However, Gardner contends, as does this paper, that this does

not take students’ intellectual strengths and weaknesses into account, but rather favours the

linguistic and logical-mathematical intelligences. Moreover, on the basis on Puchta and

Rinvolucri’s previously mentioned observations, it would also seem plausible to assume that

students who have selected an engineering degree have done so for exactly the same reason as

those language teachers no longer engage in a subject field that has any relation to

mathematics. In this case, those language teachers would apparently be much luckier than

students of Automotive Engineering, as they have been able to delete their weaknesses, while

focussing on their strengths. Therefore, the decision was taken to take steps to identify

students’ strengths, even in subject areas, where such strengths may not be evident. Once

those strengths were identified, course materials and curriculum may be altered to better suit

the students in question.

In order to achieve this objective, students from the degree programme were asked to

complete a questionnaire (which can be found in appendix A), which had the specific

intention of determining individual and group preferences for learning input. The methods of

data collection and the results and their interpretation are dealt with in later sections of this

paper.

While the rationale behind the intention to increase student motivation in an area where it may

be perceived as low is presumably clear; the reason behind the choice of a self analysis tool

for the students to identify their MI strengths (and weaknesses) may not be as transparent.

Research in the 1970s and 1980s (Dunn, 1984; Reinert, 1976) of U.S. school children came to

the conclusion that there are four basic learning channels: visual, auditory, kinaesthetic and

tactile. As outlined in the previous section, the theory of multiple intelligences covers these

learning channels and supplies a more detailed insight into why students may make certain

selections accordingly to their innate cognitive setup.

Further research from Dunn (1984) demonstrates that students who are provided with such a

tool will, for the most part, correctly identify their own particular strengths and weaknesses,

Page 15.574.10

especially in areas they feel strongly about (R. Dunn, 1984). This parallels previous doctoral

research by B.J. Farr in 1971, which found that students accurately determined their own

learning preferences using a self-analysing tool (in Reid, 1987). Furthermore, work by George

Domino has found that students who are taught in their preferred learning styles tend towards

higher scores in tests than those who do not receive input in this manner as well as showing a

‘very definite interaction between student achievement and teaching style’. (Domino, 1971).

While Domino was focussing only on the difference between learners who preferred to

conform and those who preferred to work independently, this paper argues that the broader

base of MI would allow for a group or year-group specific adaptation of course materials,

which would enhance student motivation by increasing student chances of success in the

subject.

The possibility to make the analysis a classroom language activity itself was also considered

as an additional benefit.

Data Collection

The original intention of the exercise was simply to define students’ general strengths and

weaknesses according to Gardner’s MI model and adapt course materials for Professional

English courses accordingly. For this reason, data were collected anonymously in normal

class groupings and results kept according to these class groups for later analysis.

Data regarding student intelligence profiles was collected using an adapted questionnaire

developed by Michael Berman (1998, pp.5-6). An example of the adapted questionnaire used

can be found in appendix A. The questionnaire consisted of eight sets of six statements,

grouped according to the intelligence they represented. The questionnaire was modified from

its original form for the purposes of readability and also student comprehension, as the target

group would not be native English speakers.

The intelligences which the questionnaire sought to identify were the same eight which have

been previously outlined in this paper: linguistic, mathematical-logical, interpersonal,

intrapersonal, naturalistic, musical, kinaesthetic and visual-spatial. Statements referring to the

various intelligences were grouped together and identified by a number and not name, as the

intention was to avoid any possible bias towards a particular intelligence. Students rated each

statement on a scale of 0 – 2 in relation to their perceptions of themselves. The questionnaire

was answered using the following rubric:

0 - this does not describe me at all,

1 - this is sometimes / somewhat descriptive of me

2 - this is very descriptive of me

Analysing a statement (for example: I hum lots of tunes) in a bank of six such statement

designed to reflect the qualities inherent to that intelligence (here musical), each student built

up a scaled score relating to their intelligencse, thereby demonstrating whether it was a

particular strength or weakness and building an intelligences profile. The maximum score that

a student could rate an intelligence with was 12 (6x2) and the minimum was 0 (6x0). The

resulting set of eight scores provided a clear indicator through a final set of scores of which

intelligences individual students favoured, as well as building up a picture of which

intelligences were favoured by a particular group. An example of a completed questionnaire

can be found in appendix B.

