factors influencing successful achievement in contrasting design and technology activities in higher...

Factors Influencing Successful Achievementin Contrasting Design and Technology Activitiesin Higher Education

STEPHANIE ATKINSON

Reader Design and Technology Education, School of Education and Lifelong Learning, Forster

Building, Chester Road, Sunderland SR1 3SD (E-mail: [email protected])

ABSTRACT: The aim of the study was to investigate the relationship between suchfactors as learning style, gender, prior experience, and successful achievement in con-trasting modules taken by a cohort of thirty design and technology trainee teachers duringtheir degree programme at a University in the North East of England. Achievement datawere collected from three design and three electronic modules at levels 1, 2 and 3. Dataconcerning appropriate, previous experience before starting the course was obtainedthrough a short questionnaire. The learning style of each member of the sample wasascertained using the Cognitive Style Analysis test. The findings from the study indicatedthat the learning style groupings were not as expected. A positive relationship betweenachievement and past experience in both electronics and design activity was found, al-though improvement for those with no prior experience in comparison to those withprevious experience was only evident in electronics. A concern arising out of the data wasthe differences in terms of achievement between male and female students and also thedifference in achievement when learning style and gender were scrutinised. The implica-tions of the findings in relation to the success of the trainees as impending teachers ofdesign and technology were discussed. The problems associated with the small cell sizecaused by splitting the sample by the three variables was acknowledged and a suggestionwas made that further study would be required to ascertain whether the gender andlearning style differences witnessed in this study would be replicated in a larger sample.

Keywords: learning style, design and technology achievement, gender, prior learning, design

and technology teacher training

INTRODUCTION

The aim of this study was to investigate the relationship between factorssuch as learning style, gender and prior experience, and successfulachievement in contrasting design and technology modules taken by thirtytrainee teachers (16 male and 14 female) studying on an Initial TeacherTraining (ITT) Design and Technology Education degree programme at aUniversity in the North East (NE) of England. This data were collected inorder to see whether a better understanding of the relationship between thefactors could help the academics concerned improve the teaching andtherefore the learning of individual students taking their modules.

Successful achievement in the context of this article refers specificallyto marks awarded for assignments set to assess that students hadachieved the learning outcomes of the module they were studying. All

International Journal of Technology and Design Education (2006) 16:193–213DOI 10.1007/s10798-004-8320-7 � Springer 2006

assignments used in the study had been both internally and externallycross-moderated to assess validity and reliability of the marks awardedand meet all the University Quality Assurance requirements. In this studythe achievement data were collected from three design and three elec-tronic modules at levels 1, 2 and 3. Data concerning design and tech-nology experience prior to starting the course were obtained through ashort questionnaire. In order to ascertain the trainees preferred learningstyle they were given the Cognitive Style Analysis (Riding 2002a) tocomplete. This assessed two fundamental dimensions of learning style,Wholist–Analytic (WA) and Verbal–Imagery (VI). The sample was foundnot to have subjects at the extremes of either dimension. This wasparticularly noticeable at the Wholist end of the WA dimension andthe Imager end of the VI dimension, although there were found to bemore Imagers than Verbalisers and significantly more Analytics thanWholists.

The possible implications of this finding are discussed along with thediffering level of success achieved by trainees in design modules and elec-tronic modules when grouped by learning style, gender and past experiencein design and technology related activities. The inference of the results inrelation to the success of these trainees as impending teachers of design andtechnology is also examined.

BACKGROUND TO THE STUDY

Initial teacher training national curriculum requirements

In order to teach in State Schools in England, trainees must gain QualifiedTeacher Status (QTS). To obtain this they collect evidence to show that theirteaching has met stringent Teaching Standards set by the Governmentthrough the Teacher Training Agency (TTA 2002). These Standards indi-cate what trainee teachers must know, understand and be able to do beforethey can achieve QTS. With regard to subject knowledge the Standardsspecify that trainees must demonstrate that they have ‘a secure knowledgeand understanding of the subject(s) they are trained to teach’ (ibid., p. 18).

In the case of Design and Technology the Government recognised thatdue to the breadth of subject content covered by National CurriculumDesign and Technology in schools newly qualified teachers (NQT) could notbe expected to have degree level subject knowledge across all aspects of thatcurriculum.

In 1995 the ITT subject knowledge requirements for Design andTechnology were therefore divided into four fields of knowledge and acommon core (Design and Technology Association (DATA), 1995). Thecommon core contained designing, communication, products and appli-cations, technological concepts and Information Technology, and the fourfields of knowledge were Resistant Materials, Systems and Control, Food

194 STEPHANIE ATKINSONSTEPHANIE ATKINSON

Technology and Textile Technology. An NQT is expected to be able toteach a minimum of two fields of knowledge and the common core. Thetrainees in this investigation all completed subject studies modules inDesigning and Making, Graphical Communication and Modelling, Sys-tems and Control (including both Electronics and Pneumatics), Textiles,and Information Technology (including Computer Aided Design andComputer Aided Manufacture). This study centred on achievement intwo contrasting areas of learning found in the Design and Electronicsmodules taught at the University highlighted in this article.

