Beckman joyce

Reflections on Teaching Design Thinking to MBA Students1

Sara L. Beckman2 Senior Lecturer with Security of Employment Haas School of Business University of California, Berkeley beckman@haas.berkeley.edu

Caneel K. Joyce PhD. Candidate Haas School of Business University of California, Berkeley joyce@haas.berkeley.edu

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Prepared for the Business as an Agent of World Benefit Conference, June 2‐5, 2009 2 Corresponding Author

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Introduction

As we approach the summer of 2009, we face a number of highly complex, systemic

problems. The global economy is in shambles, precipitated by fundamental issues in the U.S. financial industry. Global warming and other environmental concerns are growing, which in concert with our current economic woes is causing us to question the role consumerism plays as a cornerstone of economic prosperity. And, four billion people in the world continue to subsist on less than $2 per day (Prahald 2006). These large‐scale problems are being addressed in a wide range of venues around the world from the World Economic Forum to individual classrooms in business, engineering and design schools where students struggle to grasp and then simplify the problems to make them tractable.

This paper describes a class conducted jointly between the University of California, Berkeley

and the California College of the Arts that has evolved over the years to include tools and techniques – in design thinking and cross‐disciplinary team management – that we believe allow students to take on increasingly complex problems and design potential solutions to them. Students engage in a semester‐long design process, taking an idea through to first pass prototype by exercising the four primary design thinking activities: observation, framing, imperatives and solutions. The class involves the local design community as coaches for the student teams throughout the semester, as judges for the final “tradeshow” event at which the students show their work, and as instructors in the classroom.

Over the years, the class has grown its focus on sustainability and social responsibility both in the content of the class itself as well as in the projects the students propose and choose to do (C. Cobb, et al. 2007). The way in which we teach the design thinking process and its associated tools and techniques has also evolved over the years as the involved faculty have learned more about each others’ disciplines and been able to more formally articulate what design thinking is.

The course faculty, along with a handful of Ph.D. students in both business and engineering, have studied the student teams in the class closely, collecting data on how they use the various tools and techniques presented in the class to do their projects, on how they work with one another in this highly cross‐disciplinary setting, and on how the teams perform in the eyes of the design 2

community that judges them. We have learned about the critical role of user needs understanding in helping teams negotiate consensus about solutions, about the need for teams to develop a shared frame of the problem they wish to solve, and about the challenges of managing ambiguity in the teams.

In this paper, we share the design thinking process we teach in the class along with a collection of the findings from the research we have conducted on the teams in the class. In doing so, we attempt to set forth an agenda for thinking about what needs to be taught to MBA students to have them be better prepared to engage in design thinking in their future professions.

1.0 Our Class and Design Thinking For over fourteen years, we have offered a course, “Managing the New Product Development Process: Design Theory and Methods,” that takes students through the front end of the New Product Development (NPD) process, from initial team formation and problem selection to presentation of a tested prototype to a panel of professional designers at the end of the semester. Teams begin with an initial project proposal based around a ‘bug’ or annoyance that students have observed, around a market that interests them or around a company‐proffered concern. Teams then perform “need‐ finding” through customer and market research that often results in significant changes to their initial direction. Finally, they engage in concept generation, concept selection, concept prototyping and testing, and financial analysis closely following the general process in their textbook, Product Design and Development (Ulrich and Eppinger 2008).

Teams are multidisciplinary, with representatives from UC Berkeley’s Haas School of Business, College of Engineering and School of Information Management, and from the California College of the Arts Industrial Design program. The team project composes the majority of the class activity and 60% of the students’ final grades. In‐class project work is supplemented by lectures, guest speakers and small assignments to gain familiarity with design tools. Past teams have developed a wide range of products and services including: devices to improve the mobility of the elderly, emergency response systems for dealing with disasters such as Hurricane Katrina, lunch food service for inner city youth, mechanisms to protect grape crops from frost, toys to entertain toddlers at the dinner table, and clothing to protect farm workers from pesticides.

1.1 Design Thinking Framework

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Although the class is titled “Managing the New Product Development Process,” the process taught can be more broadly termed an “innovation process.” We teach a process of observation, framing, generating imperatives, and identifying solutions as put forth in Owen (1993) (2001) and used in other project‐based design courses (Dym and Little 2003) (Dym, Agogino, et al. 2005). It is worth providing a brief historical backdrop on design processes and thinking, as it may help us think through in what ways our thinking must evolve to deal with today’s design challenges.