Page 15.574.11

As the objective of the activity was to identify in which intelligences student strengths lay,

whether they scored themselves highly or not in comparison to their peers was of less

relevance. The most important aspect for the redesign of teaching materials was to determine

each group’s strengths and adapt materials accordingly.

The questionnaires were administered over a period of a year-and-a-half (December 2006 –

April 2008) to students of the Automotive Engineering degree programme from three

different year-groups: YG-2004, YG-2005 and YG-2006, as well as to a group of students

from the Karl-Franzens University Institute for Theoretical and Applied Translation Studies,

who acted as a control group. These students were chosen as a control group, specifically as it

was assumed that they would not perceive themselves as undertaking a technical degree

programme or career and therefore the questionnaire itself could be tested to ensure it did not

regularly produce identical results.

As the language of the questionnaire was English, it was also necessary to teach four

vocabulary items and monitor the students closely to ensure their proper understanding of the

statements and thus avoid erroneous data stemming from language miscomprehension.

Questionnaires were administered exclusively to students who had already completed their

third semester of study, as the first three semesters see a high number of students drop out of

the degree course for various reasons. Thus, a high proportion of questionnaire respondents

would also become graduates, which in turn suggests that they employ appropriate and

successful learning strategies. Likewise, the control group of translation students were all

from their sophomore year, as by this time a change of major or discontinuation of degree

course was deemed to be significantly less likely.

The numbers of students who received the questionnaire were as follows:

YG-2004: n = 31

YG-2005: n = 30

YG-2006: n = 27

Control: n = 11

The number of students is relatively low, but in the case of Automotive Engineering students,

represents the majority of students in that year-group of the course (approximately 30-35

graduate from each year-group).

Page 15.574.12

Results & Analysis of Data

Table 1: The following table shows the data collected from 31 students from YG-2004, with

the group mean in the final row:

Ling. Kinest. Logical Musical Spatial Interpers. Intrapers. Nat.

8 11 8 9 8 10 8 6

5 4 11 7 6 7 6 9

9 7 9 2 8 9 7 6

8 6 9 9 5 7 6 6

7 8 7 2 10 10 7 8

6 9 10 0 10 9 6 7

9 10 11 6 6 11 4 5

5 9 8 7 9 8 7 8

6 9 10 5 7 9 5 3

6 9 7 1 11 12 6 3

9 10 11 7 11 10 9 2

8 9 10 4 6 9 8 6

8 11 11 12 9 9 8 3

6 6 5 2 7 10 8 3

YG04 4 8 12 4 8 10 8 0

6 9 10 3 10 11 8 6

7 10 9 4 6 10 6 9

10 10 8 5 9 11 9 8

6 7 5 0 8 6 3 5

7 6 10 4 6 9 8 0

4 6 9 3 10 8 9 3

6 8 7 5 6 8 6 8

8 8 8 3 8 8 8 7

8 6 7 2 6 6 9 6

7 6 7 0 6 10 4 7

7 7 7 0 6 8 7 3

10 9 6 6 10 11 9 2

7 10 8 10 6 7 6 1

9 7 6 6 10 10 6 2

7 7 7 2 8 7 5 1

3 10 4 7 3 3 8 3

Mean 6,9677419 8,1290323 8,2903226 4,4193548 7,7096774 8,8064516 6,9032258 4,7096774

It should be noted that all 31 respondents to this survey graduated the degree programme.

