“Does that mean it’s a success?”: Beginning Designers’ Forethought and Self-Reflection in Engineering Design Thinking

Expertise in Design Thinking Entails Development of Self-Regulation

Self-regulation includes the “thoughts, feelings, and actions that are planned and cyclically adapted” when working to attain a goal (Zimmerman, 2000, p. 14). The process of self-regulation is generally divided into three or four phases, beginning with some trajectory setting activity, engaging with the task, and adapting the approach (Panadero, 2017; Winne & Perry, 2000). Zimmerman (2000) articulated a three-phrase process that is widely used, including forethought, performance control, and self-reflection. The forethought phase of self-regulation includes activities in preparation for a task, including analyzing the task, planning and goal setting, and ingrained motivational beliefs that might influence performance. Concepts from a number of motivational and interest theories, such as appraisal of one’s confidence, the expected value or costs of the task, and motivation may be used to inform orientation towards the task. During the performance phase of self-regulation, strategies are applied to control and monitor execution of the task. The final phase, self-reflection, involves judgment and reaction; assessing and explaining performance outcomes, respectively. This process involves comparison of performance to the desired goals and interpretation of the causes of success or failure. This pattern continues to operate in cycles, with information from earlier phases shaping later attempts of learning and performance.

Notably, compared to other types of problems, design problems “require greater commitment and self-regulation by the problem-solver” (Jonassen, 2000, p. 80), and Ates Akdeniz (2023) described self-regulation as one of the most important tools in the design process. Misunderstanding the design process may lead beginning designers to move linearly, rather than cyclically. Then, to paraphrase Crismond and Adams (2012, p. 772), these beginning designers show no awareness of what they are doing, what they need to know, or what matters in their design (Mentzer et al., 2023). For example, up to the undergraduate level there is evidence that beginning designers tend not to iterate as much as more experienced designers (Adams & Fralick, 2010).

Prior research on engineering design thinking has described a number of additional indicators of experts’ abilities to regulate the process. Experienced design thinking incorporates “opportunities to inspect and introspect about behavior, perspectives, worldviews” (McDonnell, 2015, p. 117). Furthermore, experienced designers studied by Dorst and Cross (2001) demonstrated an ongoing dialog between the problem- and solution-space in their work, which enabled greater understanding of the design process. Their self-regulatory practices also shift during the design process and include greater monitoring and evaluation than beginning designers (Dixon & Johnson, 2012). Such expertise with the design process develops into greater freedom (Daly, Adams, & Bodner, 2012) and personalization (Cross & Clayburn Cross, 1998) within design work. In other words, a designers’ capacity for self-regulation—that is their ability to plan, reflect, and think strategically—is a crucial element in fostering their ability to learn while designing and continually develop design expertise (Barak, 2010).

Design Thinking is Replete with Opportunities for Learning

Because design involves ill-structured problems, with more than one right answer, there are many opportunities for iteration, failed attempts, and therefore learning (Goel & Pirolli, 1992; Wynn & Eckert, 2016). A wide number of visual depictions of the design process exist (Dubberly, 2004), but most commonly, models for the design process include a type of feedback loop indicating a return to earlier steps or reworking information before proceeding. Each subsequent phase of the design process is then informed by new knowledge created during the work. Indeed, reinforcing the need to take these opportunities, Crismond and Adams (2012) indicated that “learning while designing” and a “managed and iterative design” were important milestones attained in the maturation of beginning designers. Several studies have also noted the necessity of iteration for beginning designers to arrive at effective solutions, since the quality of their solutions was related to breadth of ideas considered (Atman et al., 2005; Atman et al., 1999).

Wynn and Eckert (2016) examined discussion around iteration in design studies and noted a prevailing theme of “progression” for how it is experienced. This progress might be in the form of reduced ambiguity, optimization, or incrementally completing design goals. Yet, whether designers realize the benefits of their attempts depends on the task, their experience, and whether other intervention occurs to stimulate reflection (Jackson, Godwin, et al., 2021; Trenshaw et al., 2014; Wynn & Eckert, 2016).

In particular, previous research has also summarized key aspects related to whether experiences of failure while designing were taken as a learning opportunity (Jackson, Godwin, et al., 2021). “Failure, by itself, is not believed to be the mechanism that promotes learning. Rather, positive student reactions to failure are linked with later success” (Jackson, Godwin, et al., 2021, p. 1861). Different conceptualizations of failure in design thinking mean that, in some cases, failure is seen as part of the way forward (Lottero-Perdue & Parry, 2017; Sawyer, 2019). Teacher and context-based messaging influence whether students adopt such a perspective (Trueman, 2014; Upadyaya et al., 2012). Then, when handled positively, experiences of failure can cause students to slow down, while highlighting key concepts and different solutions (Kapur, 2014; Pathak et al., 2011).

Self-Regulated Learning Contextualized as Learning While Designing

Even with the general applicability of self-regulation, as previously described, the interconnected nature of self-regulation and domain knowledge makes it difficult to distinguish learner’s abilities (Winne & Perry, 2000). On the one hand, with additional familiarity of the content in question, “expertise sidesteps some elements of self-regulation” (Winne & Perry, 2000, p. 559). Domain experience can influence how a task is approached, making up for some inabilities to manage the process, and masking the learner’s true self-regulatory capabilities. On the other hand, self-regulation is always implicitly influenced by context (Alexander et al., 2011) and differences in domain beliefs that may be held.

