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An investigation was conducted in the design department of a medium-sized automotive company to establish engineers' preferred learning styles. This was achieved by using 2 proven questionnaires followed by statistical analysis methods. The evidence showed that the engineers investigated have a significant visual learning style preference. This means that their learning is more effective by using diagrams, sketches, photographs, schematics, flow charts, pictures, videos, computer graphics, and demonstrations in training programs and in their everyday working environment. The present computer-aided design (CAD) training in the company does incorporate some of these visual techniques and so does satisfy the engineers' visual learning style preference. Evidence also suggested that there is not a need to have different training and learning methods for design engineers and for managerial engineers such as project engineers and team leaders.
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Key-words
Learning styles, Learning curves, Engineering, Design
Abstract
An investigation was conducted in the design department of a medium-sized automotive company to establish engineers' preferred learning styles. This was achieved by using two proven questionnaires followed by statistical analysis methods. The evidence showed that the engineers investigated have a significant visual learning style preference. This means that their learning is more effective by using diagrams, sketches, photographs, schematics, flow charts, pictures, videos, computer graphics, and demonstrations in training programmes and in their everyday working environment. The present computer-aided design (CAD) training in the company does incorporate some of these visual techniques and so does satisfy the engineers' visual learning style preference. Evidence also suggested that there is not a need to have different training and learning methods for design engineers and for managerial engineers such as project engineers and team leaders.
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Introduction
Learning styles describe the attitudes and behaviours that determine the preferred way of learning of an individual. People vary not just in their learning skills but also in their learning styles (Felder, 1996). The objectives of this investigation were to establish the following:
Do engineers have different learning characteristics than the rest of the professional population, and if so what are their preferred styles of learning? Does the present computer-aided design (CAD) training available satisfy the engineers' learning style preferences? Do design engineers have different learning characteristics than managerial engineers, such as project engineers and team leaders?
From reviewing the literature, the characteristics of each learning style are summarised below (Felder, 1996; Honey and Mumford, 1992; Kolb, 1984; Senge, 1992):
Activists or active learners like to try out new experiences and tend to be openminded people. Problems are often tackled by brainstorming. They thrive on new experiences but find implementation and consolidation dull. They are gregarious people and prefer working in groups.
Reflectors or reflective learners: observe and ponder experiences. Data is collected and analysed thoroughly about experiences before any conclusion is made. In meetings they adopt a low profile, listening to others before commenting. Generally they tend to be cautious people who prefer to work alone.
Theorists integrate observed experiences, thinking problems through methodically. They feel uncomfortable with subjective opinions and ambiguity, and prefer establishing logical theories from facts. They tend to be analytical people and perfectionists, fitting things into rational schemes.
Pragmatists like to put theories into practice. They prefer producing plans before promptly getting on with things. Pragmatists are the kind of people who are keen to try out new skills learnt on courses. They are practical people who like solving problems and tend to be impatient with slow and inconclusive discussions.
Sensing learners tend to be patient and careful with detailed work. They are good at learning facts and often like solving problems by well-established methods. Sensors are inclined to be practical and like hands-on work. They dislike complications and surprises.
Intuitive learners prefer discovering possibilities and relationships. They are comfortable with new concepts and are more innovative than others. Intuitors dislike routine calculations that require memorising.
Visual learners remember best by what they see. They absorb information more effectively from pictures, diagrams, films and demonstrations. They prefer their work to be colour coded for categorising purposes, and highlighted words and sentences for clarity.
Verbal learners tend to learn best from written and spoken explanations. They understand something better by talking to peers. They are comfortable reading books and manuals to absorb information.
Sequential learners tend to gain understanding in logical steps. They prefer to methodically go through calculations until solutions are found. They tend to like procedures to follow. Global learners tend to learn randomly without initially seeing the connections, and then suddenly they are enlightened. They tend to solve complicated problems quickly but can have difficulty explaining the steps. Once the whole picture emerges for them, they will find new ways of accomplishing tasks.