Differences throughout the group can clearly be seen, with only four members of the year-

group showing a strong predilection (>9) for musical intelligence and yet fewer (only two

students) for naturalistic intelligence. On the other hand, over 60% of students in the year

group clearly favour the interpersonal intelligence (>9).

This year-group’s mean would suggest that interpersonal, logical, kinaesthetic, spatial,

linguistic and intrapersonal intelligences were preferred by the students in that order. Each

intelligence was rated >6. Both naturalist and musical (in that order) were rated weakly (<5).

Page 15.574.13


the mean score in the final row:


8 9 8 12 9 7 6 7

6 7 6 3 7 7 7 3

4 2 8 7 6 11 6 1

8 10 5 4 8 12 8 1

5 7 10 2 6 9 5 6

9 8 8 7 6 10 6 4

9 10 8 10 9 7 7 8

8 10 10 8 8 10 7 5

7 4 10 9 7 8 6 3

5 5 8 2 7 7 9 7

7 4 10 10 8 8 6 4

6 8 8 6 9 9 5 8

7 9 6 12 8 9 7 3

4 8 5 2 8 6 8 7

YG05 5 7 10 0 7 9 7 7

7 9 9 6 7 10 3 3

7 7 10 8 4 8 6 5

4 9 9 10 6 5 6 4

6 8 9 0 11 8 7 3

6 8 8 3 7 8 8 5

6 10 8 4 7 6 7 10

5 8 7 10 6 9 7 8

7 10 9 2 9 10 6 5

8 9 5 5 9 6 8 7

4 6 7 5 10 9 9 3

7 6 5 6 2 8 6 4

6 11 9 5 9 11 9 6

6 9 8 9 9 9 3 4

9 9 9 7 3 9 8 5

4 7 7 2 7 7 7 6

Mean 6,3333333 7,5483871 7,7096774 5,6774194 7,0645161 8,1290323 6,4516129 4,9032258

Of this group, all except for one student has graduated at the time of going to press, which

gives a success rate of over 96%.

The overall mean of this year-group shows that the students generally assessed themselves

using slightly lower scores than their colleagues from the 2004 year-group. Nevertheless, the

strongest intelligence remains interpersonal with exactly half of respondents (15 of 30) rating

it strongly (>9). The picture regarding weak intelligences from the previous year-group

(2004) is once again repeated, albeit with a larger number of students (8) showing predilection

(>9) for musical intelligence. The mean rating of this intelligence, however, still places it

lowly amongst the year-group’s overall strengths.

This year-group’s mean would suggest that interpersonal, logical, kinaesthetic, spatial,

intrapersonal and linguistic intelligences were preferred by the students in that order. Each

intelligence was rated >6. Musical intelligence was rated above naturalist, although both were

rated weakly in the mean (<6).

Page 15.574.14




9 10 6 8 5 6 7 0

11 8 8 12 6 7 5 7

6 10 7 3 6 8 8 4

7 7 7 3 8 8 5 4

4 8 5 1 5 7 6 5

6 9 10 1 11 7 7 3

7 5 11 9 8 8 6 4

8 8 8 4 7 7 7 8

7 10 4 0 7 7 7 3

7 5 9 10 6 9 5 8

8 7 8 7 6 11 7 4

9 11 8 3 5 12 8 2

4 7 8 5 7 7 6 5

8 10 7 6 10 9 7 8

YG06 5 10 6 4 7 4 9 6

6 11 10 5 10 9 4 3

5 6 10 7 8 8 7 1

5 10 8 3 8 10 6 6

3 12 5 4 3 12 2 2

5 6 11 5 7 7 5 2

9 4 6 4 5 8 6 5

7 9 7 2 7 10 6 3

8 9 8 5 6 11 8 3

11 7 8 8 8 9 5 3

8 6 9 0 9 8 8 9

7 6 5 10 4 8 8 3

9 9 12 6 6 9 7 5

Mean 6,0967742 7,0967742 6,8064516 4,3548387 5,9677419 7,2903226 5,5483871 3,7419355

These students will not graduate until September 2010, therefore no information will be

available in this paper in this regard.