The contrasting elements of design and technology under discussion

The rational for choosing these two contrasting areas of learning was theauthor’s belief that successful achievement was likely to be influenced bythe differences in the content to be learned; the teaching strategies adopted,the learner’s thought processes needed and the outcomes expected of them.

In the design modules trainees carried out design and make projectsleading to three-dimensional working prototypes, whilst learning new skillsand processes as the need arose. In the electronics modules trainees weretaught electronic skills, knowledge and understanding which could then beused at a later date in technological design activities in other modules beingstudied.

These two areas of design and technology were not only contrasting intheir content but also in the teaching strategies used to deliver them. Aftermuch academic debate it was decided to teach the electronics mainlythrough the use of commercially produced Computer Aided Learning(CAL) packages, with limited tutor intervention. The CAL packages usedwere part of a suite of mixed media blended learning materials developedover the past 25 years by LJ Technical Systems (www.ljgroup.com). Thesematerials have been successfully and extensively used in Schools and Fur-ther Education across much of the world, particularly in America, theMiddle East and the United Kingdom (UK), although this was the first timea University in the UK had used these materials as a part of one of theirdegree programmes.

Using these materials students were able to work at a time that suited themand at their own pace, which was important as the range of expertise amongstthe students at the start of the modules was great. The modules had fourprimary objectives, which were assessed by means of the CAL packages:� Understanding of electrical quantities and the use of instrumentation for

their measurement.� Identification of individual components and measurement of their char-

acteristics.� Synthesis of circuit building blocks based on the components being

investigated.� Location and identification of computer inserted ‘faults’ within the

investigated circuits.

FACTORS INFLUENCING SUCCESSFUL ACHIEVEMENTFACTORS INFLUENCING SUCCESSFUL ACHIEVEMENT 195

The software was linked to a dedicated electronics base unit and varioussub-circuit modules were connected to this for the investigations. All con-nections were by removable wires and link pins, so no circuit constructionskills were required. The tasks were presented in the form of an ‘on screen’manual but copies of this could be optionally printed and retained by thestudents as a logbook of activities. The answers were entered in a separatewindow and took a variety of forms. These were predominately ‘multiplechoice’, or ‘yes–no’ format, but in some instances numeric answers wererequired. In the case of calculations, the system required the exact value fora mark to be awarded, but when entering measured quantities, any valuewithin a certain range would be accepted because of component andinstrumentation tolerances. Within a particular group of questions thestudent was free to go back and alter an answer as often as necessary.However, students were unable to proceed to the next group until thatsection was submitted and marks allocated. Failure to achieve a certainminimum score would also prevent progression to the next stage and tutorintervention was then required.

In contrast, the skills, knowledge and understanding required in thedesign modules were taught through lead lectures, studio/workshop sup-port, individual tutorials and formative feedback as the design processleading to a working prototype solution unfolded. The aims of thesemodules were to extend the students’ thinking processes, independence andself-motivation, whilst developing and expanding their ability to appro-priately use research techniques, combine technical and aesthetic creativity,be sensitive to user needs, communicate their thoughts and ideas inappropriate two- and three-dimensional forms throughout the process,have an understanding, and skill in manufacturing and evaluate both theirproduct and their process (both design and manufacturing) in a mannerthat would enable them to design and make more effectively and efficientlyin future projects.

Cognitive or learning style and teaching strategies

The terms cognitive style and learning style have been widely used byeducational theorists for the past sixty years. Terminology has varied fromwriter to writer (e.g. Curry 1983; Dunn & Dunn 1993; Kolb 1984; Riding &Cheema 1991). On some occasions the terms have been used interchange-ably, whilst at other times they have been afforded separate and distinctdefinitions (Cassidy 2003). However, many (e.g. Biggs & Moore 1993;Goldstein & Blackman 1978; Riding & Pearson 1994; Tennant 1988; Witkinet al. 1971) have agreed that cognitive style is a distinct way for an indi-vidual to encode, store and perform, and one that is mainly independent ofintelligence, and that learning style ‘is adopted to reflect a concern with theapplication of cognitive style in a learning situation’ (Cassidy 2003, p. 81).Riding and Cheema (1991) supported by Biggs and Moore (1993) also


explained the difference between learning approaches or strategies andlearning or cognitive styles by suggesting that on the one hand preferredlearning or cognitive style was a fairly fixed characteristic that was purelycognitive, whilst approaches or strategies they believed were the methodspeople used to cope with situations and tasks, and that these could varyfrom time to time as they were learned or developed.