In the early to mid 1960s, the complexities of developing technologies that might transform human lives – such as the first operational nuclear power station and supersonic flight – caused academics and practitioners alike to seek some structure for the design process. Designers at that time realized that, compared to the scientists who were creating the new technologies, their processes for embedding those technologies in usable artifacts were less rigorous and explicit (Simon 1996). Further, as they were increasingly forced to work across disciplinary boundaries, they found a need to be more precise in describing their processes to the others with whom they worked.

Finally, designers determined that their trial and error methods of design, in which they identified flaws and fixed them in a process of successive approximation to a final solution, needed more predictive and evaluative methods for determining the suitability of a design (Jones 1966) (Alexander 1964).

The “first generation” (Rittel 1972) development of design theories and methods leveraged the fields of operations research for its optimization techniques, and cybernetics for its systems thinking approaches. These approaches led designers to think explicitly about how to decompose a complex problem into a set of smaller, well‐defined problems, and to seek experts in the sub‐disciplines to solve those problems. In a sense this led to a rather Tayloristic view of the design process, one of many small tasks that could be performed and optimized individually. Not surprisingly, this mechanization of the design process frustrated followers who were unable to reconcile the methods of the “first generation” with the complexities of real design problems, particularly once values of social equity and pluralism were considered.

Thus, the “second generation” of design theories and methods that focused on design as a social process (Bucciarelli 1972) was born. This social process accommodated a less top‐down view of the design process, and relied less on experts to provide the solutions, instead engaging a broader range of players. Design then shifted from a clear‐cut problem solving process to a problem‐formulating 4

process in which getting to a collectively acceptable starting point so that appropriate resources could be committed to solving the problem was the core of the effort.

Recent discourse attempts to provide an integrated view of design as a problem‐finding and solving process that involves players from multiple disciplines. Owen asserts that “Design is the creation process through which we employ tools and language to invent artifacts and institutions. As society has evolved, so has our ability to design.” (C. Owen 1993). He further describes the design process (Figure 1) as having “recognizable phases, and these, while not always in the same order, nearly always begin with analytic phases of search and understanding, and end with synthetic phases of experimentation and invention.”

Abstract

Analytic Realm of theory

n ildi bu

g

ge s led ure ow as Kn Me Inquiry paradigm

Proposal

Synthetic

Knowledge

g led ow Kn

Concrete

Realm of practice

bu ea il su res ding

Application paradigm P ri Kn ow ncipl led e ge s usi ng

s g iple nc e usin P ri

Discovery or Finding

Kn ow le M dge

Work

Invention or Making

Figure 1: Building and Using Knowledge (Adapted from (C. Owen 1993) (C. L. Owen 1997) From this discourse the “design thinking” model that is being taught in a number of courses as well as used in a number of design‐focused organizations has evolved. That process has both analytic and synthetic elements, and operates in both the real and abstract realms. In the analytic phases of design, one focuses on finding and discovery, while in the synthetic phases of design, one focuses on invention and making. Movement between the real and abstract realms happens as participants in the process draw insights from what they learned in the concrete or real world, convert them to abstract ideas which are translated once again to the real realm in the form of artifacts or institutions. Owen’s work further suggests that this is an innovation process that fits all

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fields, although the specific tools and techniques used in each may differ, as may the emphasis on real versus abstract or analysis versus synthesis.

We complement the more traditionally described linear NPD process in our textbook with the “design thinking” model (Figure 2), emphasizing to the students the need to engage in both concrete and abstract thinking and to engage in sufficient discover or finding (analysis work) before moving on to solutions. Although some number of our students works on relatively small and well‐defined physical products, we emphasize throughout the term that the process has wider applicability. In fact, at the end of the semester we have industry speakers describe the application of the process to services, processes and other activities outside the product sector (e.g., to redesigning the education system in Singapore). This allows the students to see the ability of design to make contributions at multiple levels (Figure 3). When focused on styling and on features and functions of a solution, design makes a contribution, but generally a relatively transient one. When focused on problem solving and on reframing problems altogether, it can make a much longer term, more sustainable contribution. Thus, we believe, the lessons learned by the students are more reflective of an innovation or design thinking process, not necessarily focused solely on new product development.