The overall mean of this year-group shows that the students generally assessed themselves

using scores of, on average, nearly a point lower than their colleagues from the 2005 year-

group and even more in comparison to the 2004 year-group. Once again, the strongest

intelligence is interpersonal with the highest mean and 11 of 27 respondents rating it strongly

(>9). The picture regarding weak intelligences from the previous year-groups (2004 & 2005)

is once again repeated, although this year-group only had four members who responded

positively towards musical intelligence (>9) and only one respondent who classified

themselves as having a strong (>9) naturalist intelligence.

This year-group’s mean would suggest that interpersonal, kinaesthetic, logical, linguistic

intelligences were preferred by the students in that order. Each intelligence was rated >6.

Spatial, intrapersonal and musical intelligences were rated above naturalist (in that order), and

all were rated weakly in the mean (<6).

.574.15

Table 4: The following table shows the data collected from the 11 translation students, with


Linguistic Kinest. Log/Math. Music Spatial Interpers. Intrapers. Naturalist

7 7 4 5 5 8 7 2

6 5 9 8 9 9 6 4

7 5 4 7 7 6 8 7

7 12 8 5 9 11 11 3

6 2 6 4 6 7 9 2

9 5 7 10 5 8 9 2

9 8 7 11 7 9 4 3

8 2 4 3 5 7 7 1

7 3 5 3 5 7 6 2

8 4 9 6 4 11 8 5

9 6 5 10 6 9 5 2

Mean 7,545455 5,363636 6,181818 6,545455 6,181818 8,363636 7,272727 3

There is no information on when (or if) these students will graduate / have graduated.

With the exception of the most and least favoured intelligences (interpersonal and naturalist)

the translation students’ strong intelligences differed significantly from any of the groups

from the Vehicle Technology department.

Unsurprisingly, for students studying language, the linguistic intelligence featured strongly, as

did the intrapersonal and musical intelligences. This stands in stark contrast to the preferences

of the students who had chosen a more technical path. The logical intelligence is rated much

lower by this group, which is also unsurprising given the chosen career path.

The major differences in the apparent multiple intelligences profile of the translation students

would suggest that the questionnaire itself is not the reason for the similarity of the results

accrued from the 88 students of Automotive Engineering.

Comparison of students’ mean multiple intelligence profiles

The following bar-charts represent a graphic illustration of the mean values calculated from

each group. The overall similarities in strong and weak intelligences between the different

Vehicle Technology year-groups are clearly visible, as are the striking differences in the mean

intelligences profile produced by the translation students.

Page 15.574.16

Chart 1: YG-2004

0

1

2

3

4

5

6

7

8

9


JG2004

Chart 2: YG-2005

0

1

2

3

4

5

6

7

8

9


JG2005

Page 15.574.17

Chart 3: YG-2006

0

1

2

3

4

5

6

7

8


JG2006

Chart 4

0

1

2

3

4

5

6

7

8

9

Linguistic Kinest. Log/Math. Music Spatial Interpers. Intrapers. Naturalist

Translation Students

Page 15.574.18

Table 5: The following table ranks the intelligences in order of the mean scores as assessed by

each group:

YG04 YG05 YG06 Trans.

1 Interpersonal Interpersonal Interpersonal Interpersonal

2 Logical Logical Kinaesthetic Linguistic

3 Kinaesthetic Kinaesthetic Logical Intrapersonal

4 Spatial Spatial Linguistic Musical

5 Linguistic Intrapersonal Spatial Logical

6 Intrapersonal Linguistic Intrapersonal Spatial

7 Natural Musical Musical Kinaesthetic

8 Musical Natural Natural Natural

Analysis

While the study is by no means large enough to constitute a proper quantative analysis, the

following patterns appear to be emerging.