As the relevant research base into learning style and cognitive style hasgrown so have the number of terms used to describe style groupings and thenumber of instruments developed to categorise individuals. In 2003 Coffieldet al. identified 69 models, whilst Riding & Rayner’s (1998) analysis of themultiplicity of constructs concluded that the terms could all be grouped intotwo principal cognitive styles and a number of learning strategies. Theyreferred to these cognitive styles as a ‘Wholist–Analytic Cognitive StyleFamily’, and a ‘Verbaliser–Imager Cognitive Style Family’. The ‘Wholist–Analytic style’ dimension they defined as the tendency for individuals toprocess information in wholes or in parts, while the ‘Verbaliser–Imagerstyle’ dimension they defined as the tendency for individuals to representinformation during thinking verbally or pictorially. These dimensions theybelieved were independent of one another.

The importance to the learner of linking teaching styles and strategies andlearning style and strategies (e.g. Evans 2003) has become clearer as moreresearch has been carried out in a variety of educational contexts. Theeffectiveness of many of the instruments designed to categorise individualshas been well reported (e.g. Dunn 2003; Riding 2002a, b; Riding & Cheema1991; Riding & Rayner 1998) however because of the quantity of research,the diversity of the disciplines and domains in which the research has beenconducted and the varied aims of the studies, the topic has become frag-mented and disparate (Cassidy 2003). Its popularity has also led to recentdebate into the stability and internal consistency of a number of theinstruments used (Coffield et al. 2003; Dunn 2003; Peterson et al. 2002;Riding, 2002b) although there continues to be general acceptance that themanner in which individuals choose to or are inclined to approach alearning situation has an impact on achievement (Cassidy 2003).

Many notable investigations have been carried out concerning the rela-tionship between learning/cognitive style and ability. Witkin et al. (1977)differentiated between the two by emphasising the bi-polar nature ofcognitive styles, unlike intelligence and other abilities. They suggested thateach pole of cognitive style had adaptive value under specified circum-stances, whereas to have more of an attribute such as intelligence was betterthan to have less of it. This difference was well defined by Riding (1996). Heexplained that the basic distinction between cognitive style and ability wasthat performance on all tasks would improve as ability increased, whereasthe effect of style on performance for an individual would either be positiveor negative depending upon the nature of the task and the way in which itwas presented to the learner.


The importance of this relationship between the teacher and the learnerhas been well researched (e.g. Biggs & Moore 1993; Entwhistle & Ramsden1983; McKeachie et al. 1986) as has the belief that the type of learningdemanded by different disciplines is diverse, with teaching strategies beingmore or less appropriate depending upon such factors as the context, thecontent and the learner. Entwhistle and Ramsden (1983) suggested that inthe arts learners must be encouraged to search for personal meaning andthat this depended on the empathy and openness of lecturers, informalteaching methods, and freedom for learners to explore their interests, whilstin the sciences good teaching was seen to rely more on pitching the infor-mation at the correct level and being alert to learners’ difficulties.

Recent research by Science and Mathematics educators (e.g. Kelly 1999)supported the Piaget (1936) and Vygotsky (1962) belief that even in sciencesubjects learners need to be given the opportunity to interact socially,manipulate materials, and use inquiry in order to construct knowledgemore fully and deeply. In this study design modules were seen to be amixture of the arts and science disciplines and the lecturer was aware thatshe needed to fluctuate between free and operational teaching strategies tosupport learning, whilst in the electronic modules the teaching strategiesadopted were in this instance seen mainly to follow the early scienceparadigm.

Gender issues

Equal opportunities within an educational context continue to concern thegovernment, educationalists and others, in spite of much research (e.g.Gilligan 1982; Kelly 1999; NCC 1989), the introduction of equal opportu-nities legislation (Harding & Grant 1984) and many government-sponsoredinitiatives, particularly in science and technology (e.g. Kelly et al. 1981). InDesign and Technology gender differences have been researched in terms ofmotivation (Kimbell et al. 1991; Riggs 1993), interests (Kimbell et al. 1991)and perception of the learners (Riggs 1993), as well as a teachers’ willingnessor otherwise to tackle any sex-role stereotyping (Riggs 1993) and a need tolink the subject with social values (Gilligan 1982; Riggs 1993; Smail 1984).The problems associated with gender imbalance (Riggs 1993) both in termsof opportunity (Bryne 1978; Harding & Grant 1984) and achievement (e.g.Banks 2002; Harding 2002; Kimbell et al. 1991) have also been well docu-mented. In this study there was little distribution imbalance as there were 16males and 14 females in the sample. In terms of access to the variousmodules on the programme both sexes had been given equal opportunities.This was mainly because there was little flexibility within the programmestructure. All trainees needed to study most of the modules on offer in orderto learn the required subject knowledge that would enable them to gainQTS. The issue of gender differences in terms of achievement was an areathat this study wished to pursue and one that will be discussed within theresults section of this article.


METHOD

Sample

The purposive non-probability sample comprised the total 30 trainees (16male and 14 female) studying design and technology modules during theirITT Design and Technology degree programme at a University in NEEngland during the academic year 2002–2003. These students had mixededucational backgrounds and ages. Some came straight from secondaryschool having successfully completed an ‘A’ level examination in some as-pect of Design and Technology, whilst others were mature students who hadalternative qualifications such as an HND (Higher National Diploma) insome aspect of Design and Technology. Those who had electronics expe-rience prior to studying for their degree did not necessarily have any pre-vious experience of design, whilst those who had experience of designingprior to studying their degree did not necessarily have any previous elec-tronics experience.