The company which was used to research into engineers' learning style preferences was a medium-sized automotive company (>50 employees <250). All the engineers at this company utilise CAD software for 3D design, development, optimisation, data management, and detailing. Traditional instructor-led courses are adequate for teaching CAD and other engineering skills. However, there is a need to develop customised training material that can be accessed when the engineer needs to learn new skills, and not wait until the next instructor-led classroom course. As the responsibility is put on all engineers at the company to keep updated and refreshed with engineering knowledge and skills, what techniques can be used to make their learning more effective?
Methodology
In order to examine the objectives primary data was collected using questionnaires in order to investigate the engineers' various learning style preferences. There were two questionnaires selected after careful research and discussions. One was an established questionnaire devised by Honey and Mumford (1992), referred to as questionnaire one in this paper. The other was a prototype version, developed from the latter by Felder (1996) and referred to as questionnaire two in this paper.
Questionnaire one asked 80 questions (20 for each of the activist, reflector, theorist and pragmatist learning styles). Questionnaire two asked 44 questions (11 for each learning style pair - active/reflective, sensing/intuitive, visual/verbal and sequential/global).
Questionnaire two was used to complement questionnaire one, as the author (Yvette James-Gordon) was not satisfied with the balance of questionnaire one. Honey and Mumford's (1992) questionnaire one seemed to categorise people by having either a strong to low preference of the activist, reflector, theorist and pragmatist learning styles. Engineers, due to their nature and skill-sets, will cycle through many learning styles and adopt their characteristics when determining a design solution. Felder's questionnaire two had a more balanced approach, as the learning styles were paired. A high preference for a particular learning style indicated a low preference for its corresponding pair. There was also an interest in the verbal/visual and sequential/global learning style pairs offered in Felder's (1996) questionnaire.
As there were no right or wrong answers to either of the questionnaires, assurance was given to the engineers that their results were for research purposes only and would not be used for any kind of assessment of them. The engineer needed to consider each question carefully and to what extent that question applied to him or her. Questionnaire one required a tick for sentences that were agreed with, and a cross for sentences disagreed with. Questionnaire two required (a) or (b) circled if the engineer agreed more with that sentence. Both questionnaires needed all of the questions answered, even if the answers given were borderline responses.
Both questionnaires were given to all 45 engineers in the design department in the company, of which 42 gave replies - 27 design engineers and 15 project engineers and team leaders. The questionnaires were left with each engineer for a period of two weeks before being collected from the company.
After the questionnaires were collected, the data was manually entered into each of the respective score sheets for each engineer. Each column was then totalled and the results were tabulated for all the engineers. The results were also sub-divided into design engineers only and project engineers and team leaders for comparisons in the data analysis.
Questionnaire interpretation
The population data collection for questionnaire one was based on a population of 3,500 scores of professional people working in industry and commerce in the UK (Honey and Mumford, 1992), illustrated in Table I.
As both the selected questionnaires had the population mean given for each learning style, it suggested that the data should be representation and so tend to form a normal distribution.
From scoring the results of questionnaire two, the engineer could compare himself or herself to the scale, illustrated in Table II and described as follows:
If the score is between -3 and +3, then the person is well balanced on the two dimensions of the scale (mean = 0).
If the score is between -5 and -7, or 5 and 7, then the person has a moderate preference for one dimension of the scale and will learn more easily in a teaching environment that favours that dimension. If the score is between -9 and -11, or 9 and 11, then the person has a very strong preference for one dimension of the scale. According to Felder (1996), this may produce difficulty within an environment that does not support that learning preference for the individual.
Results
Quality of results
All the engineers' scoring results are collated into the learning preference ranges as seen in Tables III and IV, from questionnaire one and questionnaire two respectively. The 42 out of 45 replies collected provide a good response to the survey. By inspecting the learning styles' results, the quality of the data and distribution features can be observed[1].
