Members appeared to be working individualistically in these times of tension. These standards included: using problem-based learning and other appropriate pedagogies, making the investment in instructors and group coaches, considering it a crucial investment to educate diverse students with and into design thinking. Attention was paid to developing projects, in class activities, reflective practices Adams et al.
Teams had instructors and groups have coaches. While students were in class, they were guided to engage the design process and mindsets. They were also guided to engage in idealized ways, receiving instruction and practice on different aspects of the design process.
For example, when they covered how to write a Point of View statement in class, the very task Team One had trouble with, they received instruction and completed activities that included: what the POV is, why it is important, what qualities and standards a POV should meet, and ways to check their POV in order to ensure it is an adequate one.
Design Thinking Research: Building Innovation Eco-Systems (Understanding Innovation)
They learned about, and practiced writing POVs in class. Still, our video revealed Team One floundering as it worked independently to develop a POV statement for its user. Even when instructors work extremely hard on course design and cover so many bases, the teaching and learning of design thinking has its wicked aspects, such as the team collaboration. The results raise questions and suggest implications for teaching design thinking and the need to better support independent teamwork.
First, is important to determine how to best help teams manage the design thinking process as they move through the different stages of a project. Finding ways to attend to team interactions in the design thinking process may pay off in terms of groups overall experiences and success in generalizing solutions. It is important to pay attention to teams abilities to recognize ambiguity in the design process. In a prior studies to this one, our research group found that students did not become design thinkers in a developmental sequence.
Instead, there were moments of significant insight that shifted ones understanding of the mindsets and processes that underlie design thinking Goldman et al. The development and handling of teamness is significant and worthy of extra attention. Teams are comprised of students with different backgrounds, disciplines, and prior design and team experiences. These differences bring both advantages and possibilities for radical collaborations Booker et al.
Students may benefit from the introduction of varied kinds of analytics in the design thinking process such as the creation of team rubrics and specific reflections on team process. Focusing on how teams collaborate in design thinking might benefit from a greater emphasis on evaluating the team process rather than just the end of course design solutions. John Dewey wrote, Conflict is the gadfly of thought. It stirs us to observation and memory.
It instigates to invention.
It shocks us out of sheeplike passivity, and sets us at noting and contriving. Design thinking relies on the resolution of conflict between a sticky problem and an elegant solution, what is known and unknown, what end-users say and what they really mean, and what does and doesnt work for users.
Building Innovation Eco-Systems
There are times where novice design thinkers are asked to make inferences about people, their needs, the possibility for solutions that will work, and what will pass muster in terms of grading of their work. There is no wonder why student teams seem unanchored in the design thinking process when they work independent of their instructors. The teams we studied found their way, more or less, and presented solutions that met course criteria.
Internally, their team processes were not elegant, and they stumbled through and around the design process. Some of what they were being taught proved useful in helping them become more attuned and responsive to each other as team members. The process was not conflict free when they worked outside of class, and both groups struggled to achieve a level of teamness that enabled them to accomplish their course and project goals. Acknowledgments We would like to thank the students who participated in our research.
Design thinking research building innovation eco systems (understand…
They have contributed to our understanding of how instruction might better impact their learning. Findings and opinions presented are those of the authors and do not represent the program. In: di Giano C, Goldman S, Chorost M eds Educating learning technology designers: guiding and inspiring creators of innovative educational tools. Wiley, Chichester, pp Cross N Designerly ways of knowing. Springer, London Dewey J Democracy and education: an introduction to the philosophy of education.
Springer, London, pp Katu Lets spend our lives together. Springer, London, pp Mercier E, Goldman S, Booker A Focusing on process: evidence and ideas to promote learning though the collaborative design process. Policy Sci [Reprinted in Cross ed Developments in design methodology. Wiley, pp ] Rowe PG Design thinking.
Design Thinking Research
Discourse Soc Schegloff EA Sequence organization in interaction: a primer in conversational analysis, vol 1. Abstract As designers collect information about a problem, they form a mental frame of the problem space that is the scaffolding around which to build a solution. When presented with new information, successful designers can reframe the problem and the solution as part of a successful iterative cycle. These iterative cycles are central to the Stanford Design Thinking process.
However, individuals and teams reframe to differing extents; is this variation rooted in intrinsic differences in cognitive style, and can it be associated with long-term innovative performance? The results shed light on the particularly strong need for improved team dynamics measurements and the challenges of transcending context-specificity.
Pathways for enhanced team dynamics measurements are explored.
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Prior research has shown that certain team dynamics indicators are strong correlates with long-term innovative performance Jung et al. There is also evidence that these dynamics may be the result of underlying intrinsic compositional characteristics Wilde However, current team observation techniques are generally time-intensive e. Another major challenge is that the definition of team. In particular we are concerned with understanding those factors which are reliably predictive of team performance. We have identified and explored three lines of inquiry toward developing a solution to this problem: 1.
Can long-term team performance be predicted on the basis of compositional characteristics? Can long-term team performance be predicted on the basis of a short-format laboratory exercise? What dynamic measurements are most effective at predicting long-term team performance? Results are presented from a longitudinal study of graduate student design engineering teams working on innovative product development projects. Significant challenges were encountered in creating a compositional model for long-term innovative performance that appeared valid across the multiple years of analysis.
Similarly, the short-format laboratory exercise proved to be somewhat unreliable in that it was not associated with any observed individual or group-level traits. However, significant opportunities were uncovered for developing a team dynamics-based model that moderates the relationship between team composition and long-term performance.
An explanation of these challenges, as well as various opportunities for direct measurement of team dynamics are explored in context. Due to the exploratory nature of the study, a broad data set was collected including standard ethnographic data and responses to four separate questionnaire-based intrinsic measures. Several promising correlations emerged at all three levels of analysis composition, dynamics and performance ; an expanded study was designed to confirm the validity of the result across contexts by incorporating measurements from the 2 years immediately preceding and following the year of the pilot study, or a total of students and 53 teams.
Due to limitations on availability of archival data, the expanded data set is analyzed at the local-only level collaboration with global partner teams is not taken into account.
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On the basis of the preliminary analysis, the Wilde Type indicator emerged as the strongest of the four measures collected in terms of apparent statistical reliability and correlations with team-level observations Kress et al. Archival Wilde. The Wilde Type data are comprised of individual responses to a item online questionnaire that are combined to yield four independent cognitive mode preference scores Wilde The four modes are generally distinguished by the dominant functions Thinking, Feeling, Sensing and Intuition, as initially described by Carl Jung Jung and Hull A summary of student and team involvement in the study by year is provided in Table 1.
Each team receives a unique project prompt from an external client; therefore, projects range in content and difficulty. Prompts were collected and scored on a four-factor scale to establish initial difficulty as a basis for comparison. Scoring was conducted by crowdsourcing consensus values for each project along each of the four factors ambiguity, breadth of scope, technical complexity, and overall difficulty using five-point Likert-type scales in an online survey. Each prompt was scored a minimum of 25 times by independent respondents and the group mean is taken as the consensus score.