In all cases, the strongest intelligence displayed by all students is that of interpersonal

intelligence. This would suggest that we are still far from the days where individual learning

(for instance, via e-learning) could dominate to the exclusion of all other types of tuition and

also indicates that pair and group work is an essential ingredient in successful learning which

should be reflected in a degree programme’s curriculum.

That logical-mathematical intelligence features highly for technical students, and much less so

for students from the arts, adheres to the hypothesis that strengths in certain intelligences

correlate to abilities in particular disciplines (in this case a technical one). The challenge

becomes how to adapt activities for an EFL classroom to make the subject more acceptable to

the student’s preferred learning channel. In comparison, that a translator, before graduation,

already displays strong linguistic ability is also unsurprising. In the same vein, it is not

surprising that the technically oriented students rate the linguistic intelligence lower, between

fourth and sixth places. This low rating of the intelligence may well correlate to a dislike /

demotivation in terms of language when taught in more traditional linguistic manners, such as

the student may have experienced at school.

The relatively high ranking of the kinaesthetic intelligence amongst technical students also

finds parallels in the concept that this intelligence is favoured by those who like to be “hands-

on” in their work. Of course, while not an essential to become an automotive engineer, many

undergraduates are relatively skilled mechanics and car enthusiasts before they begin their

studies. This intelligence can be accessed in the EFL classroom through activities which

require the use of hands or physical movement around the classroom.

Page 15.574.19

Spatial intelligence relates to the ability to visualise both internally and externally. In the days

before CAD, this would have been an essential for technical drawing and design, however

with the advent of more powerful computers and CAD software packages, this is now less

relevant and perhaps the reason for the mediocre showing in all four groups. In any case, this

is a relatively easy intelligence to access through the creation and use of visuals, both by the

teacher and student.

The intrapersonal intelligence is not rated particularly highly by automotive students,

suggesting that while they can work alone, it would be preferable to have them work in

groups or pairs (interpersonal), particularly in the less comfortable setting of the EFL

classroom. This point also correlates to the strongly influenced PBL approach in the Vehicle

Technology degree programme, suggesting interpersonal skills are required to be successful.

Finally the naturalistic and musical intelligences were rated lowly by all three technically

oriented groups. In order to facilitate an increase in motivation in the EFL classroom,

therefore, the study would suggest that activities such as playing songs should be avoided, as

generally this would be detrimental to overall group motivation.

Conclusion

These results represent the first part of a wider survey being carried out to investigate

successful intelligence profiles of engineering students. Future work will continue to focus on

potential similarities of intelligence profiles of engineering students of various disciplines, as

well as taking a larger proportion of non-technical students into account for control purposes.

What began as a simple exercise to determine a single class of students’ strong intelligences

has potentially identified an intelligence profile which correlates to successful learners in a

vehicle technology degree where the PBL didactic method is used. This intelligence profile

was shown to be quite different to an intelligence profile generated by learners of a university

translation course.

The results of the three year-groups investigated from the department of Vehicle Technology

displayed particular tendencies towards certain intelligences, in particulare, their strengths

would appear to lie in interpersonal, mathematical-logical and kinaesthetic intelligences in

particular.

Through analysis of these intelligences, it is possible for instructors to amend and adapt their

materials, particularly in non-technical subjects such as in the EFL programme. It is hoped

that through these measures such subjects will become more accessible to students, thereby

increasing the chances of improved academic achievement in the subject area as well as

motivating the student.

Not only do these results aid in identifying how an instructor can modify their input material,

but also demonstrate how they might avoid some of the pitfalls that first created demotivation

in students in the subject previously. In this way, it is hoped that motivation will increase,

academic achievement will increase as will the quality of the graduate student and thereby

their employment opportunities.

Page 15.574.20

Appendix A

Page 15.574.21

574.22

Appendix B

Page 15.574.23

574.24

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