Abstract

Frameworks

Imperatives

(Insights)

(Ideas)

Analysis

Synthesis

Observations (Contexts)

Solutions Concrete

(Experiences)

Figure 2: The Design Thinking Model

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Design thinking at the “framing” level provides long‐term competitive advantage Design Thinking used for…

Example: Apple

Competitive Advantage

Framing

iTunes Ecosystem

Decades

Problem Solving

Digital Rights Mgmt.

Years

Features & Functions Styling

iPod, Wheel

Apple Product Identity

Quarters

Months

Figure 3: Levels of Contribution of Design (Adapted from Steve Sato’s work at Hewlett‐Packard) 2.0 Lessons Learned by Students and Alumni of the Class The class uses a project‐based learning pedagogy (Dym and Little 2003) (Dym, Agogino, et al.

2005) that builds on the Kolb model of experiential learning (Kolb 1984) (Figure 4). The Kolb model encourages integrated thinking by cycling the learning experience through concrete, hands‐on activities, and experimentation sequenced between activities of abstract conceptualization and self‐ reflection. The active/reflective integration in the Kolb model allows the student to develop the self‐ reflective capabilities proposed by Schön (1987). Thus, reflection and sharing are key pedagogical tools employed throughout the course to help students gain maximum benefit from their product development experience. We ask the students to reflect on their experiences after each assignment (e.g., customer interviews), and to share their team reflections at each of three presentations to their peers. By the time they get to the final class assignment, generating the lessons learned, they are well‐experienced in reflection (Hey, et al. 2007).

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Figure 4: Kolb Experiential Learning Model 2.1 Students Lessons Learned Each year, at the end of the semester, we ask students to generate a list of the 8‐10 key lessons they learned through the class experience. An analysis of the 2,348 lessons learned from all sections of the class taught from 2000 to 2005 (Hey, et al. 2007) yielded three broad clusters of lessons learned categories emerged: 

Team‐related lessons learned comprise 35% of the total and include lessons learned, in decreasing order by frequency of mention, about: Roles, Responsibilities and Skills, Team Diversity, Communication, General Team Learning, Team Building, Conflict Management and Leadership.

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Process‐related lessons learned comprise 42% of the total and, not surprisingly, parallel the NPD model the class follows, including: generating the goals and mission statement of the team, customer and user needs research and analysis, concept generation, concept selection, prototyping and testing, and financial analysis. The largest category of process‐related lessons relates to identifying and dealing with customer and user needs. The phases of concept generation, concept selection, prototyping and testing, and financial, economic and business were less significant to the students.

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Other lessons learned, including topics that were general and not specific to NPD, such as managing time and arranging meetings, comprise 23% of the lessons learned.

2.2 Alumni Lessons Learned To test whether or not these “lessons learned” were also valuable after graduation, we undertook a study of the alumni of our class (Cobb, Agogino and Beckman 2007). We asked both open‐ended questions about what our graduates had learned and how they were applying that learning in their jobs, as well as survey questions that would allow us to compare their thoughts to those of the students just completing the class. On average, alumni rated all of the topics covered in the class highly, but they rated working in multi‐disciplinary teams at the top. This corroborated answers to the open‐ended responses in which alumni said that the abilities to work on a diverse team and to generate multiple design concepts for a problem were crucial to their everyday work.

Many felt that the team situations they experienced in class prepared them for their current jobs.

Some alumni also said they took the class because it was a chance to work with students from other 8

disciplines. On the process side, the ability to conduct thorough ‘user needs’ analysis showed up in the top four most valuable lessons learned from the class, but unlike the semester‐end lessons learned study in which it was rated at the top, concept generation and concept prototyping/testing were also ranked with similar importance by the alumni.

3.0 Evolution of Team‐Based Learning Tools In our first few experiences with the lessons‐learned exercise, we were struck by the large quantity of lessons learned that surrounded the team (rather than the process). At the time, we dedicated only one class session, at team launch, to formal discussion of team dynamics. We now dedicate an additional class meeting to debrief feedback from a 360‐degree peer review exercise in which we engage the students partway through the semester and are attempting to formalize our teaching of the team dynamics portion of the course. We frame the elements of team performance with

a

model

developed

by

Ulrich

Nettesheim

of

Passages

Consulting

(http://www.passagesconsulting.com/, accessed May 2009) (Figure 5).

