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Journal of Engineering EducationOctober 2011, Vol. 100, No. 4, pp. 703–740

© 2011 ASEE. http://www.jee.org

Engineering Competence? An Interpretive

Investigation of Engineering Students’Professional Formation

JOACHIM W ALTHER , N ADIA K ELLAM, NICOLA SOCHACKA, AND D AVID R ADCLIFFEa

University of Georgia, Purdue University a

B ACKGROUND

There is growing evidence that engineering students’ professional formation is shaped by the inter-play of explicit learning activities and various influences from the wider educational context. Theunintended outcomes of these processes, or Accidental Competencies, formed the lens for an empir-ical investigation of this social learning system.

PURPOSE (H YPOTHESIS) The exploratory inquiry addressed the following research questions. What are influences that contributeto engineering students’ professional formation? How does it occur and what are resulting competenciesand incompetencies

DESIGN/METHOD

Data was collected internationally in focus groups with 67 students in their transition from university intoprofessional practice. The students’ accounts were analyzed qualitatively using the software NVivo7. Fromthe iterative analysis based on a grounded theory approach, categories and subordinate clusters of influ-ences, mechanisms, and outcomes emerged.

R ESULTS

The following three forms of representation provide an authentic view of the social learning system underinvestigation: (i) a contextual model of competency formation illustrates the complex nature of the learn-ing processes; (ii) an overview of the coding structure presents seven competence clusters (Flexibility,Interaction, Plan, Professional Realities, Self, Social Context and Technical); and (iii) thick descriptionsfrom the students’ accounts trace three characteristics of the complex learning processes (compound influ-ences, ambiguity of outcomes, context-dependent nature of learning outcomes).

CONCLUSION

Engineering education is a complex system where a range of influences outside the realm of explicitinstruction contribute to the development of students as professional engineers. This study provides anevidence-based framework to consider this complexity in reflective teaching practice and innovative cur-riculum design.

K EYWORDS

accidental competencies, interpretive research, professional formation

INTRODUCTION

We had to get the machines working. ... It was the deadline. Right! It was thefinal exam! (Bob Kahn, in a radio interview, January 30, 2006)

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Journal of Engineering Education 100 (October 2011) 4

In this quote, Bob Kahn, a co-designer of the TCP/IP protocol, recounts the first pub-lic demonstration of a computer network using the Internet Network Protocol at a 1972conference in Washington. In describing this most crucial moment of his professional ca-

reer, Kahn recalls and explicitly mentions the effect of years of taking exams at university.Noticeably absent from this quote is any reference to the content of courses, although thispresumably also came to bear on the project. Rather, the parallel which strikes Kahn in thismoment concerns the tensions and pressures associated with the “final exam,” an educa-tional influence which may very well have equipped Kahn with the skills required to meetcritical deadlines in his professional career.

In this study we conceptualize such learning processes as Accidental Competency for-mation. The following demonstrates that this notion provides an ideal lens to empirically investigate how the complex interplay of teaching activities and influences from the widereducational context impacts on the development of engineering students’ professional for-

mation. The following three characteristics of such learning processes can be identifiedfrom the quote by Kahn. These aspects also mirror the three areas that outline the contextand background of this study as discussed in the next section:

• The learning connects to non-traditional aspects of engineering work (“cramming”in the example here) and is crucial for professional competence. This links to discus-sions concerned with a broader scope of engineering education and the necessity of preparing students for professional competence.

• The learning is not the result of formal, intentional instruction. This points to someof the tensions and persisting difficulties of the construct of outcomes-based educa-tion as the current paradigm of formal engineering instruction.

• The learning is influenced by factors from the wider educational context. This sug-gests the necessity to explore broader perspectives of student learning as emergentfrom the social learning environment.

To empirically investigate this holistic perspective on engineering students’ professionalformation, the exploratory study presented here used an interpretive research approach.Focus groups based on critical incident techniques (McClelland, 1973, 1998; Spencer & Spencer, 1993) were conducted at institutions in Germany, Australia, the United States,and Thailand with 68 engineering students in their transition from university into the workplace. Transcripts of the focus groups were analyzed using the qualitative researchtool NVivo7 (Bazeley, 2007; Richards, 1999). Based on a grounded theory approach

(Glaser & Strauss, 1967), the iterative data analysis process yielded a set of seven compe-tence clusters (Flexibility, Interaction, Plan, Professional Realities, Self, Social Contextand Technical), each of which contained three to four competence categories (e.g., Eco-nomic Awareness in the Professional Realities cluster).

This paper describes in detail the research design and methodology with a particularfocus on the iterative process of data analysis. The results are presented in three forms toprovide an authentic representation of the social learning systems under investigation: (i) acontextual model of competence formation illustrates the complex nature of the learningprocesses; (ii) an overview of the clusters and categories of Accidental Competencies provides a view on the range of outcomes; and (iii) “thick descriptions” (Geertz, 1973) from

the data illustrate the abstract representations and trace narrative trajectories through thesystem. The overall results, and in particular the complexity of student learning that wasuncovered, are discussed in the context of the current challenges in engineering educationand are explored in their usefulness for teaching practice as well as for curriculum design.

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CONTEXTANDB ACKGROUND

In the twenty-first century, the world is facing a number of unprecedented challenges

which have led to a sustained transformation of the discipline of engineering (American So-ciety for Engineering Education (ASEE) 1994; Engineers Australia (EA), 1996; Fortenber-ry, 2006; Journal of Engineering Education, 2006). A decade ago, the engineering educationcommunity anticipated these challenges and initiated a paradigm shift towards an outcomes-based education system with a focus on broader graduate attributes (ABET, 1995; EA,1996). Prominent examples of this development are both the 1994 ASEE report Engineering

Education for a Changing World (ASEE, 1994) and Engineers Australia’s 1996 review Chang-ing the Culture: Engineering Education Into the Future (EA, 1996). Recent examples are thedevelopment of a Malaysian outcomes-based model (Johari, Abdulla, Aziz, & Jaafar, 2005; Johari et al., 2002) and discussions on adopting an outcomes-based approach to accreditation

and mutual recognition of engineering programs within the framework of the EuropeanBologna Process (Walther, Mann, & Radcliffe, 2005). Recent reports, however, suggest thatthe transformation of engineering education to prepare students for current and future pro-fessional practice remains a “formidable challenge” (Splitt, 2003).

A Broader Scope of Engineering Education and Preparationfor Professional Competence

Engineering and technology are likely to play a major role in addressing the previously mentioned challenges on a global economic, environmental, and social level (AmericanSociety of Civil Engineers (ASCE), 2004; National Academy of Engineering, 2005;

Spinks, Silburn, & Birchall, 2006; The Royal Academy of Engineering, 2006). This inter-links the engineering profession more closely with a wider range of stakeholders and soci-ety at large (Committee on Public Understanding of Engineering Messages, 2008;Salganik & Rychen, 2003) and engineers are consequently faced with questions concern-ing the social and ethical implications of their work (ASEE, 1994; EA, 1996). Engineer-ing graduates today are thus expected to be equipped not only with a whole set of new technical abilities to keep pace with the rapid technological developments but also with arange of broader attributes to be able to assume the expanding responsibilities of the pro-fession (ASEE, 1994; EA, 1996; National Academy of Sciences, National Academy of Engineering, & Institute of Medicine, 2007; Wormley, 2006).

For engineering education, such expectations have driven a fundamental shift fromtransmitting technical content knowledge to the urgent need for educating for broadercompetencies which concern students’ attitudes and values (ASCE, 2004). This has led toa rethinking of the goals of engineering education (Besterfield-Sacre et al., 2000) and a widening of the scope of engineering programs to include broader aspects such as students’“understanding of the social, economic, and environmental consequences of professionalengineering activities” (EA, 1996, p. 7) (and similarly in: ABET, 2004). This broader defi-nition of the educational goals also entails an explicit commitment to the preparation of students for current professional practice (EA, 1996). For example, Lemaitre, Prat, Graaff,and Bot (2006) confirm that the preparation “of students for professional competence has

always been the ultimate goal of engineering curricula” (p. 45).

Outcomes-Based Engineering Education and Persisting Difficulties The transformation of the overall goals of engineering education was accompanied by a

shift of the instructional paradigm toward outcomes-based education. More specifically,

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what was previously an input, content, and process-oriented curriculum was transformed towards a system based on educational outcomes. In the course of this transition, the aspira-tional student attributes postulated in both the American and Australian reports were devel-

oped into binding outcomes of the educational process as outlined in the ABET ProgramOutcomes (1995, 2004) and the EA list of Graduate Attributes (EA, 2005). At a time when authorities in both Australia and the United States are reviewing the

operation and success of outcomes-based education in engineering (King, 2008), severalauthors indicate that engineering education still falls short of the goal of adequately prepar-ing students for professional practice (Atman et al., 2010; Lord, 2010; Vanasura, Stolk, & Herter, 2009). A recent report of the Business Council of Australia (BCA, 2006), an orga-nization representing the leading one hundred corporations in Australia, for example,points out that engineering graduates have deficiencies with respect to crucial job skillssuch as “problem-solving, communication, and entrepreneurship” (p. 14). In the United

States, Wulf and colleagues (Wulf & Fischer, 2006; Wulf & Fisher, 2002) equally indicatethat “many of the students who make it to graduation enter the workforce ill-equipped forthe complex interactions [...] of real world engineering systems” (p. 35). This suggests thatindustry requires more adequate preparation of students for the complex and socially situ-ated job aspects of real-world engineering (Jonassen, Strobel, & Lee, 2006). Conversely,“much of the energy in teaching and learning in universities is still focused on developingthe observable skills and knowledge dimension” (Radcliffe, 2005), rather than the less easi-ly observable attributes required by industry.