The activist and reflector learning style scores from questionnaire one should compare to the active/reflective scores on questionnaire two. The very strong learning preferences range for activist is 13 to 20 and the active learner is -9 to -11. The two same design engineers do correspond to this. However, the reflector scores seemed to be marked much higher than the corresponding reflective learning style scores for all the engineers.
The scores of the engineers' learning styles are representative, being within the professional people population scores. As the outliers in the learning styles are feasible preferences, this tends to suggest that these learning style distributions are skewed or that error has crept in. The reasons for error may have been because incorrect answers were given due to ambiguity of sentences. During the period that the questionnaires were left at the company, many engineers did contact the author (Yvette James-Gordon) and complained that questionnaire two had a few ambiguous and controversial sentences, e.g.:
I would rather be considered: (a) realistic or (b) innovative.
This question annoyed many engineers as they considered themselves to be both (a) and (b), and did not want to choose one answer over the other. If they chose (a) realistic, did that mean they were not innovative and vice versa?
Engineers by their very nature will be innovative during the initial design stages and realistic when it comes to selecting materials and manufacturing processes for their design. These design activities are typical of the engineers at the company.
The reflector, theorist and pragmatist scores are skewed towards the strong learning style preferences. The visual/verbal learning style pair is biased towards the visual end, indicating this is a very strong learning preference for all the engineers.
Analysis of results
First, let us consider the test to establish if the mean of each learning style for all the engineers at the company was significantly different or not from that of the population (professional people working in industry and commerce in the UK). The statistical calculations used the student's t-test method, as the sample size was only 42. Given the evidence available, the significance test calculations indicated that for the visual/ verbal learning style pairs there was a highly significant difference between the mean score for all the engineers and the mean score for the population (99.9 percent confidence level). For other learning styles, the evidence suggested, with 95 percent confidence, there was not a significant difference between the means. This can be seen on the radar diagrams in Figures 1 and 2.
Second, let us consider the test to establish if the mean of each of the learning styles for the design engineers was significantly different or not from the project engineers and team leaders. The statistical calculations again used the student's t-test method. From the significance test calculations, the evidence indicated, with 95 percent confidence, that there was not a significant difference between design engineers mean score and the project engineers and team leaders mean score for all the learning styles. This can be seen on the radar diagrams in Figures 3 and 4. Finally, let us consider the test to establish if the variance of each learning style for the design engineers was significantly different or not from the project engineers and team leaders. The statistical calculations used the F-ratio test method. From the available evidence and the significance test calculations, this suggested with 95 percent confidence, there was a significant difference between the variances of design engineers and the project engineer and team leaders for the pragmatist and sequential/global learning styles. For other learning styles, the evidence suggested, with 95 percent confidence, there was not a significant difference between the variances. This can be seen on the radar diagrams in Figures 3 and 4.
Discussion of results
The quantitative analyses carried out that used the standard statistical methods did produce reasonable results. However, due to the small sample tested, especially when comparing the design engineers with the managerial engineers, the results cannot be taken as conclusive. Nevertheless, evidence suggests that all of the engineers investigated are predominatly visual learners. Hence, their learning is more effective from diagrams, sketches, photographs, schematics, flow charts, pictures, videos, computer graphics, and demonstrations. By the very nature of engineering design, these visual learning techniques are used every day. CAD is typical of this and uses visual techniques such as icons to select commands, 3D parts are sketched, designed, assembled and analysed using coloured graphics, with more advanced animation and simulation techniques being incorporated into the CAD's functionality.
The present CAD training at the company does satisfy the engineers' learning style preferences, as courses take advantage of these visual aids by using them in the training material. Demonstrations are given by the trainer to show the engineer what the CAD system is capable of, and then he or she works through a modelling example on the CAD system using the graphical interface, command icons, and visual computer-based help functions.
The design engineers after the CAD training learn on the job. According to Senge (1992, p. 23) "the most powerful learning comes from direct experience". This is the preferred method of learning at the company; as engineers become more skilled, the more they use the CAD and still produced useful design work. A little more work time is given to the newly trained engineers to allow for their learning and familiarisation with the graphical user interface.