Team Performance Model Context Mandate

Processes Goals • Tangibles • Intangibles • Value-add of the team

Membership

Results

Roles • Leadership • Line Members • Staff Members • Advisors

Performance

Relationships • Quality of Dialogue

Team Effectiveness

• Conflict Management •Trust

Incentives

Resources

Procedures • Prioritization

Quality Assurance • Agenda and task

• Decision making • Delegation • Links to other groups • Communications

management • Measurement of results • Focus on Team Development

Individual satisfaction with membership

Feedback

1

Figure 5: Team Performance Model

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There are three primary points in the course at which we pay attention to team issues and learning: 

Team formation: The teams are formed around shared interests and the need to balance disciplinary representation on the teams. Students are asked to take a short version of the Myers Briggs test (www.humanmetrics.com, accessed May 2009) and to complete a questionnaire that elicits information about their goals for the team project, how they like to give and receive feedback, etc. See Goleman (2000) and Druskat and Wolff (March 2001) for other ways in which team launch might be structured. Students are led through an in class exercise in which they debrief their Myers Briggs results and their questionnaires



Team check‐in: About halfway through the semester we administer a survey that examines team effectiveness, individual satisfaction and peer perceptions of individuals on the team. A professional organizational development expert debriefs the survey results with the teams in class, and coaches teams in particular need of support.



End‐of‐the‐semester: We have the students list the lessons they have learned from the class on Post‐in notes (one lesson per Post‐it). We then divide the teams into a new set of groups that have at most one member of an NPD team to share what they have learned. They create an affinity diagram with their lessons, and then report out to the rest of the class about the general categories of learning. We also have students keep journals during the semester, and ask in particular that they use those journals to capture their reflections on the process in which they were engaged. See (Lau, Oehlberg and Agogino 2009 January 4‐7, Berkeley, CA) for one analysis of the contents of student journals. In addition to grades from the faculty teaching the course, we invite outside designers to evaluate the students’ work. They complete a short survey for each team that asks them to assess the various steps of the NPD process and how well that team has executed them. There are several evaluators for each project, so we can generate mean scores for each team The problems we grapple with in the world today require both a new way of defining and

thinking about them, which the “design thinking” models and approaches provide, but is also requires working across disciplinary and functional boundaries. By integrating team‐based learning with design thinking tools in project‐based settings, we attempt to provide students with experience 10

in both areas. The research we are doing on the student teams in our class also attempts to shed light on the intertwined dynamics of design thinking and teamwork and to uncover some of the fundamental principles that need to be taught to MBA students.

4.0 Research Summary 4.1 Learning Theory and Diversity on Teams As described in Beckman and Barry (2007), the “design thinking” cycle as set forth by Owen maps closely to Kolb’s experiential learning theory model. There is a long history of research on learning, and in particular on the role of experience in learning. Some argued that experience is all that is needed for learning to occur; others, such as Dewey (1938/1997), proposed that learning is an ongoing “reconstruction of experience” that reconciles new experiences with old ones in a continuous learning process. In 1984, Kolb pulled from these many theories of learning to build what he called “experiential learning theory” in which he defined learning as “the process whereby knowledge is created through the transformation of experience,” (Kolb 1984, 41) and defined the learning process as applying the four steps of experiencing, reflecting, thinking and acting in a highly iterative fashion.

The experiential learning theory model juxtaposes two approaches to grasping experience, concrete experience and abstract conceptualization, and two approaches to transforming experience, reflective observation and active experimentation. Placed on a two‐by‐two matrix (Figure 6), these dichotomies define four learning styles: diverging, assimilating, converging, and accommodating. Individuals with a preference for a diverging style are good in idea generation activities, while individuals with a preference for a converging style prefer technical tasks over tasks dealing with social or interpersonal issues. Individuals with the assimilating style are good at taking in a lot of information and logically ordering it, while individuals with the accommodating style prefer hands‐on experience and action oriented learning.

Individual preferences for learning styles are thought to be derived from their personality type, educational specialization, professional career, current jobs and the specific task or problem the person is working on at present. Importantly, learning style is not a fixed trait in an individual, but “arises from consistent patterns of transaction between the individual and his or her environment….people create themselves through the choice of actual occasions they live through.”

