A holistic view on the development of engineering students’ flexible and creative problem solving.
J. Walther1, Chantinee Boonchai2, D. F. Radcliffe3 1
University of Queensland, Catalyst Research Centre for Society and Technology, Brisbane, Australia (
[email protected])
2
Prince of Songkla University, Faculty of Technology and Environment, Phuket, Thailand
3
Purdue University, School of Engineering Education, West Lafayette, USA
Abstract This paper presents results of an interpretive study into engineering students’ formation of professional competence from the interaction of learning activities and other influences from the learning environment. Data was collected in focus groups with students from Germany, Australia, the United States and Thailand. Using the qualitative analysis tool NVivo7, the interpretive data analysis categorised factors influencing the formation of students’ competence and derived seven competency clusters that were found to be significant in the students’ transition from university into professional practice. A particularly important cluster contained competencies related to the students’ ability to flexibly and creatively solve engineering problems. The analysis revealed that a number of educational influences such as schematic learning approaches in single-discipline lectures have a negative impact on the development of this competency cluster. In order to mitigate these negative processes we propose a holistic perspective on the educational process combined with measures to support reflective awareness of the complexities of competence formation in both teachers and students. Keywords: Engineering competence, learning outcomes, creativity.
1.
INTRODUCTION
Flexibility and creative problem solving are increasingly recognized as an essential component of engineering students’ professional competence [1]. The pressure for innovative solutions and products to be developed has grown substantially in today’s global economy [2]. The emergence of new technologies and disciplines results in the increasing complexity of the systems being designed [3] and engineering solutions are embedded in a wider social context [4-6]. The recognition of these emerging pressures has led to a transformational change of engineering education systems worldwide towards the definition of broader educational outcomes. This is reflected in both the Australian Graduate Attributes [7] and ABET’s Program Outcomes [8] as well as in the discussions on adopting an outcomes-based approach to accreditation and mutual recognition within the framework of the European Bologna process [9]. Most recent attempts to define the qualities of future engineers specifically highlight the need to enable students in creative and flexible ways of doing engineering work [1, 10]. The concept of outcomes-based education [11] revolves around a list of desired educational outcomes. In the application of this concept to instructional design in engineering, the outcomes are broken down into learning objectives [12, 13], subsequently learning activities are selected and delivered in order to achieve the learning outcomes. While the benefit of this concept lies in focussing both teaching and learning efforts and it ideally lends transparency to educational process, it also implies a deterministic and thus in some ways limited perspective on student learning. This paper contends that engineering students’ competence is influenced positively or negatively through the complex interaction of learning activities and influences from the wider educational context.
To investigate this phenomenon of Accidental Competency or In-competency formation [14], focus groups with engineering students from Germany, Australia, the US and Thailand were conducted. From the qualitative analysis of the transcripts, clusters with categories of influences and categories of resulting competencies were developed. Particularly prominent in the data set collected was the development of a cluster of competencies related to the development of students’ ability to flexibly work in a professional engineering context and develop creative solutions and products. This cluster comprised the following four competency categories: •
Engineering Pragmatism
•
Procedural Agility
•
Systems View
• Engineering Indeterminism After presenting details of the data gathering procedure and the interpretive data analysis method, we examine the competencies of the flexibility cluster in more detail. Through rich contextual descriptions and illustrative quotes from the data, we explore influencing factors and patterns of competence formation. On this basis we subsequently derive recommendations for engineering education to support the development of students’ flexible and creative problem solving.
2.