Information gathering instruments

The following materials were used:1. Learning Style. A well-established Cognitive Styles Analysis (CSA),

which was computer presented and self-administered, was used (Riding1991). This indicated a trainee’s position on both the ‘Wholist–Analytic’(WA) and the ‘Verbal–Imagery’ (VI) dimensions of cognitive style(Riding & Rayner 1998) by means of an independent ratio for each.Every member of the sample carried out the CSA in the manner pre-scribed in the CSA administration documentation.

2. Achievement. Two mean marks were obtained for each trainee, one fordesign activities and one for technological activities. The first mean markwas calculated for each trainee using the results of assignments in mod-ules in which design activity had been assessed at levels 1, 2 and 3. Thesecond mean mark was calculated using the results from a contrasting setof three modules where electronics skills, knowledge and understandinghad been assessed also at levels 1, 2 and 3.

3. Previous Design and Technology Skills. Each member of the samplecompleted a previously piloted, short questionnaire ascertaining experi-ence in design and technology activity prior to starting the degree pro-gramme. This asked questions regarding designing and electronicsseparately and concerned: examinations taken in Design and Electronicsprior to starting the degree programme; any work experience in industryutilising design or electronic skills prior to starting the degree pro-gramme.

4. Lecturer perceptions. A semi-structured interview was carried out with thelecturer responsible for the electronics modules once the other data hadbeen analysed. This interview was used to ascertain the lecturer’s thoughtsregarding factors that could have influenced the results. In the case of the


design modules the author of this article provided her observations andconsidered opinion regarding the same interview questions, as she wasresponsible for that element of the degree programme.

RESULTS AND DISCUSSION

Learning style of the sample

As mentioned previously, debate into the stability and internal consistencyof many cognitive/learning style models (Coffield et al. 2003; Peterson et al.2002; Riding, 2002b) was taken into consideration when methods of anal-ysing the collected data were designed. Initially the data was analysed usingthe raw CSA ratios as suggested by Peterson et al. (2002). However nolinear correlation with any of the variables under discussion were found, soit was decided to group the sample by the cognitive style categories definedin the CSA administration documentation, Wholist-Intermediate-Analyticon one dimension and Verbaliser–Bimodal–Imager on the other, as theselabels seemed very appropriate to the type of activity under scrutiny in thisresearch project. The WA ratios of the total sample ranged from 0.700 to2.910 with a mean of 1.412 (SD ¼ 0.515) and a median of 1.320. The VIratios ranged from 0.750 to 1.430 with a mean of 1.093 (SD ¼ 0.149) and amedian of 1.105. The correlation between the two cognitive style dimensionswas )0.154 attesting to the orthogonality of the two dimensions (cf. Riding& Cheema 1991; Riding & Douglas 1993). In comparison to the CSAStandardisation Sample (n = 999) referred to by Riding (2002a) the samplereported in this study did not have subjects at the extremes of eitherdimension, this was particularly noticeable at the Wholist end of the WAdimension and the Imager end of the VI dimension (see Figure 1).

This later result was initially considered surprising, as it had been expectedthat many of the sample would be ‘strong’ imagers as they were training tobecome design and technology teachers where the need to be able tomanipulate images in the mind during design and technology activity wasrecognised as a valuable skill. However, as prospective teachers, the need tobe able to work equally competently with both text and images and be able tocommunicate with pupils who may themselves be at the extremes of adimension would suggest that being at the centre of the VI dimension couldbe an advantage. In this study the categories on the VI dimension were fairlyevenly split into 9 Verbalisers towards one end of the dimension, 11 Bimodalsaround the centre and 10 Imagers towards the other end of the dimension.

With regard to the WA dimension, the researcher would suggest that inboth the task of being a successful teacher and a successful design andtechnologist (Denton 1992; Lawson 1990) being strongly Analytic orWholist could be an advantage at certain times and a disadvantage atothers. Data from this study indicated that the sample on the WA dimensionwas surprisingly unevenly balanced. It was predominantly Analytic (n =19)


with only 6 Intermediates around the centre of the dimension and 5 Wholistsmaking up the rest of the sample on this dimension. It was recognised by theresearcher that this skewed distribution could affect results and needed to beborne in mind during data analysis.

With respect to learning style and gender distribution, the equal balanceobserved by Riding et al. (1995) on both dimensions was repeated in thisstudy on the VI dimension. However in line with the overall sampleimbalance on the WA dimension where there were more Analytics thanWholists, there was gender imbalance between the three categories (seeTable I), and this was particularly noticeable in the female sample, this didnot replicate the balanced findings of Riding et al. (1995). This imbalancebetween cells led to very small numbers in certain learning style categories,unfortunately meaning that statistical analysis was not always feasible whengender and learning style were used as variables.