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(Kolb 1984, 63‐64). This adaptability is what makes this model interesting with respect to our ability to engage students in learning design thinking. If we can modify the “actual occasions” our students “live through”, perhaps we can change the way they thing about formulating and solving problems.

Abstract Assimilating

Converging

good at understanding a wide range of Information and putting it in concise, logical form

good at finding practical uses for ideas and theories; solving problems

Analysis

Synthesis Diverging

Accommodating

good at seeing concrete situations from multiple viewpoints

good a learning from hands-on experience

Concrete

Figure 6: Kolb Experiential Theory Learning Styles

There are two fundamental hypotheses that we have yet to test in our class, but would like to

in the future. First, we’d like to know whether teams that have greater diversity in Kolb learning style outperform those that have smaller representation from the four quadrants. There is evidence that teams composed of a diverse set of learning styles outperform those with homogeneous makeup (Halstead and Martin 2002) (Kayes 2001) (Wolfe 1977), but no research to date that has been done on new product development or design teams of the sort we employ in this class.

Second, we’d like to understand how the leadership of such teams should work, and whether or not rotating leadership as the team progresses through the four quadrants of the design thinking process to employ the person at each stage who is best suited to that stage is valuable. We are in the process of designing a study that will allow us to get at the first of these questions.

We have, however, looked at some of the other characteristics that underlie the

performance of teams that are applying the design thinking process.

4.2 Learning about Behavior of Teams Much of the empirical research on teams happens either in an experimental laboratory or in the field each of which has advantages and disadvantages. Lab research offers control over confounds such as group size and team composition (e.g., functional diversity), but lacks the richness 12

of intense group dynamics over time – including the evolution of status hierarchies, group norms, emotional conflicts and resolution of competing goals all under tight time constraints – that characterize teamwork in the real world. In the field however, teams have wildly different histories, pressures, incentives, and resources making it difficult to effectively compare their performance and draw conclusions about a wide variety of team design parameters.

The NPD teams in our class are all similar in their composition (functionally diverse), size, tenure (all members start at the same time), resource constraints, and timelines. Having a syllabus built around a systematic staged NPD process even minimizes differences in the timing of different activities and milestones. Thus, the class provides a unique opportunity to study a large number of reasonably homogeneous teams working on similar problems with great depth over time, without as many confounds as found in the real world or the artificiality of the lab.

4.2.1 Framing and Reframing A qualitative study of just 22 of the NPD teams we’ve examined over the years, for example, allowed us to develop a model of how design teams negotiate shared “frames” early in their project lifecycles (Hey, Joyce and Beckman 2007). Frames are implicit collections of goals, assumptions, and values that shape how individuals and teams perceive and prioritize problems. Frames have an important effect on how solutions are found and developed. This research showed that upon team formation, individuals held their own frames, and that over time teams either succeeded in developing a shared team frame, or they did not.

Teams that succeeded in building a shared frame were those that made heavy use of user research. Research about user needs was collected by all team members, but each team used it differently. Members of teams that tended to share raw data (direct quotes, field notes, photographs) often discovered that the way individuals on the team had been thinking about the problem was different than the way their teammates were thinking about it. By coming together to discuss the meaning of a piece of raw data, different interpretations of the same information revealed latent conflicts in members’ individual frames, and resolving those conflicts was key to the negotiation of a shared frame. In contrast, members of teams that shared processed data (summaries, generalizations, interpretations) were much slower to identify incongruencies among their individual frames. This study was completed before teams finished their projects, so future research needs to examine the effects of framing activities on project outcomes.

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Another stream of research also investigated framing and reframing work by examining the use of language on the part of the NPD teams in their written communications during the semester.

(Hill, et al. 2001) used latent semantic analysis techniques to analyze NPD team email archives as well as other team‐produced communications to determine the level of semantic coherence among team members as a proxy for level of shared understanding in the team. High performing teams cycled between periods of high and low semantic coherence, but always “got on the same page” to achieve deliverables. Low performing teams, on the other hand, ignored or did not act on feedback as effectively.

4.2.2 Creativity, Conflict and Constraints A quantitative study of 38 NPD teams also demonstrated the path‐dependent effects of early stage team activities. In her dissertation, Joyce (2009) found that teams whose mission statements constrained their problem up front developed more creative final products two months later, as rated by a panel of product design professionals. Interestingly, constraint was not beneficial if it focused on constraining the solution that would be created. Problem constraint (but not solution constraint) also predicted members’ own satisfaction with their final products.