Towards a Holistic Perspective of Student Learning

This disconnectedness (Walther & Radcliffe, 2007b) shows that the concept of educa-tional outcomes in today’s application to engineering education has not been able to ade-quately prepare students to be part of a profession that is grappling to “fully assume its ex-panding responsibility” (EA, 1996, p. 7). It also suggests that the development of broader,attitudinal aspects of competence is not sufficiently part of engineering students’ overalllearning experience. This is not merely a question of the quality of implementation of theconcept of outcomes-based education but rather points to conceptual difficulties in defin-ing the construct in the context of engineering learning (Besterfield-Sacre, et al., 2000).

The notion that the concept of outcomes-based education might be limited in its ca-pacity to capture all aspects of engineering learning is supported by growing evidence sug-

gesting that a wide range of educational factors interact in a complex fashion to impact stu-dents’ professional formation on the level of both specific learning outcomes andintangible, attitudinal aspects (Dall’alba & Sandberg, 1996; Davis & Sumara, 2006; Doll,Fleener, Trueit, & Julien, 2005; Karen, 2006; Mason, 2008; Scott & Yates, 2002; Stevens,O’Connor, Garrison, Jocuns, & Amos, 2008; Walther, Boonchai, & Radcliffe, 2008; Walther & Radcliffe, 2006b).

To explore this broader notion of student learning and its implications for currentmodels of engineering education, this study employs the lens of Accidental Competency formation to systematically investigate students’ professional formation within the sociallearning environment of engineering programs.

Aspects of Accidental Competency Formation in Educational Research To provide a better understanding of this concept, the following outlines areas of

educational research that have explored facets of what we have termed AccidentalCompetency formation.

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One example for conceptions of unintended learning is the notion of the hidden cur-riculum (Jackson, 1968; Snyder, 1970) which refers to “those things pupils learn throughthe experience of attending school rather than the stated educational objectives of such in-

stitutions” (Haralambos, 1996). Contributions in this area usually maintain an aspect of strong social critique and focus on social implications, political underpinnings, and culturaloutcomes that are imparted to students to produce conformist citizens of society. Takingthe notion of the hidden curriculum into the context of higher education, Snyder (1970)observes, for example, that students develop strategies to cope with excessive workloads by neglecting portions of the formal workload and he interprets this “selective negligence” as anegative outcome. The introductory quote by Bob Kahn, however, illustrated that thesame workload pressures can also serve as a valuable preparation for coping with the daily requirements of professional practice. This points to the complexity of the learningprocesses that are explored in this study.

On a broader level, the notion of “Trained Incapacity” (Veblen, 1918) describes thatthe increasing specialization in education leads to a narrowing focus of the students’overall professional competence. As an example from business education, Veblen ob-serves that “transactions are carried out with an eye single to pecuniary gain, the industri-al consequences, and their bearing on the community’s welfare being matters incidentalto the transaction of business” (p. 351). Considering the discussions on broader attribut-es of engineering students and the necessity of social awareness, this early work appearsstartlingly current in voicing the concern that broader aspects of education could get lostor even suppressed in the teaching of technical content. In a similar vein, the Engineers Australia report Changing the Culture (1996) cautions that “the present emphasis placed

on engineering science resulting in graduates with high technical capability, has oftenacted to limit their appreciation of the broader role of engineering professionals” (p. 7). This effect of a narrowing of the students’ intellectual horizon as their specialized disci-plinary knowledge increases was also identified as an Accidental Incompetency in thedata of this study.

In the area of engineering education, Scott and Yates (2002) report a study to “identify capabilities [...] important to engineering practice” (p. 363) and identify a number of im-portant competencies outside the scope of the stated graduate attributes. Commenting onthe origin of these capabilities, the authors remark that it is important to “focus on the en-tire undergraduate experience rather than just what is taught” (p. 363). Without a system-

atic distinction between intentional learning outcomes and alternative forms of learning,the article reports, for example, that “the ability to network, use peer support, and developinterpersonal skills can receive support from the whole range of formal and informal expe-riences encountered whilst at university” (p. 372) (similarly in Karen, 2006).

In the context of continuous learning in the workplace, the notion of incidental learn-ing (Marsick & Watkins, 1990) focuses on the acquisition of competencies that are unin-tended and unrelated to the specific tasks accomplished by individuals as part of their work. As this concept does not focus on structured learning environments, it can be seen as ex-tending the concept of Accidental Competency formation into the realm of life-longlearning.

These examples demonstrate that particular aspects of Accidental Competencies havebeen explored in specific contexts of learning. This link to existing theories offers an op-portunity for a broader understanding of the notion presented here and at the same time validates the theoretical construct developed as a result of this study (Apel, 1972; Kirk & Miller, 1986).

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R ESEARCH Q UESTIONS

To explore the issues discussed previously concerning both the holistic conception of

student learning and the broader goals of engineering education that specifically focus onprofessional competence, the following specific research questions were developed:

1. What are influences that contribute to engineering students’ formation of profes-sional competence?

2. How does competence formation occur?3. What are competencies, or incompetencies, that engineering students develop as a

result of their entire university experience?

The contribution of this work is two-fold. It concerns (i) the process of competenceformation as well as (ii) the nature of professional engineering competence.

(i)In order to attempt an exploratory, yet systematic, examination of such learningprocesses, this study employed a complex systems perspective to holistically investi-gate engineering students’ competence formation in the broader educationalcontext. The notion of Accidental Competency formation provided the lens for theinquiry. This led to the development of a contextual model of competency forma-tion detailing categories of influences and mechanisms of student competence for-mation (see results section).

(ii)The investigation of student competence formation in their transition from theuniversity into the workplace inherently spans the nexus between education andpractice to examine the nature of professional engineering competence. This as-pect of the inquiry produced a categorization of elements of professional engi-neering competence including detailed competence descriptions (see results sec-tion). This is, however, not intended as a comprehensive listing of competencies.Rather, it focuses on broader non-technical attributes and, in particular, aspectsoutside the scope of intentional learning outcomes that emerged from this partic-ular data set.

METHODOLOGY

The goal of this study was to empirically investigate student learning in engineeringprograms through the lens of Accidental Competency formation. Following an interpre-tive methodology, this phenomenon was investigated in focus groups (Barbour, 2007;Morgan, 1988; Morgan, Krueger, & King, 1998) with engineering students and graduateengineers. The semi-structured focus group procedure was based on critical incident tech-niques (Boyatzis, 1982; McClelland, 1973, 1998; Spencer & Spencer, 1993) to elicit stu-dents’ learning experiences. Using the qualitative data analysis tool NVivo 7 (Bazeley,2007; Bazeley & Richards, 2000; Richards, 1999), the focus groups were analyzed for cate-gories of educational influences, work situations and competencies discussed by the stu-dents. Figure 1 illustrates the stages of the research process with a focus on the data analy-

sis. Following this outline is a description of the research design and procedures for datagathering and data analysis in more detail.

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Data Gathering In the course of this study, nine focus groups with a total number of 67 participants

from Germany, Australia, the United States, and Thailand were conducted. All respon-dents were graduate engineers or final year engineering students who had had at least fourmonths of experience in the workplace or in a situated learning program, which encom-passes internships, co-op programs, service learning programs, and engineering vacation work. This was to ensure that participants were sufficiently close to their educational expe-rience as to be able to recall detailed incidents. Yet, the students’ experience of engineeringpractice in the various programs ensured the relevance of the competence concepts for pro-

fessional engineering thus bridging the gap between education and practice (Walther & Radcliffe, 2007b). An overview of the participant’s discipline, country of study, and genderis presented in Figure 2. The focus groups were conducted face to face by the primaryauthor with participants from one of the situated learning programs listed below.

An additional nine Australian students submitted a weekly written reflection on criticallearning experiences while taking part in a semester-long placement program. This self-recording was a concurrent form of data gathering that “can capture personal information atthe point it occurs, structure it to be most meaningful, preserve its accuracy without need forintrusive rehearsal, and provide a longer data base for discerning evidence of progress”(Hartman, 2001, p. 211). The alternative data source was chosen as a form of triangulation

to collect data outside the interactive focus group format. Based on the observation that theincidents reported in either formats of data collection were similar in nature indicates thatthe phenomena observed were not artifacts of the dynamics in the focus groups.