Achievement in design modules

All trainees in the sample completed each assignment in all the design andelectronic modules. Each assignment was awarded a maximum of 100marks. A mean mark for each trainee was calculated using the combineddata from level 1, 2 and 3 modules in design and electronics separately.Total sample and gender differences in terms of achievement will bediscussed in the following paragraphs.

In the combined mean mark for the three design modules the totalsample achieved a mean mark of 57.100 with a standard deviation of10.516. The maximum mark awarded was 76% and the minimum was

WHOLIST ANALYTIC

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 2.0 3.0 4.0 5.0

STANDARDISATION SAMPLE0.370 4.050

STUDY SAMPLE0.700 2.910

VERBALISER IMAGER

0.4 0.5 0.6 0.7 0.8 0.9 1.0 2.0 3.0 4.0 5.0

STANDARDISATION SAMPLE0.400 5.610

STUDY SAMPLE

0.750 1.430

6.0

Figure 1. Comparison between the cognitive style ratio found in the CSA standardisation

sample and the sample reported in this study.


38%. In an analysis of mean marks in terms of gender it was found thatresults were in-line with recent research (e.g. Skaalvik & Rankin 1994;Wong et al. 2002) in that females outperformed males by achieving anoverall higher mean mark (58.071) than the male sample (56.250)although this was not by a significant amount (unpaired t-test, p-value0.6442). The marks for female trainees tended to be compressed aroundthe mean whereas the marks for male trainees were spread across the fullmark range (see Table II). At the lower end of the achievement scale31% of males as apposed to only 21% of females were awarded marks ofunder-50%. It was also the case that the only trainee to gain a meanmark of less than 40% was male. The large number of males with theselow marks had a marked affect upon the overall mean mark for males.However it needs to be noted that at the opposite end of the achievementscale there was a higher percentage of males (18.750) than females(14.286) achieving marks of over 70%, and that the top two marks forthe whole sample were awarded to males.

Achievement in electronics modules

All trainees in the sample completed each of the electronic skills modules atlevels 1, 2 and 3. The results indicated an overall mean mark of 65.167 with astandard deviation of 11.350. In this case the maximum mean mark awardedwas 86% and the minimum was 43%. In contrast to the design modulesmale trainees in the electronic modules achieved a higher mean mark(69.375) than female trainees (60.357). Once again this result was not sig-nificant although there was a greater gender difference than in the designmodules. It was found that a significant 50% of males achieved a mark of

TABLE I

Data illustrating gender differences on the two cognitive style dimensions

Dimension: Wholist Intermediate Analytic

Number: n percentage n percentage n percentage

Male 2 12.500 5 31.250 9 56.250

Female 3 21.428 1 7.143 10 71.429

Percentage

difference

8.928 24.107 15.179

Dimension: Verbaliser Bimodal Imager

Number: n percentage n percentage n percentage

Male 5 31.250 6 37.500 5 31.125

Female 4 28.680 5 35.714 5 35.714

Percentage

difference

2.680 1.786 4.464


over 70% in comparison to only 14% of females (variance 18, df 1, v2

18.000, p-value <0.0001). At the opposite end of the mark range 14% offemales achieved a mean mark of less than 50% whilst only 6% of maletrainees were found in this category (see Table II).

Previous experience

All members of the sample provided their answers to the questionnaireregarding prior experience as described in the Methods section of thisarticle. The answers provided were separated for scoring purposes into thetwo that concerned previous experience in design and the two that con-cerned previous experience in electronics. A student who had studied for anexamination and had industrial experience in one of the two areas was givena score of 2, whilst a student who had only experienced one of those situ-ations was given a score of 1. A student with no experience of either situ-ation was given a score of 0.

In analysing the data regarding previous design activity the three cate-gories were evenly balanced both in terms of the total sample and when splitby gender. Ten students had studied previously to examination level and hadprior industrial experience, 9 students had some experience, either toexamination level or had industrial experience and 11 had had no experienceof design activity before starting their degree programme.

In electronics the results were not balanced. A significant 25 students hadhad no previous experience of electronics prior to starting their degreeprogramme, three, one male and two female, had taken either anexamination or had industrial experience and only two students, both malehad taken an examination and had industrial experience.

The relationship between learning style and achievement

With regard to the relationship between learning style and achievement, inthe first instance the overall mean mark for design assignments was viewedusing the two learning style dimensions separately. The mean mark achievedby each learning style category was then placed in rank order (see Table III).

TABLE II

Data illustrating the number of trainees in each achievement category for design and electronics

modules

Percentage Design modules Electronics modules

Total Male Female Total Male Female

<40% 1 1 0 0 0 0

40–49% 7 4 3 3 1 2

50–59% 10 5 5 6 2 4

60–69% 7 3 4 11 5 6

>70% 5 3 2 10 8 2


On the WA dimension, Analytics with a mean mark of 59% were themost successful and Intermediates at the centre of that dimension with amean mark of 53% were the least successful. On the VI dimension, Imagerswere the most successful with a mean mark of 61% and once again those atthe centre of the dimension, were the least successful with a mean mark of55% (see Table III).