For all but the visual learning style, there is not a significant difference between the means for all the engineers at the company and the population. Engineers due to their nature will automatically cycle through all the learning styles and adopt their characteristics.
This iterative process, with reference to questionnaire one, is illustrated in Figure 5, and is fundamental to continuous learning. Experience gained adds to the engineer's own knowledge and skill-base, and provides corrective feedback, for example:
Stage 1. Run a finite element analysis (FEA) simulation on the CAD part to see where the high areas of stresses are, in order to prove that the design is feasible. This is an activist learning style where learning is achieved by doing and experiencing.
Stage 2. Analyse FEA output results. This is a reflector learning style where information is gathered before conclusions are reached.
Stage 3. Use the CAD software to methodically model a new part or modify the existing part, based on the FEA results. This is a theorist learning style where the reasons behind things, concepts and relationships are important for understanding.
Stage 4. Test the new CAD part in a CAD assembly, to see if the part fits. Other design considerations to test include manufacturing and packaging implementations. This is a pragmatist learning style by testing the practical application of what is learnt.
From the experience the author (Yvette JamesGordon) has with the company, the organisation empowers all their engineers to take responsibility for their own learning and working practices. The design engineers have greater knowledge of their designs and developments than their team leaders. Design engineers are self-motivated, adaptable and selfdisciplined working alongside and collaborating with the project engineers and team leader.
Even though the sample sizes were small, the available evidence suggests there is not a significant difference between the investigated design engineers' mean score and that of the project engineers and team leaders. All engineers are given the same learning opportunities and tools in the automotive company, so it is feasible that they would have similar mean scores for their learning style preferences.
Given the available evidence and by inspecting the results for the pragmatist learning style, the project engineers and team leaders investigated have less dispersed and higher scores than design engineers do. This is possibly because the team leaders produce project plans for the design engineers in their team, which is a pragmatist learning style characteristic.
There are three extreme scores for the sequential/global learning styles of project engineers and team leaders. The project engineer who has a very strong sequential learning style preference is the most experienced traditional engineer at the company. He is the oldest engineer there, having a structured career route and being methodical in his design cycle. However, the other two team leaders have a very strong global learning style.
In today's engineering climate that has technological advances to speed up design solutions and reduce product lead times, engineers, especially project engineers and team leaders, need to adopt more global learning style characteristics.
Conclusions
The evidence concludes the following outcomes of the investigation at the automotive company:
Engineers in the design environment have a very visual preferred style of learning. For all but the visual learning style, there is not a significant difference between the engineers at the company and the professional population.
The present CAD training in the company does satisfy the engineers' learning style preferences, as it incorporates visual techniques and graphically represents the design.
There is not a significant difference in the learning style preferences design engineers or managerial engineers such as project engineers and team leaders. Hence, there is no need to have different training and learning techniques for them.
As all the engineers investigated have predominantly a visual learning style preference, the company was recommended to incorporate visual techniques when developing future customised training material, learning aids, and work-based programmes. The visual techniques can include diagrams, sketches, photographs, schematics, flow charts, pictures, videos, computer graphics, and demonstrations, in order to create an effective learning environment for the engineers.
Note
1 Complete data analysis and statistical calculations, including graphical representation of the data collected, can be obtained from the author (Yvette James-Gordon) on request.
References
Felder, R.M. (1996), "Matters of styles", ASEE Prism, Vol. 6 No 4, pp. 18-23.
Honey, P. and Mumford, A. (1992), The Manual of Learning Styles, Ardingley House, Berkshire.
Kolb, D.A. (1984), Experiential Learning, Prentice-Hall, Englewood Cliffs, London.
Senge, P. (1992), The Fifth Discipline, Century Business, London.
Yvette James-Gordon and Jay Bal
The authors
Yvette James-Gordon is a Research Engineer at Warwick University, Coventry, UK.
Jay Bal is a Senior Research Fellow at Warwick University, Coventry, UK.
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