Selection strategies. In order to obtain a diverse data set in the sense of an exploratory study, the participants for the focus group were selected from a wide range of programsthat offered situated learning experiences at different institutions in Germany, Australia,the United States, and Thailand. Gibbs (2007) refers to this as a form of triangulation toincrease validity of the research by collecting “geographically disparate data” (p. 94). Re-spondents from this range of programs that included more traditional internships but also

innovative service learning and international placement programs (see EPICS andGEARE on the next page) were likely to contribute diverse experiences that also coveredadvanced or non-traditional fields of engineering practice. This strategy of “extreme sam-pling” (Babbie, 2008, p. 182) thus allowed capturing competencies that are future orientedand representative of the on-going changes of engineering practice, rather than being

F IGURE 1. Stages of the research process and details of the interpretive data analysis.

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solely a reflection of the current educational system. As an example, the students from aservice learning program contributed unique experiences concerning their personal recog-nition of the societal responsibilities of engineers. An exploration of the experiences of these students is thus likely to lead to a deeper understanding of “the social [and] culturalresponsibilities of the professional engineer” (EA, 2005, p. 3).

With the goals of geographically disparate data and extreme sampling in mind, thefocus groups were conducted with:

• Graduates from the Technische Universität Darmstadt (Germany), who had com-pleted a six month internship program which covered prescribed areas of industrial work and included structured reporting activities during their time at university. These engineering graduates had also worked for up to a year in different engineer-ing fields after graduation.

• Final year students from the University of Queensland who had completed a sixmonth industry placement program that combined work experience with the com-pletion of an industry based final year thesis (Radcliffe, 2002).

• Students at Purdue University from the Engineering Projects In Community Ser-

vice program (EPICS) (Coyle, Jamieson, & Oakes, 2005; Coyle, Jamieson, & Sommers, 1997), the Global Engineering Alliance for Research and Education(GEARE) (Allert, Atkinson, Groll, & Hirleman, 2007; Groll, Krousgrill, Meckl,& Hirleman, 2006) and the Co-op program (Stwalley, 2006).

• Final year students from the University of Georgia who had gathered their industry experience in vacation work and internships.

• Students from the Prince of Songkla University in Thailand, who had completed afour months internship in their final year of study.

This selection followed a purposive sampling strategy, whereby the researcher “selectsthe units to be observed on the basis of [a] judgment about which ones will be the most

useful” (Babbie, 2008, p. 189). Morgan et al. (1998) identify purposive sampling as themost suitable strategy for focus group research. The choice of participants in this study wasthus not determined to allow for a statistical analysis but aimed at increasing the“likelihood that the full array of multiple realities will be uncovered” (Lincoln & Guba,1985, p. 40).

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F IGURE 2. Participant details by country, discipline, and gender.

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Critical incident focus groups. The focus groups were based on a semi-structured pro-tocol using critical incident techniques (Boyatzis, 1982; McClelland, 1973; Spencer & Spencer, 1993) to elicit instances of accidental learning. Critical incidents are detailed ac-

counts of real-world experiences of the participants. In the area of competency research,critical incident techniques are shown to be more reliable than, for example, expert panelmethods or respondents’ self-assessment (McClelland, 1973; McClelland, 1998; Spencer& Spencer, 1993). The focus on detailed descriptions of incidents from the students’ timein practice or at university thus mitigated the influences of “espoused beliefs” (Schoen,1983), inaccurate self-assessment (Boyatzis, 1982; McClelland, 1998; Spencer & Spencer,1993) or “self-report bias” (Maxwell, 2005) on the quality of the data.

To elicit students’ accounts of critical learning experiences, the following three types of triggers were used (for more information on the focus group procedure and its use as a re-flective tool see Walther, Kellam, Radcliffe, & Boonchai, 2009; Walther & Radcliffe,

2007a). Nonspecific triggers - competence anomalies. The first stage of the protocol used a number

of non-specific trigger statements (e.g. “When doing this job at work I suddenly remem-bered a particular situation at university.”) to elicit students memories of what Walther andRadcliffe (2007a) call “competence anomalies.” These moments are characterized by thestudents’ vague feelings of surprise or consternation when prior understandings of engineer-ing that they had acquired at university were challenged by a situation encountered in prac-tice (Dewey, 1933). This provides students with intuitive access to critical learning experi-ences and does not limit their contributions to preconceived categories of competence orforms of learning. In this part of the focus groups the students freely contributed accounts

that concerned a wide range of often unforeseen aspects of competence formation.

Specific triggers - interactive incident recall. The second phase of the protocol made useof the dynamic of the focus groups, in that any aspect of one participant’s account couldserve as a trigger for other students’ memories of learning incidents. A particular focus infacilitating the focus group was to reinforce this openness of the discussion so that students were not limited to contributions similar in content or nature to other participants’accounts.

Abstract triggers - competence descriptors. Once the two previous stages were exhaustedin their capacity to elicit further accounts, abstract competence descriptors were intro-duced. This phase was thus limited to pre-determined aspects of engineering competenceand was used to investigate particular facets or further explore trends observed in otherfocus groups.

For the subsequent analysis, the focus groups were audio-recorded and transcribed inde-identified form. The research procedures adhered to the guidelines set out by the Australian National Health and Medical Research Council (NHMRC, 1999) with respectto participants’ informed consent relating to voluntary participation and confidentiality.

Data Analysis Analysis of the focus group data proceeded iteratively from understanding and collating

students’ multiple perspectives to deriving abstract categories of influences, mechanisms,and outcomes of Accidental Competence formation (see Figure 1). Geertz (1974) de-scribes this essential process of interpretive research as “grasp[ing] concepts which, for an-other people, are experience-near, and do so well enough to place them in illuminating

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connection with those experience-distant concepts that theorists have fashioned to capturethe general features of social life” (p. 29). This iterative progression was implementedthrough coding on two levels of increasing abstraction. Topic coding was used to capture

the content or themes in students’ accounts and interpretive coding was used to developabstract categories. Distinguishing the two levels of coding of text allowed a gradualprocess of interpretation and was additionally a step in establishing reliability of the inter-pretation. Flick (2006) confirms that “the data needs to be explicated in a way that makes itpossible to check what is a statement of the subject and where the researcher’s interpreta-tion begins” (p. 370). The following gives an overview of the codes (see Figure 3) to provide the context for the subsequent description of the analytic procedures that were em-ployed in this study.

Overview of Coding Structure. To capture both the educational and the professionalcontext of the student accounts for the later interpretation of competencies, the topic cod-

ing consisted of clusters and subordinate categories of educational influences and work sit-uations (see Figure 3). This step did not require significant interpretation and the cate-gories employed were largely descriptive (Flick, 2006). For the topic coding, a set of a priori categories were developed from experience and the broader literature and after this itera-tively adjusted in the course of the analysis.

Figure 3 illustrates the progression from topic coding to interpretive coding. The treestructures on the left side contain the clusters of work situations (top) and educational influ-ences (bottom) each with examples of subordinate categories that describe more specificfactors that impacted students’ learning. The tree structure on the right lists the results of the interpretation as clusters and categories of accidental competencies . In this schema every

incident described by a student was analyzed for the aspects of work situation or education-al experiences the student described. Based on this categorization, the next step of theanalysis concluded one or more categories of competencies that the student had developedin this particular situation.

The clusters of categories forwork situations systemized student accounts that described ways in which their work impacted their social life , various practicalities of the industrialcontext, instances of collaboration in the workplace, aspects of planning , types of technical work, and issues concerned with responsibilities and regulations (see Figure 3). Each clusteragain contained categories and subcategories to classify, for example, collaboration with various types of counterparts such as the students’ supervisors in the workplace.

Similarly, the codes for educational influences were clustered into types of learning activi-ties, influences from the learning environment (Karen, 2006), aspects of the students’ disposi-tion (Atman et al., 2008), so-called meta influences (Stevens, et al., 2008) and various extra-curricular elements (see Figure 3). For example, the cluster of learning environmentcontained a category for assessment to describe students’ accounts that were concerned withexaminations or grading.

On the basis of this topic coding, the interpretive coding for competencies (for de-tailed processes see the following section) yielded seven clusters of Accidental Compe-tencies (see Figure 3). More specifically, this means that from a student account whichrelated an educational experience to a situation in the work place, it was concluded which competency was relevant in this context. Included in each competency category are processes of competence formation that had either a beneficial (Accidental Com-petencies) or a non-beneficial (Accidental Incompetency) impact on the students’performance in the professional context. The seven clusters included competence

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categories related to flexibility, interaction, planning, dealing with professional realities ,the self , the social context, and technical work. The flexibility cluster, for example, con-tained the competency category engineering indeterminism to describe students’ ability to cope with ambiguity inherent to some engineering problems (more detail is pre-

sented in the results section).Processes of the interpretive analysis. The interpretive coding followed a grounded

theory approach of analysis (Charmaz, 2006; Glaser & Strauss, 1967; Hatch, 2002;Lincoln & Guba, 1985). The described competence clusters and categories emergedfrom an iterative process of discovering and confirming patterns in the students’ ac-counts. Richards (2005) calls this “up from the data.” This sustained involvement withthe data allowed for interpretations to emerge gradually from the participants’ multi-ple perspectives without being limited by pre-defined or existing theories or conceptsof competence. This approach was ideal in taking into account the complexity and

richness of the social learning system under investigation (Kellam & Walther, 2009; Walther & Radcliffe, 2006a). As discussed by Flick (2006), “the aim [of interpretiveresearch] is not to reduce complexity by breaking it down into variables but rather toincrease complexity by including context” (p. 98).