In comparing the success rates of the six learning style categories togetherit was found that Imagers were overall the most successful and Intermedi-ates were the least successful. As already explained, it was not a surprise thatImagers were the most successful learning style group in design activities,due to the nature of the activity. However the nature of the activity couldnot explain the lack of success for Intermediates on the WA dimension. Infact the researcher would have expected Intermediates, who had the abilityto oscillate more easily between viewing the task as a whole and concen-trating on segments of the task, to have achieved a much higher level ofsuccess in this area of design and technology. It was recognised thatalthough this later result provided interesting food for thought, it had to beremembered that the skewed distribution in terms of learning style on theWA dimension could have affected the result (see Table I).

As with the design marks, the electronics overall mean marks were viewedusing data from the two learning style dimensions separately and the resultswere placed in rank order (see Table III). On the WA dimension Interme-diates who had been the least successful in the design activities were found tobe the most successful in electronics with a mean mark of 70%, whilstWholists were the least successful with a mean mark of 63%. On the VIdimension the results replicated those found in the design modules, Imagerswere the most successful with a mean mark of 69% and Bimodals, at thecentre of the dimension, were the least successful with a mean mark of 62%(see Table III).

In comparing the mean marks of the six learning style cells together it wasfound that overall Intermediates were the most successful and Bimodalswere the least successful. For the reasons given in the paragraph above therewas no surprise in finding that Intermediate trainees were the mostsuccessful. However, as the teaching materials were provided in an evenly

TABLE III

Data illustrating achievement in design and electronics modules split by learning style. Top and

bottom marks on each learning style dimension are highlighted in bold

Rate of success Design modules Electronics modules

WA Mean

mark

VI Mean

Mark

WA Mean

mark

VI Mean

mark

Most successful A 58.632 I 61.200 INT 70.167 I 68.500

W V A V

Least successful INT 53.333 BIM 54.545 W 63.200 BIM 62.364


balanced mixture of text and images it was unexpected to find that Bimo-dals, who were considered equally able to process information in the form oftext or images, were in fact the least successful. Nor could the genderbalance be a factor as there was little difference between the male and femaleresults on this dimension (see Table I).

When viewing the achievement and gender relationship for design mod-ules it was found that on the WA dimension males and females followed asimilar pattern (see Table IV). Intermediates from both sexes did equallybadly although no one learning style category achieved a significantly highor low mean mark. On the VI dimension male Imagers achieved a high meanmark and Bimodal males achieved a very low mean mark, whereas there waslittle difference between female achievement in any of the three learning stylecategories.

In an analysis of the relationship between learning style and achievementin electronic modules when split by gender it was found that the achieve-ment of female trainees was similar across each cell of the WA dimension. Incontrast there was a difference between male trainees in each cell (seeTable IV)

On the VI dimension a mirror image was found between the data formales and females. Female Verbalisers achieved the best result and femaleImagers achieved the poorest result. In contrast male Imagers achieved thebest result for males and male Verbalisers obtained the poorest resultamongst males on this dimension (see Table IV).

In many ways these differences were to be expected as a search of theliterature suggested a fundamental difference between males and females interms of information processing even when studying similar material (e.g.Riding & Rayner 1998). In this study this was further compounded by theneed for very different information processing skills in the two contrastingareas of design and technology under scrutiny. The data analysis indicated

TABLE IV

Data illustrating achievement in design and electronics modules split by cognitive style and

gender

Design modules Electronics modules

WA Mean

mark

VI Mean

mark

WA Mean

mark

VI Mean

mark

Rate of success of males

Most successful A 58.000 I 64.200 INT 72.200 I 79.000

W 57.000 V 55.400 A 68.889 BIM 66.167

Least successful INT 52.800 BIM 50.333 W 64.500 V 63.600

Rate of success of females

Most successful A 59.200 I 59.600 W 62.333 V 66.500

INT 56.000 BIM 58.200 INT 60.000 BIM 58.000

Least successful INT 55.000 V 57.000 A 59.800 I 57.800


that female Verbalisers did well in electronics and poorly in design modules,whilst female Imagers did well in design modules and poorly in electronics.In contrast male Imagers did equally well in both electronics and designactivities whilst Bimodal males achieved very poor results in design activitiesand male Verbalisers achieved poor results in the electronics modules.Unfortunately as mentioned previously the distribution of learning style wasskewed on the WA dimension and when split by gender many cells becametoo small for meaningful statistical analysis. However, the data did providesupport for the need for further research into gender differences in thecontext of design and technology, preferred learning style and achievement,using a larger sample in the future.

Difference in the sample in relation to learning style and previous experience

When the relationship between learning style and previous experience wasscrutinised it was found that there was a similar pattern across almost all thelearning style categories. A mirror image predominated with a greaternumber of students with prior knowledge of designing and no previousknowledge of electronics than the other way around. This was significantlyso in the case of Imagers (variance 18.00, df 1, v2 18.00, p-value <0.0001)(see Table V). This result added support to the reseacher’s belief that thelearning style of Imagers was highly appropriate to students choosing designactivity as a career although it does not help to clarify why there was not agreater predominance of Imagers in the total sample.