RESEARCH METHODOLOGY
The phenomenon of Accidental competency formation was investigated in focus groups [15] with engineering students using critical incident techniques [16]. Additionally, critical incident data from ongoing self-recording [17] of a cohort of industry placement students was collected. Using the qualitative analysis tool NVivo7, the data was subsequently analysed for categories of educational influences, work situations and competencies acquired by the students. 2.1. Data Gathering The data gathering consisted of eleven focus groups with a total number of 64 participants from Germany, Australia, the US and Thailand. The focus groups were based on a semi-structured protocol using critical incident techniques [16] to elicit instances of accidental learning. Critical incidents are detailed accounts of realworld experiences of the participants. In the area of competency research, critical incident techniques were shown to be more reliable than for example expert’s panel methods or the respondents’ self assessment [18]. In order to obtain a diverse data set in the sense of an exploratory study, the participants of the focus groups were selected from a wide range of innovative placement programs at different institutions in worldwide. Focus groups were conducted with students from: •
The Technische Universität Darmstadt, Germany (Structured six month internship program)
•
The University of Queensland, Australia (Six month industry placement program combined with an industry based final year thesis [19])
•
Purdue University, USA (Engineering Project In Community Service program (EPICS) [20], Global Engineering Alliance for Research and Education (GEARE) [21] and Co-op program [22])
•
The University of Georgia, USA (Vacation work and unstructured internships)
•
Prince of Songkla University, Thailand (Four to six month placement program in local industry)
2.2. Data Analysis and development of coding structure The focus groups were digitally recorded and transcribed verbatim. The transcripts were analysed using the qualitative data analysis tool NVivo7 [23]. The text was coded on the two levels of topic and of interpretive coding (see Figure 1). This represents the increasingly abstract interpretation from what Geertz [24] calls the “experience-near” to “experience-distant concepts.” The coding structure illustrated in Figure 1 contains the clusters of topic codes to categorize educational influences and types of work situations. The subsequent interpretation yielded clusters of categories to systemize types of Accidental Competencies. The first level of topic coding was based on an a priori structure of clusters and categories of educational influences and work situations, which were adjusted in the course of the analysis. The educational influences were captured in clusters for different learning activities, influences from the learning environment, aspects of the students’ disposition, so-called meta influences and various extra-curricular elements. The cluster of
learning environment, for example, contained a category for assessment to describe students’ accounts that were concerned with examinations or grading (Figure 1).
FIGURE 1: Coding model – topic and interpretive categories Similarly, the clusters of categories of work situations systemized student accounts that described ways in which their work impacted on their social life, various practicalities of the industrial context, instances of collaboration in the workplace, aspects of planning, types of technical work, and issues concerned with responsibilities and regulations. Each cluster again contained categories and subcategories, for example, to classify collaboration with various types of counterparts such as the students’ supervisors in the workplace. On the basis of this topic coding, the interpretive coding for competencies yielded seven clusters of Accidental Competencies. Included in each competency category are processes of competence formation that had both a beneficial (Accidental Competencies) and a non-beneficial (Accidental In-competency) impact on the students’ performance in professional practice. Clusters included competence categories related to flexibility, interaction, planning, dealing with professional realities, the self, the social context and technical work. The flexibility cluster, for example, contained the competency category engineering indeterminism to describe students’ ability to cope with ambiguity inherent to some engineering problems (Figure 1). The step of interpretive coding followed a grounded theory approach [25] and the categories emerged iteratively from the data. First interpretations or explanatory patterns, which were often vague and ill-defined, were coded “in-vivo” [26] using characteristic terms from the respondents’ utterances. The coding at this stage consisted of a collection of accounts that ‘somehow seemed to belong together’. For the engineering indeterminism category, for example, quotes were initially collated in the in-vivo category “No cross or tick”. This captured the essence of a set of accounts that were concerned with the students’ experience of ambiguous problems in industry. Those problems were perceived as different to the more defined tasks at university where, for example, the assessment implied the existence of one clearly defined answer. From these initial in-vivo descriptions, more defined and abstract categories were developed through the iterative process of “constant comparison” [27].
3.