F IGURE 3. Topic and interpretive coding - overview of Work Situations, Educational In-fluences, and Accidental Competencies with example categories.

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While the interpretations are necessarily subjective, the sustained and systematic involvement with the data by the researcher1 through the strategies described ensured consis-tency of the coding. Additionally, the emerging categories were discussed at regular

intervals with the other members of the research team to provide an outside perspective onthe interpretations. The following describes the detailed processes and strategies employedin this study to support the iterative emergence of the interpretive categories.

Gradual abstraction and the use of in-vivo descriptors. On the basis of the completedtopic codes, the first interpretive codes were created as free nodes, initially without the finalhierarchical structure presented here. These first interpretations or explanatory patterns were often vague and ill-defined. To attempt a high degree of abstraction in the early de-scription of the categories was even found to be detrimental to the analysis. In order toprovide the necessary vagueness and yet remain as close as possible to the participants’experience-near constructs, “in-vivo” (Richards, 2005, p. 95) descriptors were used for the

early categories. This means that categories were defined using the participants’ utterances which allowed for a gradual process of abstraction towards the experience-distant theoreti-cal constructs.

As an illustration for the use of in-vivo codes, the following examines the developmentof the engineering indeterminism category. During early iterations of analysis, the category captured a number of accounts in which students expressed feelings of uncertainty con-cerning various aspects of their work experiences and linked this to a number of differenteducational influences. The coding at this stage consisted of a collection of accounts thatsomehow seemed to belong together. Early abstract descriptions of the category were notable to capture its core meaning and had to be redefined and reordered a number of times.

In one of the accounts, a student described a typical situation in the workplace as “It is notcross or tick. It is not one out of one or zero out of one,” referring to a perception of gradingat university that clearly identifies an answer or solution as right or wrong. Using ‘No Crossor Tick’ as an in-vivo category name captured a range of ambiguous work situations andalso hinted at the source of the students’ difficulties from the educational context. On thebasis of the accounts coded for this in-vivo category, the abstract descriptor of engineering indeterminism was eventually developed to encompass incidents that were concerned withstudents developing an awareness and acceptance of, and strategies to deal with, ambigui-ty, true uncertainty, and the multitude of possible outcomes inherent in real world engi-neering problems (Jonassen, et al., 2006).

Continuous documentation to support reliable interpretation. While the openness of thecategories was crucial in the early stages of interpretation, it was, at the same time, impor-tant to explicitly record the current definition of a category at increasing levels of abstrac-tion to form the basis for subsequent coding decisions and avoid what Gibbs (2007) calls a“definitional drift in coding” (p. 98). For this purpose, memos linked to each category werecreated in a standardized format. These were used to make the considerations for the for-mation of a category explicit, to record the rationale behind the coding decisions, and todiscuss peculiarities, exceptions, or “outliers” in the data. The category memos were alsoused to discuss the category’s relation to other categories and the reasons why it was dis-

tinct from related categories. The overall coding process and the development of the

1 The primary author was a graduate student of engineering education research at the time of the study with an undergraduate background in mechanical engineering.

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cluster structure were recorded in a reflexive log trail. This document was used in a chrono-logical manner to capture overall observations about the data and hunches of explanatory patterns in the form of potential coding categories. At the same time, the reflexive log trail

served as a repository for reflections on the process of analysis and the influence of valuesand preconceptions resulting in a potential bias when interpreting the data (Sandberg,2005; Sochacka, Walther, Kavanagh, & Jolly, 2009). Richards (2005) similarly states theimportance of a reflective documentation as “telling and writing the processes of handlingdata to inform claims of validity” (p. 5).

Constant comparative method to define, collate and delineate categories. The definition of categories, their delineation, and the cluster structure was developed through variousprocesses of comparison of coded data. This involved an iterative process of re-reading thetranscripts and different ways of crosschecking of categories. This process is formally de-scribed as the “constant comparative method” (Glaser & Strauss, 1967) by “which re-

searchers engage in detailed analytic processes that require repeated confirmations of po-tential explanatory patterns discovered in the data” (Hatch, 2002). The iterations of theconstant comparative method include “(1) comparing incidents applicable to each catego-ry, (2) integrating categories and their properties, (3) delineating the theory and (4) writingthe theory” (Glaser & Strauss, 1967, p. 105). The following are examples of the compara-tive strategies used in this study to develop and delineate categories and clusters:

• Text coded for a particular category was collated and compared to clarify the coremeaning of the category.

• Accounts of related categories were cross-compared to validate and confirm theirdistinguishing features.

• Accounts of related categories were cross-compared to justify their allocation to aparticular cluster.

F INDINGS: ENGINEERING EDUCATION AS A SOCIAL LEARNING S YSTEM

The purpose of this section is to generate an authentic understanding of the complexsocial learning system that was investigated through the lens of Accidental Competenceformation. This authentic view is based on the students’ shared lived experiences of be-coming professional engineers that emerged from the interpretation of their multiple per-spectives and is represented here through the following three types of results: (i) a contex-tual model of Accidental Competency formation, (ii) categories of competencies, and(iii) “thick descriptions” (Geertz, 1973) from the data that “trace narrative trajectoriesthrough the system” (Cilliers, 1998, p. 130). The following draws on Geertz’ (1974) notionof experience-distant constructs, experience-near constructs, and their illuminating con-nection to explore the role of each type of results in generating this authentic view and theirrespective limitations if viewed in isolation.

The two abstract types of findings, the contextual model, and the coding categories,represent experience-distant constructs and constitute theoretical knowledge claims.

1. The contextual model of competence formation presented in Figure 4 provides an

understanding of the nature of the processes that take place in the system. Morespecifically, it illustrates the complex, emergent nature of the socio-cognitiveprocesses that contribute to engineering students’ professional formation.

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2. The second type of abstract findings consists of general patterns across the range of outcomes from these complex learning processes. These patterns are captured in the

coding categories that describe the types of outcomes in the form of competenceclusters and categories. The codes presented here emerged from the multiple per-spectives of this particular group of participants and are thus not intended to be rep-resentative for a larger population.

While providing an understanding of the nature of the system in abstract terms, thesetwo types of findings have a number of limitations. As the process of abstraction inevitably entails reduction, these findings cannot convey the complexities and intricacies of theprocesses of students’ competence formation and the multiple perspectives from which thefindings were derived. This means that abstract terms cannot constitute a comprehensiverepresentation of the complex system, in other words, to comprehensively “describe a com-

plex system you have, in a sense, to repeat the system” (Cilliers, 1998, p. 10). This meansthat the results are not generalizable in the traditional sense but rather require the “tenta-tive application” (Lincoln & Guba, 1985), or transfer, to other contexts.

Addressing these issues indicates the crucial role of the experience-near constructs andtheir illuminating connection to the abstract findings. The experience-near constructs arerepresented in thick descriptions, that is, students’ concrete accounts of their learning expe-riences. On one level these accounts are intended to fill the abstract competence categoriesand learning mechanisms with the life of students’ shared lived experiences, thus providinga sense of the intricacies that cannot be represented in abstract terms. This “rich context”(Lincoln & Guba, 1985) also facilitates the context-sensitive, tentative transfer of the find-

ings to other settings. On another level, these accounts are organized in a way as to providefurther insight into the structure of the social learning system. More specifically, three nar-rative trajectories that each link a number of student accounts were chosen to represent thestructural features of (a) the importance of the interactions between various educational in-fluences, (b) the ambiguity of learning outcomes from a particular set of influences, and, (c)the context dependent nature of the learning processes.

Contextual Model of Competence Formation The concept of Accidental Competency formation described how various factors from

the wider educational context interact to influence students’ professional formation(Stevens, et al., 2008). Figure 4 presents an abstract, contextual model of the complexity of these processes of competence formation.

The outer circle of the model illustrates the range of educational influences collated inthe five clusters that were detailed in the overview of the coding categories presented earli-er. The range of these influences constituted the input of the social learning system. Theircomplex interplay led to the formation of engineering students’ overall competence. Theelements of intentional teaching were clustered as learning activities and one trajectory within the system resulted in the achievement of intentional learning outcomes , thus repre-senting the concept of targeted instruction (Walther & Radcliffe, 2006b). Some elementsthat are part of the process of targeted instruction, such as assessment practices, are not in-tended to directly contribute to student learning. However, in this study we found , for ex-ample, assessment to have a significant impact on learning (Hargreaves, 1997; Parsons,Caylor, & Simmons, 2005) and was together with similar influences collated in the clusterof learning environment (Karen, 2006). The remaining clusters described elements of what

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the student brings to the educational process (student disposition in Figure 4), extra-curricular activities and the group of so-calledmeta-influences.