The relationship between achievement and previous experience

As already stated, with regard to previous experience in design activity, thesample was split fairly evenly into those who had little or no experience priorto beginning their degree programme, those with some experience, and thosewith considerable experience. When design achievement data was added to

TABLE V

Data illustrating the relationship between learning style and previous experience in design and

electronics modules

Design experience Electronics experience

Prior experience No prior

experience

Prior experience No prior

experience

Wholist 4 1 1 4

Intermediate 3 3 1 5

Analytic 12 7 3 16

Verbaliser 5 4 1 8

Bimodal 6 5 1 10

Imager 8 2 3 7


the analysis, a positive relationship was found. Those trainees who had themost previous experience were the most successful, and those who hadminimal experience were the least successful (see Table VI). When genderwas added to the equation the data indicated that the female sample con-tinued this expected pattern, whereas the male sample provided a differentpicture. However, the result for males did not indicate a reliable trend asthere were only four males in the ‘with considerable experience’ categoryand whilst one trainee achieved the expected high design mark the otherthree had pulled the mean down with very low marks which had unfortu-nately occurred across many of their University studies that year.

In electronics it was found that there was a skewed distribution of thesample (see Table VI). As reported earlier 75% of trainees had no previousexperience of electronics before they began their degree programme incomparison to only three that had some experience and two (both maletrainees) that had considerable previous experience. As these later twocategories were so small it was decided to amalgamate them, thus providinga category, ‘with previous experience’ (n ¼ 5) and one with ‘no previousexperience’ (n ¼ 25). When achievement data was then scrutinised it fol-lowed the expected trend identified in the design data, those trainees whohad no previous experience were the least successful and those with expe-rience were the most successful. The data once again provided a clearindication of the poor performance level of female trainees in electronicswith the top mean mark for female trainees who did have previous electronicexperience being just lower than the bottom mean mark for the maletrainees even though they had had no previous experience of electronics.

The relationship between achievement and previous experience in designmodules compared to electronic modules

As stated above the data indicated a positive relationship between levels ofprevious experience and achievement in both design and electronic modules.

TABLE VI

Data illustrating achievement in design and electronics modules split by previous experience

and gender

Level of prior experience Sample size Total sample Males Females

Design modules

Considerable previous experience 10 59.700 54.500 63.500

Some previous experience 9 58.222 69.500 55.000

No previous experience 11 53.818 54.000 47.000

Electronics modules

Considerable previous

experience + some previous

experience

2 + 3 76.400 79.000 66.000

No previous experience 25 62.920 66.167 59.923


In order to tease this out further the individual module marks for eachmodule studied at level 1, 2 and 3 were scrutinised separately (see Table VII).There remained a positive relationship between levels of experience andachievement. However, the data for electronics indicated a decrease betweenthe difference in the mean module mark for trainees who had no experienceand trainees who had previous experience as more electronics was studied.At level 1 there was a 14% difference, at level 2 a 13% difference and at level3 an 8% difference. This was not found to be the case in the design modules.In this instance the difference actually increased with a 6% difference at level1, a 7% difference at level 2 and a 9% difference at level 3.

To try to ascertain the reasons for this increase in the gap in design andthe decrease in the gap in electronics as the two subjects became morefamiliar to the students a semi-structured interview was conducted with thelecturer responsible for the electronic modules. As the author of this articlewas the lecturer responsible for the design modules, she then reflected uponthe same interview questions in the context of the design modules.

In the case of electronics the lecturer concerned identified several possiblefactors, for the diminishing gap between those with no experience and thosewith some experience. He suggested that although the majority of thetrainees did not have electronic skills at the start of their degree programme,the operational teaching strategies adopted gave them the necessaryknowledge and understanding to pass the modules. He also believed that thecontent being tested was a set of knowledge and rules that could be learntsystematically and that in many instances there was a right and a wronganswer that helped the beginner to see easily where they had made mistakesin the level 1 module and how they could improve, even though he felt thatmany of them had no affinity with the approach necessary to be successfulor even the mindset required to specialise in electronics.

TABLE VII

Data illustrating a comparison of levels of previous experience in design and electronics prior to

starting the degree programme and success in modules at levels 1, 2 and 3

Level of prior experience Level 1 Level 2 Level 3

Design modules

Considerable previous experience 59.500 59.667 65.667

Some previous experience 56.625 58.000 56.500

No previous experience 53.556 52.750 56.375

Difference top ) bottom 5.944 6.917 9.292

Electronics modules

Considerable previous experience

+ some previous experience

79.250 71.333 64.000

No previous experience 65.059 57.941 56.091

Difference top ) bottom 14.209 13.392 7.909


Another important factor he highlighted was that those who were new toelectronics tended not to bring misconceptions about the subject with them,whereas trainees who had already studied electronics often did and that thissometimes hindered their progress. The lecturer also suggested that becausemany of the beginners in the group were afraid of failure and needed to passthe modules at levels 2 and 3 to achieve their subject knowledge require-ments for QTS, he believed that they were more willing to invest sufficienttime to learn the materials provided and thereby achieve success.