FINDINGS AND DISCUSSION
The coding structure presented above (See Figure 1) represents explanatory patterns to categorise educational influences and work situations as inputs, and competencies as outcomes of the complex processes of competence formation. These explanatory patterns, however, can not provide a comprehensive description of the complexities of student learning. In particular, it is not implied that individual processes of competence formation follow a deterministic pattern that can be described for all cases – in other words the findings do not allow the prediction of specific outcomes with a given set the influences. This limitation is not necessarily due to such aspects as sample size or analysis procedure. Rather, the complex nature of the system under investigation precludes a comprehensive and predictive representation. Chilliers [28] aptly states that “to describe a complex
system you have, in a certain sense, to repeat the system”. One way to gain a deeper understanding of the complexities of student learning, beyond the explanatory patterns embedded in the coding structure, is to examine individual processes of competence formation through what Geertz [29] calls “thick descriptions”. This “tracing of narrative trajectories through the system” [28] can, for example, mean to consider a particular competency cluster as one outcome and examine the range of contributing influences and their interactions. 3.1. The engineering flexibility cluster In the context of this paper, we examine the development of students’ competencies in the engineering flexibility cluster. This cluster includes a number of competencies that contributed to the students’ ability to flexibly approach engineering problems and develop creative solutions. Figure 2 gives an overview of the four competencies of procedural agility, engineering indeterminism, engineering pragmatism and systems view and illustrates where they are located in the engineering problem solving process. In the following, we present a short overview of the four categories and Table 1 gives additional illustrative quotes for each category and lists the educational influences that were identified in the particular quote. After this general description, we turn to the procedural agility category to examine contributing influences and mechanisms of competence formation in detail. The students’ reflective accounts in this cluster were triggered by a number of differences they perceived between engineering problem solving in professional practice and their experiences of tasks at university. One element of problem solving in the industry context was the absence of a fixed problem solving schema and the resulting multitude of possible pathways to a solution (See Figure 2). We examine this aspect of procedural agility in more detail below. Beyond the absence of schematic ways to solve engineering problems, students also recalled situations when it was impossible to positively identify one correct solution to an industry task (See Figure 2). Their experience of this ambiguity was combined with an element of insecurity.
FIGURE 2: The competencies of the flexibility cluster and their function in creative engineering problem solving processes. Mortimer, a fourth year electrical engineering student, described this in the context of a team project in industry where he was hesitant to contribute to the solution because of what he recalled as a “skewed student mindset where the professors looking for one right of answer.” In other transcripts, the students similarly described this as an Accidental In-competency they had to overcome in their transition from education into professional
practice. The competency cluster of engineering indeterminism is thus defined by the awareness and acceptance of the uncertainty embedded in the multitude of possible solutions to problems of professional practice (For more detail see Table 1). After deciding on a solution to a problem, the students were confronted with a variety of other challenges they had not previously experienced at university. The common thread across accounts coded for engineering pragmatism cluster was the students’ realisation of inexact elements of professional engineering practice (See Figure 2). Hasslam, a 4th year mechatronics student, described this in context of finding non-analytic solutions to a dimensioning problem during his work experience: “If something breaks, like we had bucket problems, […] I was not sitting there and calculating anything. All I was doing is: 'Ok we want to make it a bit bigger”. The students described the deficiencies they had developed in this category not predominantly as a lack of practical skills. They rather experienced this Accidental In-competency as a mindset that prevented them from seeking non-analytic solutions or from accepting pragmatic solutions that were not accurate from an academic perspective (See also Table 1). An overarching element the students experienced when solving problems during their time in practice, was the need to consider wider aspects of a problem, particularly across disciplinary boundaries (See Figure 2). A number of participants reflected that they needed to overcome an initial tendency to see parts of a problem in isolation. The students identified disciplinary isolation of courses at university as a main influence towards this lack of a systems view. Adam, a fourth year mechanical engineering student, summarised this aptly as: “When you are at uni you just do thermodynamics or just dynamics.” In their industry experience, the students thus needed to develop strategies to take the interconnectedness of various parts of engineering problems into account when developing a solution.
Competency
Quote
Influences
Engineering Indeterminism
“We hand it in and get a right or wrong answer - get a tick or a cross. Whereas in the industry you can make a decision and they would say 'this is not what we prefer'. But it is not cross or tick.”
Assessment
Engineering Pragmatism
“There was definitely not a lot of information. It was just assuming different things and trying to get it to work.”
Procedural Agility
“There’s one way of doing things and it’s the way that they’re telling you. And so you might be at a class and the professors saying these are the steps you follow to solve the problem and these are the equations you use.”
Systems Approach
“And you don’t really consider those aspects at college. Because each course is kind of isolated.”