In intricate patterns of complex interactions, elements from all of the clusters led to thedevelopment of intentional learning outcomes (Gagne, 1985) as well as Accidental Competen-cies and Accidental In-competencies . The latter are non-beneficial outcomes of the complex

interactions that Vanasura et al. (2009) describe as the “unintended consequences of thecurricular environments in which engineers are educated” (p. 68). The subsequent sectionfurther details this model by first giving an overview of the categories of Accidental Com-petencies, and second, by tracing the three aforementioned narrative trajectories throughthe complexity of students’ overall learning experience.

Categories of Competence The following gives a summarizing overview of the clusters and categories of Acciden-

tal Competencies and their thematic foci as they emerged from the iterative interpretationof the data collected in this study (for a more detailed overview with descriptions for each

category, see Appendix A). To further illustrate the results, the subsequent paragraphs provide abstract definitions of each cluster with examples of their subordinate competence cat-egories (cluster and category names are presented in italics).

F IGURE 4. Contextual Model of Competence Formation with clusters of educational in-fluences as inputs and Accidental Competencies, Accidental Incompetencies and inten-tional learning outcomes as outputs of the system.

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The interpretive competency codes were applied on the level of coherent incidents tosupply “generous context” (Richards, 2005, p. 97) to support the interpretation. Morespecifically, the interpretations emerged from the context of the entire incident descriptionand could not necessarily be associated with individual word groups. This coding on thelevel of coherent incidents allowed an accurate view of the relative importance of the com-petency concepts within the accounts of the studied sample.

A generalization beyond the sample under study, however, is not the intention of thispresentation. The particular set of competencies and their collation into the presented

clusters was found to be an appropriate way to coherently represent the learning experi-ences of the 67 participants of this study. Likewise, the relative importance of the cate-gories is reflective of the particular experiences of those students and also of the thematicfoci that developed within the individual group discussions. Another factor that influenced

T ABLE 1Overview of the Seven Competence Clusters with the Subordinate CompetenceCategories

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the number of reported incidents was the varying depth of reflection required for the dif-ferent categories.

It was, for example, relatively easy for the respondents to recognize that they carried out

their industry work under financial constraints. This meant that the students needed to develop economic awareness , an experience that their university project work did not necessari-ly provide. In contrast, the competence category of engineering indeterminism, required thestudents to acknowledge and confront their own anxiety when being faced with open-end-edness and true ambiguity. Recognizing this and establishing the link to learning activitiesthat had generated a deterministic view of engineering problems, required deep insight andthe need to explore the emotional facets of some aspects of their learning, which engineer-ing students are typically not very comfortable with (Jolly & Radcliffe, 2000).

The combination of the factors discussed thus precludes an absolute quantitative view of the competence categories and their relative importance. The references to relative im-

portance of certain competence categories in the following presentation are consequently not only based on a high incident count but also on the contextual significance the studentsattributed to those aspects of their learning.

The cluster of flexibility and creativity contained a number of competence categoriesthat contribute to flexible problem solving in practice that yields creative engineering solu-tions. The high number of incidents coded for this cluster indicates that this aspect was rel-atively significant in the participants’ experience of their transition from university intoprofessional practice. Particularly prominent within this cluster was the competence cate-gory of procedural agility . This described the ability to solve problems that are characterizedby a multitude of possible pathways to a solution. The category thus included the students’

awareness of the open-endedness of engineering tasks and strategies to devise a solution inabsence of a fixed problem solving schema. Interaction described a cluster of competencies that related to the students’ interac-

tions with others. This includes the personal, as well as the professional, dimension of communication in the workplace. One of the most significant experiences for the stu-dents who participated in this study was the need to select the appropriate means,form, and register of communication according to the needs of the counterpart. Thestrategies that the students developed in this context were summarized in the compe-tence category tailor communication.

A group of competencies relating to the planning of work or time were collated in the

cluster plan. The aspect plan task described the ability to define the task or the problem inits nature and its boundaries. In contrast, plan work referred to the capacity of planning thetime dimension of a complex project. This specifically included the challenge of planning alarge number of dependent and interconnected steps over a long time.

Subsumed under professional realities , were a number of competencies that enabled thestudents to cope with the realities of the professional engineering workplace. A major as-pect in the students’ transition from university into practice was the realization of personalresponsibility for the results or the consequences of engineering work in practice. This in-cluded responsibility in a local sense within the company, as well as in a broader, such aslegal and ethical, sense.

The students reported the development of a number of competencies that were direct-ed towards regulating or improving their own working performance. These aspects, that were gathered in the cluster self, were particularly prominent in the focus groups. This sug-gests that the development of these competencies constituted a significant learning experi-ence for the students. The cluster contained aspects such as know self, which described

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meta-cognitive activity that is directed toward improving personal work efficiency or quali-ty. More specifically, this included a conscious consideration of the extent, availability, andinterconnection of the students’ own performance capacities.

Summarized under social context were a range of competencies that relate to the socialsetting of professional engineering work. Somewhat similar to the cluster of interactionthat was discussed earlier, social context referred to relations in the workplace. The focus,however, was not on transmitting information but on the social foundations of interac-tions with others. A good illustration is the category bridging different perspectives thatdescribes abilities to work across different cultural, disciplinary or personal boundaries. This includes a skill and an attitude dimension such as the preparedness and the ability to work in a diverse team.

The cluster technical encompassed a range of competencies relating to technical aspectsof engineering work. The fact that this cluster was much less populated than the other

clusters that were described is not necessarily an indication of insignificance of these as-pects of engineering work. This phenomenon can be understood in light of the way in which the student accounts were elicited in the focus groups. The description of the focusgroup protocol above was based on the notion of a competence anomaly as a moment where the students experienced a discrepancy between the competence expectation in the workplace and their prior learning at university. Since a large portion of engineering pro-grams is devoted to the development of the students’ technical competence (Jonassen,et al., 2006; Radcliffe, 2005), it is not surprising that the students did not experience a largenumber of competence anomalies in this domain.

Thick Descriptions – Characteristics of Complexity The paragraphs below present thick descriptions from the data in the form of narrativetrajectories to provide contextual richness to the abstract representations of the partici-pants’ learning. The following three aspects were chosen to illustrate the complexity inher-ent in the learning processes: (a) the interaction of various educational influences, (b) theambiguity of learning outcomes from a particular set of influences, and (c) the context de-pendent nature of the learning processes. The examples and quotes presented here are a se-lection of participants’ contributions coded for the respective categories and do not consti-tute the entire evidence base that informed their development.

Complex interaction of learning influences. This first narrative trajectory illustratesthe complexity of the interactions between influences that lead to students’ compe-tence formation. This phenomenon is similarly described in (Vanasura et al., 2009) asthe “connections among many factors that simultaneously act on one another to influ-ence the learner’s development” (p. 68). For this purpose, the interplay of a range of educational factors with both positive and adverse impacts on the competence cate-gories systems view and plan work are examined.

Figure 5 illustrates the supporting (straight arrows), combining (overlapping ellipses),and in some cases conflicting (jagged arrows) influences on the development of these twoclosely linked competency categories.

The competency category of systems view was defined as the students’ ability to recog-nize and simultaneously consider various parts of a problem in a broader context and ap-preciate their interconnectedness especially across disciplinary boundaries. This ability of “looking at the big picture” (Haslam, fourth year mechanical engineering student) wasclosely linked in its development of the students’ ability to plan work. The competency

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category plan work described abilities that support the time planning of a large project witha number of dependent and connected aspects.

In recounting situations in practice, several students recalled feelings of consternationupon being confronted with the challenge to consider multiple disparate aspects of a prob-lem when devising a solution to it. Conrad, a fourth year mechanical engineering student,described this as:

So at work you’ve got one thing to do with a set number of tasks which are all

obviously related.

In their accounts, the students contrasted this to their learning at university, where thedegree structure (see “navigation” in Stevens, et al., 2008) fragmented their learning intodisciplinary silos (see Figure 5). Adam, a final year mechanical engineering student, elabo-rated this difference between university and practice as:

When you are at uni you just do thermodynamics or just dynamics. […] so you are just focusing purely on that subject […]. You don’t think about interrelating themat all. […] At work I had to plan a lot more […] the plan had to have a lot morethings in it. Like, these and these things need to be done and you always have to

consider everything at the same time.

Conrad, a fourth year mechanical engineering student, confirmed:

That is one thing I found is a massive learning curve, which I ran into doing thisproject. You need to do a lot of planning. At university, because you’ve got say,four individual courses and they all have their minor part of planning but nothingreally comes together.

These contributions specifically highlighted the connection of the systems view and theplanning aspect of the industry project. The students did not only recognize the need to

consider all aspects of the problem simultaneously but they also stressed the necessity tosynthesize these disparate parts into a coherent solution strategy.

The following incident account by Cassandra, a third year mechanical engineering stu-dent, illustrates the previously discussed aspects of systems view and plan work in context(project details altered for the purpose of de-identification).