In the context of design activities carried out by the trainees, and afterconsiderable reflection concerning the reasons for the increase in the gapbetween the results of those with no prior experience and those with con-siderable experience as more design modules were studied, the authoridentified a number of possible contributing factors. These were as follows:trainees who had chosen to study design in earlier educational situations hadusually done so because they had an affinity with the activity. Various linkedfactors such as innate ability; self-motivation and success having bred fur-ther success were thought to influence this.

It was also evident that those with previous experience had alreadydeveloped good visual communication skills that had often come naturallyto them at an early age. Most of them had indicated during discussions withthe author that they had spent countless hours throughout their lives, bothin and out of formal educational situations, developing their design skills ina wide variety of materials, techniques and processes that they found easy totransfer to different contexts.

In comparison the author had observed that those trainees who hadtechnological rather than design backgrounds tended to be people who hadnot studied drawing and designing in the later stages of secondary education.This was either because they had taken a science route leading to their schoolexaminations, which left little time for the arts, or because they had alreadydecided that they did not enjoy such activities. Discussions with thesetrainees indicated that this was often because as pupils these trainees had notbeen successful in communicating their thinking in a visual sense. They hadalso found that they could assess their own inadequacies easily just bylooking at their poor drawings or badly made products and had then made aconscious decision not to continue to study an aspect of the school curric-ulum which they did not enjoy and believed they were likely to fail. Howevernow that they were training to become design and technology teachers theyneeded to develop design skills even though they found it difficult, as thesewere an essential element of the subject requirements for QTS.

As explained at the start of the article the ITT Design and Technologydegree programme is an intense course that expects trainees to develop awide range of skills and expertise and there is therefore only a finite amountof time that can be spent studying each module. This lack of time wouldappear to be a greater disadvantage to those trainees who come to theprogramme lacking in design skills than those who lack technological skills.This would seem to be partially because there are no right or wrong answers


to design problems nor is there one process that if adhered to will auto-matically achieve a successful solution. To assimilate the esoteric concepts isa slow developmental procedure rather than something that can be factuallylearnt in a short space of time. Routes through the design process can betaught, and all trainees can be guided in appropriate directions. However,the development of high order creative, innovative thinking that is anessential ingredient for those wishing to achieve high marks in designactivities cannot happen in a short period of time for those who are notnaturally creative.

CONCLUSION

The learning style groupings of the sample were not as expected although itwas recognised that there was a possible conflict between the learning stylescompatible with acquiring design and technology skills and those consideredhelpful to teachers of that or any other subject. On the VI dimension it hadbeen expected that there would be either, more Imagers due to the nature ofthe design and technology being studied or more Intermediates as thetrainees were intending to become teachers, however on this dimension therewere an even number in each learning style category. The significantly largeproportion of Analytics on the WA dimension was surprising as it had beenanticipated that if any of the three categories were to have a larger numberof trainees in it then that would be Bimodals at the centre of the dimension,both due to the nature of the design and technology activities being studiedand the fact that the sample were training to be teachers.

With regard to achievement in relation to past experience, the expectedpositive relationship was found throughout both design and electronicsmodules. Those who had previous experience achieved higher results thanthose who were new to the subject even after having studied modules atlevels 1, 2 and 3. However there were differences between the contrastingmodules. In electronics the difference diminished as more electronics wasstudied. In design modules the opposite was the case. The nature of the twocontrasting activities suggested a reason for this. The intensity of the coursewith its wide range of skills and expertise required of every trainee in a shortspace of time meant that there was a limit to the time that a trainee coulddevote to each module. Electronic skills were found to be more suited to thiscompacted learning scenario whereas the esoteric nature of design activityneeded more time if beginners were to develop high order creative, inno-vative thinking that is seen as an essential ingredient for achieving improvedmarks in design activities.

However, the main area of concern that this study has highlighted hasbeen the relationship between gender and achievement. In design activitiesalthough female trainees achieved a better mean mark than male trainees, itwas males in the sample who achieved the highest marks. In electronics maletrainees outperformed female trainees even though there were a similar


proportion of both sexes who had no experience of electronics beforestarting the programme. When learning style data was added to the pictureit was found that the relationship between learning style and achievementwas different for males and females. In the case of the male sample, Imagerswere the highest achievers in both design and electronic modules whilstfemale Imagers who achieved high marks in design modules achieved lowmarks in electronics and female Verbalisers provided a mirror image of thisresult. Unfortunately the size of the total sample was such that when splitinto the six learning style categories the cells became too small for mean-ingful statistical analysis to be carried out. However, the picture that hasemerged could be significant if the same results were replicated in a largersample. It is therefore the intention of the author to collect data from thenext cohort of students and also from other institutions training design andtechnology teachers so that the gender and learning style differences wit-nessed in this study can be investigated further.

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