[Strephon, 4th year mechanical engineering student]
Work experience
[Katherine, 3rd year mechanical engineering student]
Delivery of learning content
[Hasslam, 4th year mechatronics student]
Degree Structure
[Mortimer, 4th year mechanical engineering student]
TABLE 1: Competence categories with illustrative quotes from the data and contributing influences 3.2. The development of students’ procedural agility In order to gain deeper insight into the development of competencies or in-competencies in the procedural agility category, we examine the interaction of a range of influences as illustrated in the influence model in Figure 3. A commonality in the student accounts in this category was an element of hesitancy or consternation when initially approaching problems or tasks in engineering practice. Cain, a fourth year chemical engineering student, described a situation in industry when he attempted to calculate certain variables in a heat exchanger system. Even though this task was superficially similar to typical classroom problems, the student’s experience in the industrial context was distinctly different. He expressed the element of consternation as “you find out you are not given a log mean temperature difference, you're not given all these values and they can actually fluctuate. So how do we actually deal with this?” (#1 in Figure 3). 3.2.1. Work situations This quote also identifies the ill-defined nature of the problem, caused by the lack of sufficient information, as one source of the students’ insecurity. The students described this contrast to tasks at university in statements such as “in school you get a project or a problem and everything seems to be pretty much there” (Mortimer). However, the underlying difficulties went beyond just the lack of information and concerned the procedure of problem solving. Students were unsure as to how to proceed towards a solution to the task at hand. Cain described this procedural component of the difficulties as: “How do we actively figure out ways how to do this?” This hints at the students’ realisation that some types of engineering problems did not follow a schematic
solution procedure. This lack of a prescribed way of solving a problem was affirmed by the students’ experience of collaboration in the workplace with peers or superiors. Katherine, a 4th year mechanical engineering student, described the contrast between university where the lecturer “had all the answers in his book, and was just waiting for me to get to the answer” and industry where her supervisor also “didn’t know how to do it […] and we were learning at the same time and just decided how to do it” (#2 in Figure 3). From these quotes emerged the realisation of the absence of a fixed problem solving schema as the critical learning element for the students. The accounts were thus collated in the in-vivo category “no recipe” using an expression directly from the participants’ contributions. In the further analysis, the strategies the students developed in overcoming this difficulty were conceptualised in the category procedural agility.
FIGURE 3: Influence model of the development of procedural agility and related competency categories 3.2.2. Educational Influences When exploring the reasons for the students’ lack of procedural agility in the educational context, it became apparent that students had developed an expectation that there are schematic ways to solve engineering problems. Hasslam described this perception as “you go to the lectures and they give you a set structure to solve a problem or come up with something” (#3 in Figure 3). This was embedded in the instruction of engineering fundamentals which is still predominantly conducted in “large classes and single-discipline, lecture-based delivery […], particularly in the early years of study” [30]. Other focus group participants described this as being amplified by assessment practices that reinforce procedural approaches to engineering problem solving: “At university we get an assignment […] and it will say ‘one mark for this, two marks for that, for this it is two marks.’ And you just work through that. […] With the company [we] were basically looking at the final piece and were saying 'if you are having trouble here, modify your solution so that you get to […] something that works’” [Hasslam] (#4 in Figure 3) Other students reported that schematic problem solving is also embedded in particular understandings of engineering that were espoused at the respective institutions. A student reports this explicit imparting of a schematic perception of doing engineering as: “There’s one way of doing things and it’s the way that they’re telling you. […] The professor says ‘these are the steps you follow to solve the problem’” [Mortimer] (#5 in Figure 3). Referring to non-schematic or what he calls ‘work around solutions’, Hasslam recalls an incident when a lecturer did not accept a piece of student work because “they are never happy with the work-around solution, because that's not what they taught you” (#5 in Figure 3). Problem or project based learning is frequently suggested as a mean to promote the development of students’ independent problem solving skills [30-32]. Project work at university and its connection to the students’ experiences in practice also played a significant role in the focus group accounts. In the following, we examine the role of problem based learning more closely and particularly focus on how other factors impact on the effectiveness of this form of active learning.