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F IGURE 5. Influence model for the competency categories plan work and systems view.Supporting (straight arrows) and compounding factors (jagged arrow) illustrate the com-

plex interplay of work situations and educational influence on students’ learning in thosecategories.

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Well, we were building a deep sea probe exhibit for the children’s museum in[name of city]. I am a mechanical engineer so I was helping to build the actual vehicle itself that kids are going to drive around. And so we built the probe two

semesters ago and we decided last semester to rebuild it because it wasn’t very sturdy and so we rebuilt it. But then this semester we found out that it’s too heavy now for the battery power that we have and for the motors that we have. [...] That’s not something you learn in class, how to re-evaluate what you have to do when coming up with a plan. [...] So now it’s like we’re building this motor basedaround fifty different things. Whereas the first time we built it, we only thought we had a couple of specifications that we had to meet and then the more werealized; ‘Oh, it has to have certain weight - we need to worry about that too. Andit has to have a certain motor capacity we have to worry about that. We only haveso many amps. We only have so much power in the batteries.’ And this stuff we

weren’t thinking about the first time we built it. So it’s like we learn as we go,instead of it all being on paper and we have everything we need to know and wecan just solve the problem.

In the context of assessment (see Figure 5), however, the separation of courses and thelack of coordination of examinations did, in some cases, promote this very ability to considermultiple aspects of learning and, at the same time, prioritize tasks and establish a holistic view on the material. Adam, a fourth year mechanical engineering student, observed.

This [planning at work] is very similar to SWOT Vac [Study Without TeachingVacation], where you get yourself organized. Trying to plan what exams you have

in which order. [It] is very similar […], just because you are trying to have so many things together in your head at once. […] You’ve got all this stuff in a week ortwo’s time. So what am I attending to first?

The isolation of subjects that some students felt detrimental to their ability to develop asystems view on engineering problems was, in combination with particular assessmentpractices, a positive influence for other students (see Figure 5).

A second example of the interaction of two educational influences emerged from thedata in the context of students’ reflections on their own learning from courses that were in-tended to promote systems thinking and integrated approaches to planning engineering work. In this study, participants mostly related their learning from such courses to experi-ences of practice they had obtained during their internship programs (see Figure 5). Conrad,for example, described this connection as:

Even [the project management course] I was doing; I was thinking: ‘This isridiculous. I hate this course.’ But when I did my [industry project], the more I gotthrough, I kept thinking ‘Yes, this is like [name of project management course].’

A number of students similarly reported that they did not attribute significant learningoutcomes to their design or project management courses at the time. Only from the com-bination of the university course and the internship experience, did the learning content

become meaningful. Based on this perspective, the students were able to integrate boththeir university learning and their experience of practice into the development of theiroverall competence.

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F IGURE 6. Ambiguity of learning outcomes (the Accidental Incompetencies and the asso-ciated work situation are shaded in the figure).

These examples show that a diverse range of influences such as degree structure, partic-ular assessment practices, project-based learning activities, and internship experiences in-teracted in an intricate way to influence the students’ overall competence formation.

Ambiguity of learning outcomes. The second narrative trajectory illustrates how a par-ticular set of educational influences can produce positive and, at the same time, negativelearning outcomes (see also, for example, Vanasura et al., 2009).

Similarly to Bob Kahn’s account that was presented at the beginning of this paper, Tom, a graduate mechanical engineer, described a work situation relating to time con-straints and pressures of work load.

During exam times we had enormous workload peaks. But somehow you got by. Today [in my job] I find that I can handle tight deadlines and do a lot of work in ashort time.

Figure 6 illustrates the analysis of the associated critical incident account in the contextof the overall coding structure. In the educational context, assessment and a high workload created time pressures for the student. This prepared him for a work situation where he was required to manage his personal working time, prioritize tasks, and meet time criticaldeadlines (time management in Figure 6). This was coded as the ability to work under pres-sure, an Accidental Competency allocated to the professional realities cluster.

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However, in the context of this incident account, the student further elaborated on thefollowing learning effect:

... But somehow I initially had trouble structuring my workload. At university you

always just do the work you need to do in order to survive at that point in time.

Figure 6 illustrates that the same set of educational influences (assessment, workload ) alsocontributed to a second aspect of the student’s competence formation. The work situation was also concerned with planning, in particular with the aspect of overall project planning.In the educational context, assessment in combination with the high workload did not allow the student to independently plan his learning and he thus worked within the existing scaf-fold that was provided by assignments and exams. In the work context, this led to an Acci-dental Incompetency to plan work(see Figure 6).

Plan work referred to the planning of the time dimension of a complex project. The

time planning aspect pertains to the challenge described by several respondents to plana large number of dependent and connected steps over a long time. Conrad,a fourth year mechanical engineering student, described a similar experience inpractice as:

So, at work you’ve got one thing to do with a set number of tasks which are allobviously related.

Several students experienced this as a contrast to their learning at university. Steven, afourth year mechanical engineer, confirmed:

But, because you get this many layouts with your different subjects ... where a lotof stuff is due. You tend to... you are forced to work within those layouts.

These examples reveal the ambiguity of processes of Accidental Competency forma-tion. The combination of assessment and a high study load had, on the one hand, a posi-tive impact on the students’ ability to cope with work load peaks later in their careers. Onthe other hand, this same combination of influences hampered the development of theirability to independently plan their own work.

Context-dependent learning outcomes. The third narrative trajectory illustrates thecontext-dependent nature of the processes of competence formation. For this purpose, the

influence of the structuring function of assessment on the development of engineering stu-dents’ ability to plan is analyzed in the context of the differences between national educa-tion systems. In this context, one focus group with American and German students, whohad participated in an exchange with the respective other country, was particularly illumi-nating. The following quotes from this particular focus group are not sole evidence for thiscategory but were chosen here for the purpose of a coherent presentation.

Richard, an American fourth year mechanical engineering student, confirmed the scaf-folding function of the assessment pieces in the American system:

We have accountability [through homework] Monday, Wednesday, and Friday.[For the exam] we already know [the material] or you don’t pass the class because

you don’t get the 20 percent homework grade.

The students of the same group had a very different experience of the German system, where the assessment consisted of only a single exam at the end of the semester:

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Over there [in Germany], … we didn’t really have assignments daily. (Richard)

Referring to this account, Jacques (a fourth year mechanical engineering student fromthe United States) described, that for him, the lack of continuous assessment resulted in a

more self-structured way of learning:

It was incumbent on [me] to be caught up with class and caught up with lecturesand know everything.

Marcus (a fourth year mechanical engineering student from the US) confirms the ne-cessity for structuring his own learning:

[You] have to go the whole semester. So if you forget what you learn at thebeginning of the semester, you’re in trouble.

However, this less structured learning environment did not contribute to the abilitiesof all students to be able to independently plan their work or study load. Brett, for exam-ple describes:

[I didn’t] learn hard during the semester and then studied hard for one week before the exam.

In a related incident description from his time in industry, Brett also described fallinginto the same pattern of working to deadlines. Similar to Tom’s account that was analyzedearlier, Brett also experienced this ability to work under pressure as beneficial in the profes-sional context.

These accounts reveal that the impact of the learning structure on the students’ inde-pendent planning ability was context-dependent on two levels. First, the differences be-tween the national education settings obviously determined how much of a structuringfunction the assessment had for the students’ learning. Second, the learning processes alsodepended on the individual student’s personal context. Whereas for some students, thelack of a strong learning structure fostered their independent planning abilities, for otherstudents it did not have the same effect. In the context of an investigation of students’ self-directed work, Litzinger, Wise, and Lee (2005) similarly observe that “students who preferlow levels of structure in their learning benefit more from experiences that require self-directed learning than students who prefer high levels of structure” (p. 22).

DISCUSSION

The previous sections developed an authentic view of the complexity and patterns of engineering students’ overall learning experience through the combination of a contex-tual model of competence formation, categories of Accidental Competencies, and stu-dent accounts that form narrative trajectories through the complex social learning sys-tem (Davis & Sumara, 2006; Doll, et al., 2005; Vanasura, et al., 2009; Walther & Radcliffe, 2006a, 2007b).

The following examines these results in their implications for engineering education.

This refers to the dual meaning of the paper title and discusses (i) the process aspect ofeducation—the “how” of engineering competence and (ii) the content and goals of engi-neering programs—the “what” of engineering competence.

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The “How”—Implications for Curriculum Design and Teaching Practice The results of this study indicate that engineering students’ overall competence is

formed in a complex, socially situated learning environment through intricate learning

processes with a wide range of varied influences at play (see also: Davis & Sumara, 2006;Doll, et al., 2005; Walther & Radcliffe, 2006a, 2007b). The following explores what thiscomplexity means for engineering education. How, in the face of this inherent complexity,can engineering educators shape the process of students developing into professional engi-neers on the level of both curriculum design and teaching practice?