We also looked at the role of team conflict on the NPD teams (Joyce 2009). As has been found in prior work (De Dreu 2006), task conflict at mid‐semester had a curvilinear effect on creativity; it had a positive impact on the creativity of the teams’ final products, but the returns diminished as the level of task conflict increased. Conflict also moderated the effects of constraint on creativity. Task conflict at mid‐semester was more beneficial the more the team had constrained the problem (but not the solution) they were working on up front ; perhaps an agreed‐upon focus on a problem helped to structure conflict about the task in such a way that it was more productive.

Solution constraint did predict one thing however – a lack of task conflict. Perhaps agreeing to a solution up front created an artificial sense of agreement between team members, thus stifling the task conflict necessary to reach a creative outcome. These findings suggest that constraint plays an important role in the creative process – one that is not always negative as prior work (e.g., (Amabile 1996)) has suggested. This effect has recently been demonstrated at the individual level in the laboratory as well (Joyce 2008).

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Finally, recent work (Roschuni, et al. August 30 – September 2, 2009) examines the use of “feeling language” or “feeling communications” by the NPD teams and how the use of such language interact with the levels of conflict on the teams and ultimately with team performance. High‐ performing design teams appear to consistently use high levels of feeling language, unless there is high conflict, in which case they suppress its use. Medium and low‐performing teams’ use of feeling language is much more erratic across the phases of the project. There is some evidence that some, but not all, teams with high conflict levels suppress their use of feeling language when given feedback on their process. Further exploration is needed into how coaching and instructor intervention affects the use of feeling language, and negative feeling language in particular. Low‐ conflict teams use high levels of feeling language over the course of the project.

Other work is ongoing to look at the role of tolerance for ambiguity, and in particular, the

variability in tolerance of ambiguity among team members in design team performance.

5.0 Some Preliminary Conclusions

We still have much, much more to learn about the dynamics of design thinking by multi‐

disciplinary teams. But, some of our early work suggests opportunities to think about as we continue to integrate more design thinking into business school curricula. 

Shared interpretation and understanding of user needs is critical to developing shared frames in NPD projects. This suggests that we need to ensure that students working in team settings appropriately balance the extent to which they summarize and present information to their teammates versus spending time jointly interpreting that information and mining it for interesting insights and meaning. Our experience with MBA programs is that there are many “group” (rather than “team”) projects in which students divide the work among themselves, and spend little time together as a team in interpretation and decision‐making. Learning the skill of true joint work, in this case around interpreting data about the context for which a solution is being developed, may well be important to future design‐focused MBAs.

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Development of shared frames around the problem to be solved rather than around the solution itself is critical to being generative and creative on NPD teams. This suggests that students need to learn as much about problem finding and framing as they do about problem solving. There is arguably a tradition in many disciplines (e.g., engineering) of having students practice solving problems they did not find or create. To the extent that those 15

disciplines are feeding the MBA programs, there is not only a need to teach problem finding skills, but perhaps a need to have students unlearn some of the ways of thinking they bring to the program. 

Relationship and task conflict are inherent parts of successful design efforts, but must be productively used and managed. Students must learn to be comfortable with both divergence and convergence phases of a design process, which implies being comfortable with adjusting to different levels of conflict on the team. Implied in the management of conflict is a role for a team leader, a role that may need to shift among team members depending upon the task to be performed at the time. We have observed, but not researched, rotating leadership on a number of the NPD teams. Better understanding team leadership approaches in design projects would help us better prepare students to undertake them.

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Although we are starting research on the roles that design journals play for NPD teams and their members, we need deeper understanding of the role that prototyping and, more broadly, visualization play on design teams. Our CCA students are recognized by MBA and Engineering students alike (in their lessons learned and elsewhere) for their superior visualization skills. While unreasonable to think that we would teach MBA students much of what an industrial designer learns about visualization, some number of skills in this arena and an appreciation for the value of visualization may well be valuable.

We hope that some of the research we’ve been doing on a design‐focused multi‐disciplinary class that includes MBA students contributes to the growing number of discussions around the integration of design and business curricula. There is still considerable work to be done, and many other paths to explore in thinking about how MBA students might benefit from learning about the field of design.

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