A number of students confirm the positive impact of problem based group work in preparing them for solving ambiguous problems in industry. Conrad, a fourth year mechanical engineering student, remembers that particularly design projects at university created open-ended ‘real-world’ situations: “As you get to fourth year you have a lot of courses on design. And, you have to choose what shape it is or what formula you have to use […] - you basically start with a clean slate. […] I mean, it was something we had to face which was really big.” (#6 in Figure 3) However, this quote also indicates that for this particular student, this learning experience occurred relatively late in the program. This means that at that point in their learning, students had to overcome some of the negative impacts of the schematic learning described earlier. This observation corresponds with the increasing calls in the literature to introduce problem or project based approaches into the early years of engineering programmes [30, 32]. The positive effects of the open-endedness of problem based approaches and particularly student design projects were, however, diminished by a number of other influences. As a result of these influences, Franklyn a 4th year electrical engineering student, observed: “it ends up that everyone is doing the same thing. So it kind of goes back to the old follow the structure thing” (#7 in Figure 3). This statement points to the crucial aspect of the influence of the cohort in reducing the emotional component of insecurity that the students described in the context of industry projects. Hasslam confirms: “In […] team projects […] you are basically on your own and have to work out what you are going to do. […] But at the same time you've got six or seven other teams doing the same project.” (#7 in Figure 3) Assessment proved to be another compounding influence in diminishing the students’ benefits from group projects. Franklyn acknowledges the attempt to create a realistic problem situation in a final year design project: “They actually say this is open-ended you are not going to come up with a perfect solution.” However, the assessment practice in the particular course did not support the intended flexibility in the project. The student remembers: “On the website there was a structure of all the headings you should have for each section. I mean, they still tell me what to do. It's open-ended but you are still told what has to be in it” (#4 in Figure 3).
4.
RECOMMENDATIONS FOR ENGINEERING EDUCATION
The analysis of the competence categories in the flexibility cluster revealed a number of Accidental Incompetencies that students acquired from the interplay of learning activities and other influences from the educational context. Explicit attempts to foster students’ flexibility such as open-ended project based learning showed beneficial outcomes. However, these learning opportunities occurred relatively late for some students and by that time they already had to ‘unlearn’ some of the consequences of the earlier schematic, singlediscipline learning. Additionally, some practicalities of the educational process such as the cohort or assessment diminished the effectiveness of these active forms of learning. On a more general level, this means that student competence formation in engineering programs occurs within a complex social learning system. As a consequence, engineering educators cannot control all elements and outcomes in a deterministic sense. However, in looking towards the students’ entire university experience as a source of learning, there are a number of opportunities to influence and develop some aspects of students’ competence that cannot be accessed through explicit teaching. More specifically, this systems view of student learning implies the need to create situations that offer students the widest possible range of learning opportunities. This can be implemented in project or problem based approaches. However, the results of this study suggest that it is crucial to see these learning experiences embedded in the entire learning context. This specifically means that engineering educators need to pay careful attention to possible negative interactions with other influences and capitalize on possible synergies. One concrete way to achieve this is to generate awareness of the unintended learning processes that are crucial in the development of engineering students’ professional competence. This entails the need for reflective teaching practice where engineering educators are attuned to the subtleties and complexities of student learning and try to avoid negative learning effects and foster beneficial processes. In this context, students can be equal partners in the learning process in that they are aware of the goals and the limitations of their university learning. In similar ways to the research focus groups presented here, this can be achieved in explicitly guiding students in their reflection on the impact of the entire university experience on their formation as professional engineers [33]. Acknowledgements The study reported here was supported by the University of Queensland’s Graduate School Research Travel Grant and research travel funding from the Studienstiftung Des Deutschen Volkes.
Most importantly, we would like to acknowledge the invaluable input of the students in Germany, Australia, the US and Thailand who participated so enthusiastically in the research focus groups. For the Thailand study, I1 am deeply grateful for the generous assistance and the warm welcome I received from Dean Puwadon Bootrat and the Faculty of Technology and Environment at the Prince of Songkla University in Phuket, Thailand. This work would not have been possible without the tireless support from Naiyana Srichai, Pun Thongchumnum and my collaborator Chantinee Boonchai in organizing my stay, conducting the research and affording me a rare inside glimpse of Thai culture through the numerous patient explanations. For my American visit, I would like to acknowledge the invaluable help of Robin Adams, Llewellyn Mann, Carla Zoltowski, William Oakes, Eckhard Groll and Robert Stwalley not only in conducting the part of the study at Purdue University, but also making my stay there a very rewarding and stimulating experience. Thanks must also go to Nadia Kellam for the cordial reception to the University of Georgia and her support in conducting the research focus groups.
1
Refers to the primary author of the paper.
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