A balance between control and emergence in curriculum design. The introduction tothe context and background of this study discussed some of the difficulties of the adoptionof outcomes-based education in engineering. The results of this research suggest that thesedifficulties need to be further explored by examining the tensions between the complex so-cial processes of engineering competency formation uncovered in this study and the inher-

ently rigid structures underlying curriculum design. More specifically, this means that when the notion of educational outcomes was operationalized in engineering curricula it was not fully acknowledged that this concept carried with it a certain set of implicit as-sumptions (Besterfield-Sacre, et al., 2000; McGourty & Shuman, 1999). Already duringthe introduction of outcomes-based education in the engineering community, consider-able need for conceptual clarification of this concept was pointed out (Besterfield-Sacreet al., 2000; Splitt, 2003). One reason for the conceptual difficulties that still prevail today is the fact that “as educators we have initiated reform actions assuming the nature of theconstruct [of educational outcomes] without really exploring its underlying meaning”(Besterfield-Sacre et al., 2000, p. 100).

This underlying meaning is best analyzed in the context of the historical developmentof the theory of outcomes-based education. As pointed out by several authors (Heywood,1997; Miles, 2003), the philosophy of outcomes-based education goes back to the behav-ioral objectives movement promoted by Tyler (1949) and Bloom (1956) during the 1950s. This movement was firmly rooted in educational theory from the field of behavioral psy-chology and thus carries with it a set of specific assumptions about human learning. Ac-cordingly, defined outcomes or objectives are the basis of this instructional paradigm andare understood as “the description of a performance you want learners to be able to exhibit,before you consider them as competent” (Mager, 1962, p. 11). This means that a learningoutcome is related to a specific behavior or performance and overall competence results

from achieving all predefined learning outcomes. On this basis, the teaching process is seenas selecting and administering learning activities in order to achieve specific outcomes in atargeted way (Gagne, 1985). Walther and Radcliffe (2006b) termed this Targeted Instruc-tion. The advantage and charm of this concept lies in the fact that the clear definition of outcomes helps to focus the learning efforts of the student and lends precision to teaching.It is an appealing idea that the teacher as well as the students are aware of the goal and pur-pose of learning which ideally results in a transparent and deliberate educational process.

In spite of these advantages, the notion of outcomes-based education with its intellec-tual heritage in the field of behavioral psychology suggests a limited view of the complexprocesses of student learning discussed here. More specifically, this concept assumes a rela-tively simple mapping between explicit teaching activity and the achievement of learningoutcomes and thus implies a reductionist, and in some ways limited, perspective on studentlearning (Carr & Kemmis, 1986). The theory of outcomes-based education employsOckam’s Razor on two levels. First, it assumes that the overall competence of an individual

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is made up by adding up individual competencies or attributes. And second, it implies thatlearning can be segmented into individual activities which independently lead to theachievement of specific learning outcomes. Accordingly, in the application of this concept

to instructional design in engineering the outcomes are broken down into learning objec-tives (Felder & Brent, 2003; McGourty & Shuman, 1999) and subsequently learning ac-tivities are selected and delivered in order to achieve the learning outcomes.

While educational outcomes are useful for some aspects of the educational process, theresults of this study and particularly the holistic view of student learning developed here,suggest that this concept is limited in capturing all facets of the students’ educational expe-rience (Dall’alba & Sandberg, 1996; Davis & Sumara, 2006; Doll et al., 2005; Karen,2006; Mason, 2008; Scott & Yates, 2002; Stevens et al., 2008; Walther, et al., 2008; Walther & Radcliffe, 2006b). In the context of curriculum design, this indicates that notall aspects of this rich experience can be designed and controlled in a deterministic sense.

This allows an interesting comparison to the area of economics where the application of complex systems theory suggests that the complex system of a national economy cannot,for obvious reasons, be completely controlled through external measures. Arthur (1999)advocates that a complex system should be influenced “not by a heavy hand, not an invisi-ble hand, but a nudging hand.” He sees the role of external design in seeking “to push thesystem gently toward favored structures that can grow and emerge naturally” (p. 108). In atime when the field of engineering education thrives internationally for more structure andcontrol of student learning through the implementation of outcomes-based education, thisraises questions as to whether a comprehensive design or the “engineering” of students’competence through a rigid and deterministic system is possible in a narrow sense.

This also sheds light on the difficulties in comprehensively and accurately measuringlearning outcomes or the effects of teaching. With respect to quality control of educationalprograms, the results of this study suggest a careful combination of, first, checks that tangi-ble learning outcomes are achieved and, second, attention to the implicit learning processesthat shape some of the less tangible learning outcomes. A way to implementing such a bal-ance pedagogically are, for example, open learning opportunities such as problem-basedlearning (Ahern, 2010; Kolmos, 1996; Mills & Treagust, 2003; Smith, Sheppard, Johnson, & Johnson, 2005) or service learning approaches (Bauer, Barbara, Joan, Juan, & David, 2007; Coyle et al., 2005; Edward, Leah, & William, 2006). Such approaches thatVanasura and colleagues (2009) describe as “learning opportunities to broadly define any-

thing designed by the faculty to foster students’ development” (p. 68), might ultimately mean that engineering education needs to relinquish some of the comprehensive control of student learning (Doll, 1993) that is implied in the current application of educational out-comes. However, this would on the other hand enable engineering educators to supportthe students’ individual development in ways that best align with their personal interestsand dispositions. In consequence, this would support the development of a diverse cohortof graduate engineers (similarly advocated in: Atman et al., 2008; Stevens et al., 2008) whocan satisfy the varied needs of industry and, more importantly, who can responsibly assumetheir expanding role in current society.

Evidence-based frameworks for reflective teaching practice. The above paragraphs sug-gest that on the level of curriculum design a narrow view of outcomes-based education mightbe limited in taking into account the diverse facets of students’ learning experiences that wereuncovered in this study. Similarly, on the level of teaching delivery, the deterministic view

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implied in the notion of targeted instruction does not capture the full range of educationalstimuli that emerge from authentic teacher-student interaction (Chen,Lattuca, & Hamilton, 2008; Paretti, 2008; Vogt, 2008). The findings of this study provide

an evidence based framework to synthesize reflective teaching practice and the tacit knowl-edge of engineering educators to beneficially leverage the broader influence of the teacher.In a similar vein, Stevens et al. (2008) advocate an “informed tinkering culture ... some of [which] is based on research ... and some based on other varied inspirations that designersin all fields rely upon” (p. 366).

As an example from the results of this study, the following considers the role of the edu-cator in students’ appreciation of the ethical aspects of professional engineering work (Colby & Sullivan, 2008). Traditional teaching has been shown to be limited in achievingsuch learning outcomes that are situated on a deep attitudinal level (ASCE, 2004). It is, forexample, arguable whether a theoretical introductory course in ethics can develop “an un-

derstanding of professional and ethical responsibilities and a commitment to them” (EA,2005, p. 3). Students in the research focus groups, for example, reported that separateethics instruction, particularly in a different department conveyed the sense that this con-tent was not part of professional engineering, since it was isolated from the rest of the cur-riculum and not taught by engineering faculty (see also: Boyer Commission, 1998; Kellam,et al., 2006). Fromm (2003) confirms in a general sense that the “undergraduate engineer-ing educational experience [is] increasingly fragmented into what appears to the student asindependent parts” (p. 113). However, the broader influence of the educator (Chen, et al.,2008; Vogt, 2008) provides a chance to integrate intangible aspects, such as ethics, intotheir engineering teaching. An engineering instructor who conveys a commitment to ethi-

cal responsibilities in, for example, his way of talking about engineering or in selecting gen-der neutral (Cynthia, Susan, & Deborah, 2007; Du, 2006; Male, Bush, & Murray, 2009)and appropriate examples to demonstrate engineering content (Burack & Franks, 2004),can serve as a role model and help students to integrate such aspects in their own concep-tions of professional engineering.

Examples such as this demonstrate that, contrary to the assumption that learning re-sults only from explicit teaching activity, everything educators do has an influence on thedevelopment of engineering students. For teaching practice, this realization of the impactof broader influences on students’ competence development leads to the need to explicitly consider both the personal impact of the educator and the side effects of their instructional

strategies. This can take the form of reflective teaching practice where the instructors de-liberate their influence beyond the delivery of learning content. The examples from thedata and the framework of competence formation discussed here can provide a startingpoint for both reflection in action and reflection on action (Schoen, 1983).

In the same way, students can be made equal partners in this reflective process and develop an awareness of not only the goals of their learning, but also an appreciation of the in-terconnectedness of the educational influences and the impact of their entire university ex-perience. The focus group protocol presented here as a data gathering tool also provides ameans to introduce structured student reflection (Walther, et al., 2009; Walther &Radcliffe, 2007a). The participants in the focus groups conducted for this study reported

that the holistic reflection on their learning helped them to overcome some of the unavoid-able tensions (Walther & Radcliffe, 2007b) in their transition from university into indus-trial practice (see examples in the section on complex interaction of learning influences).Reflection of this type provides students with opportunities to synthesize their learning

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from both formal and open learning opportunities into a coherent professional identity (Walther et al., 2009).

Beyond these specific recommendations, the results of this study can serve as an

evidence-based foundation to support individual teaching innovations. It can be assumedthat experienced engineering educators have at their disposal a wealth of implicit knowl-edge of the subtleties of student learning. Thus, some of the examples and general mecha-nisms presented as results of this study would be familiar to many instructors. In this re-spect, the findings of this study, specifically the holistic view of student learning and thecategories of competence, can serve as an empirically grounded framework to integrate re-flection and experiences to give a voice to this wealth of implicit knowledge. In the aboveexample, the engineering educator who is considering the impact of the choice of engi-neering examples on students’ attitudinal development can draw on this evidence-basedframework of unintended learning effects to appreciate and argue for the significance of

such processes on a general level. In a more specific sense the model presented here alsoserves to integrate existing research into an overall understanding of student development,as discussed in the example of the influence of gendered learning content (Cynthia, et al.,2007; Du, 2006; Male, et al., 2009) .

In summary, the argument presented suggests that the instructional process is complexand affected by a wide range of influences, some of which are beyond the control of the ed-ucator. Some of these mechanisms impact non-cognitive aspects of students’ competenceon the behavioral and attitudinal level. Considering such influences in a reflective practiceof engineering teaching presents a significant challenge and an expanded responsibility forthe individual engineering educator. However, acknowledging these effects and providing

an evidence-based foundation for their investigation and integration into teaching practicealso creates an opportunity for engineering education to achieve some of the broader goalsdefined in the educational outcomes.

The “What”—Implications for Conceptions of Engineering CompetenceIn addition to the procedural view presented, the following examines the meaning of

the specific competency findings in light of the educational outcomes defined in the vari-ous national frameworks (ABET, 2004; EA, 2005; Engineering Council UK (ECUK),2004; The Institution of Professional Engineers New Zealand (IPENZ), 2005). A key premise of this exploration is that the competence categories that were derived in this re-

search are, in their nature, different from the conceptions of professional engineering com-petence presented in the definitions of educational outcomes. More specifically, the com-petence categories are ideographic findings that primarily describe the sample of engineering students that was under study. The categories represent those aspects of beingan engineer that the 67 students from a diverse range of programs experienced as impor-tant in their transition from university into different roles of engineering practice. The de-fined learning outcomes, in contrast, present broad aspirational goals that were intended totransform the direction and scope of engineering education by focusing on broader aspectsof professional competence (ASEE, 1994; EA, 1996) rather than defining engineeringcompetence in a detailed and comprehensive way. For this reason, the following para-

graphs are not intended to uncover congruence between the competence categories and thegraduate attributes, nor are they meant to reveal gaps in either framework, or even add new items to the existing lists of learning outcomes. However, a “tentative application”(Lincoln & Guba, 1985, p. 42) of the findings to the stated learning goals, reveals a num-ber of interesting perspectives that can perhaps foster a deeper understanding of the notion

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of professional engineering competence as it is conceptualized in the defined learningoutcomes:

• A number of the competency findings resonate with specific aspects of the defined

educational outcomes. The cluster of Technical Competence, for example, corre-sponds to the first of the Australian Graduate Attributes (Graduate Attribute iv in:EA, 2005, p. 3) that calls for the students’ “ability to apply knowledge of basic sci-ence and engineering fundamentals” (p. 3).

• Some of the findings can be seen to provide differentiated perspectives on the broadand general definitions of the graduate attributes. The second Australian graduateattribute, for example, specifies as one of the learning outcomes the “ability to com-municate effectively, not only with engineers but also with the community at large”(p. 3). Several competencies from the Interaction cluster can shed light on whichtypes of abilities support the acquisition of this educational goal. The competency

category of empathy, for example, describes a very specific aspect that the studentsexperienced as crucial in “communicating effectively” with others.

• Other aspects of the competency results add new perspectives to some of the statedlearning outcomes. One predominant pattern from the data was the students’ ap-preciation of the ability to work under pressure that they developed during their de-gree and later found useful in the professional context. This aspect of students’ com-petence development is one example that does not seem to be included in the broaddefinition of the learning goals. More specifically, the emergence of this ability fromassessment pressures is obviously not implemented in an intentional way in engi-neering programs.

• A range of these non-traditional competencies could also be seen to be included inthe very broad definitions of the learning outcomes. However, the ways in which stu-dents in this study developed some of these competencies, or in negative cases in-competencies, suggests tensions between the stated learning goals and the students’educational reality. For example, the competence category of engineering indetermin-ism, described the students’ awareness of the ambiguity, true uncertainty, and themultitude of possible outcomes inherent to engineering problems in practice. Thiscould be taken to be an element of the “ability to undertake problem identification,formulation, and solution” (EA, 2005). The findings of this study, however, call intoquestion as to whether the appreciation of ambiguity is what engineering students

overwhelmingly take away from their entire experience of attending university. While these relationships provide illuminating perspectives on various aspects of engi-neering competence, they do not constitute a comparative analysis which was not the pur-pose of this study. Rather, the main benefits of the findings lie in providing a different lensto better understand how the various types of learning outcomes relate to the students’ con-crete experiences of education and professional practice.

One possible opportunity for understanding lies in bringing alive the abstract concep-tions of learning goals with rich contextual examples from the students’ shared lived experi-ence of engineering education. The analysis of such accounts, some of which werepresented in the results section, can provide insights into what it actually means for stu-

dents to develop these competencies in the educational context and in which ways they be-come relevant in their experiences of engineering practice. This understanding can enableengineering educators to recognize when learning processes occur, both within and outsidethe scope of formal instruction.

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In summary, the competency categories presented provide interesting perspectives onsome of the educational goals that were defined for engineering programs. However, moresignificantly, the findings are hoped to be a first step in connecting the graduate attributes

with the students’ educational reality and, in turn, with the demands of professional prac-tice. This is hoped to help engineering educators overcome the competence dilemma be-tween university and practice (Walther & Radcliffe, 2007b) and, at the same time, preparestudents better to bridge some of the unavoidable gaps in their individual paths of becom-ing professional engineers.

CONCLUSION

This paper presented a holistic inquiry into engineering students’ professional forma-tion through the lens of Accidental Competency formation. This notion conceptualizes

student learning as the diverse, contextual outcomes of the complex interaction of teachingactivities and a range of other influences from the students’ educational and personalcontext.

This phenomenon was empirically investigated in critical incident focus groups with 67engineering students from a range of professional placement programs internationally. The student accounts of their learning experiences in the transition from university intoprofessional practice were analyzed qualitatively using the data analysis software NVivo7.

The iterative analysis based on a grounded theory approach yielded clusters and cate-gories to characterize patterns in the types of accidental learning outcomes, both positiveand negative, as well as in the educational influences and the corresponding situations in the

workplace. These results were presented in three ways to achieve an authentic representa-tion of the complex social learning system under study. First, a contextual model of compe-tence formation highlighted the complexity of the learning processes observed. Second, anoverview of the competence categories was presented to provide a sense of the range of out-comes that emerged from the shared experiences of the participants of this study. The third view of the results took the form of thick descriptions that traced patterns through the intri-cacies of student learning, drawing on the rich accounts of their experiences.

On the basis of this representation, the results were discussed in their implications forengineering education. The first part explored some of the tensions between this holistic view of student development and some of the linear, deterministic elements that are im-

plicit to the implementation of outcomes-based education in engineering. Based on thisdiscussion recommendations for curriculum design and teaching practice were derived.More specifically, the results suggest that engineering education strive for a balance be-tween control and emergence in designing curricula. For teaching practice the results pre-sent an evidence-based framework to support reflective teaching practice and to incorpo-rate the wealth of implicit knowledge held by engineering educators into teachinginnovation. The second part of the discussion analyzed relationships between the compe-tency categories presented here and the conceptions of engineering competence expressedin the definitions of learning outcomes. These two conceptions of engineering compe-tence, ideographic findings on the one hand and aspirational goals of a reform on the

other, are different in nature which precludes a comparative analysis. The findings can,however, provide an illuminating perspective on the learning outcomes in that they can fillthese abstract notions with the life of students’ experiences and illustrate how these becomerelevant when our students cross the gap between their educational experience and profes-sional engineering practice.

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A UTHORS

Joachim Walther is an assistant professor with the Faculty of Engineering, University of Georgia, Driftmier Engineering Center, Athens, Georgia, 30602; [email protected].

Nadia Kellam is an assistant professor with the Department of Biological and Agricul-tural Engineering, University of Georgia, Driftmier Engineering Center, Athens, Geor-

gia, 30602; [email protected].

Nicola Sochacka is an instructor with the Faculty of Engineering, University of Geor-gia, Driftmier Engineering Center, Athens, Georgia, 30602; [email protected].

David Radcliffe is Kamyar Haghighi Head of Engineering Education and Epistemol-ogy Professor of Engineering Education, School of Engineering Education, Purdue Uni- versity, Neil Armstrong Hall of Engineering, 701 W. Stadium Avenue, West Lafayette,IN 47907-2016; [email protected].

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