Rather than trying to disentangle self-regulation from domain knowledge then, a different approach to investigating self-regulation is to contextualize it in a domain-specific manner. This approach necessitates intentionality over convenience when selecting the domain under study and enables a more focused view of the regulation strategies exhibited by learners (Alexander et al., 2011; Peters-Burton, 2018). Several other studies have also drawn on self-regulation to explore the behavior of design students, and offer insight into students’ approaches to design problem solving (Li et al., 2020; Zheng et al., 2020). In the model of self-regulation for engineering design proposed in those studies, the general phases of self-regulation were coupled with design process steps, and used trace data (Winne & Perry, 2000) to characterize students’ behavior in completing the task. These studies contribute to the identification of specific behaviors showing self-regulation in engineering design, and describe potential learner profiles and trajectories during the design process. Ates Akdeniz (2023) also provided instruction to support self-regulated learning behaviors in a design studio setting, and demonstrated a positive impact in terms of both student perceptions and their design performance.

The nature of design tasks, with inherent iteration, failure, and embedded learning, warrants a reconceptualization of self-regulation that is adapted to the context. Nevertheless, the similarities between self-regulation and design thinking processes ease their combination. Both design iteration and models for self-regulated learning are cyclical, where information and thoughts shape behavior proactively (such as when planning or goal setting) and reactively (when reflecting on experiences; Zimmerman & Cleary, 2006). These cyclical patterns are why Kapur and Bielaczyc (2012) noted self-regulatory functioning as a useful framework to study learning from failure. Reflection, a vital phase in self-regulation, is also a hallmark of good design (Crismond & Adams, 2012) and may even be requisite for learning while designing, according to Purzer et al. (2015).

Therefore, I offer a framework combining self-regulation and design thinking phases, which I applied in this research (see Figure 1). Self-regulation phases and subcomponents identified in the framework are those proposed by Zimmerman (2000), and have been extensively supported in self-regulation literature. The general self-regulatory phases are matched to distilled phases of the design process: analysis, synthesis, and evaluation (Dorst & Cross, 2001; Dubberly, 2004; Jones, 1963). Analysis includes early stages of design thinking such as establishing the problem, criteria, and gathering information. It is divergent, expanding possibilities in design, and setting an initial trajectory. Synthesis is a convergent phase involved in processing information toward attaining a solution. Evaluation includes judgments of the design progress and generates energy for successive iterations of design.

Figure 1

Importantly, the framework emphasizes that self-judgment elements of self-regulation (self-evaluation and causal attribution) lead to a determination of whether the idea was a failure or success, catalyzing self-reactions (satisfaction or adaptation). It is not guaranteed that design students will notice or understand failures (Matlen & Klahr, 2013), so the interpretation of failure or success here is their own. Then, from this self-reflection phase, information from each attempt can be used to shape later attempts. Reactions to previous attempts are assimilated (forethought) to shape the approach (performance) and then results are measured against the criteria to determine whether further iterations are needed (self-reflection). Ideally, both self-regulation and designed solutions become more sophisticated in each round.

This prior research summarizes the interplay between design and self-regulation. Experts in design show proficiency when managing the process, despite its open-ended nature. When applied while designing, self-regulation facilitates learning throughout the design process—capitalizing on the abundant opportunities provided in an iterative process—and greater intention when responding to design successes and failures.

Setting and Participants

Two high-school classes delivering an introductory level engineering design course were selected for observation from a district in Maryland, USA. The rural district is predominantly white, with minority enrollment about 20%, and about as many students with low socioeconomic status. Both classes were participating in a larger study to support diversity in engineering, focused on student attitudes and the efficacy of the instructional materials. The teachers were purposefully chosen based on familiarity with their teaching and assurance that their classes would provide relevant, detailed data.

The course, Foundations of Technology, helps students understand and apply technological concepts in design-based activities. Instruction in Foundations of Technology uses a mixture of group and individual activities in design-based instruction, giving students natural chances to explain their thinking and make plans together (International Technology & Engineering Educators Association, 2017). It is the first course in the engineering pathway, yet it was required for all students in the district, whether or not they continued in the pathway. Subsequently, most students were in ninth or 10^th grade. For the unit observed, students completed the design project in pairs. Therefore, the course setting represents a strong opportunity to examine self-regulated design thinking as it occurs.

Teachers in both classes were experienced, each with more than 10 years teaching and familiarity with the specific content of instruction. Several criteria influenced the selection of four pairs of students for observations. A university Institutional Review Board and the district’s Research Review Board conducted a review of the study and approved the method. Then, eligibility for students was determined by having had parent permission and individual assent to be audio recorded as part of the larger study being conducted. Next, teams were recommended by teachers as being cooperative and able to verbalize their thinking (van Someren et al., 1994), to “maximize what we can learn” (Stake, 1995, p. 4), even though they had not worked together previously. The selected teams comprised two pairs of female students and two pairs of a female and male student. Each teacher and student participant included in the study was represented in the results by a pseudonym chosen by the researcher.

Iterative Soft Robotics Design Experience

The content of instruction was a soft robotics design challenge which had been developed in the course of a design-based research project to support diversity in engineering and was taught in the second half of the course (Jackson, Mentzer, & Kramer-Bottiglio, 2021; Jackson et al., 2017). Soft robotics is a “young” (Bao et al., 2018, p. 229; Tauber et al., 2023, p. 9) area of engineering application which uses compliant, soft, and bioinspired systems to solve robotics problems (Trimmer, 2013). Compared to traditional robotics these systems have advantages such as handling fragile objects and human-centered applications (Wang et al., 2015). Given its nascency, there have been relatively few attempts to integrate soft robotics in secondary curricula, though these experiences have shown potential to grow students engineering conceptual understanding (Cayetano-Jiménez et al., 2024; Holland et al., 2018). Soft robot design is fundamentally iterative and involves many decisions for effective performance (Rus & Tolley, 2015). Furthermore, the pneumatic inflation mechanism of the fabricated robots provided clear evidence of success or failure—did it inflate? Yet, there were a variety of possible failure modes (Zhang et al., 2017) meaning that progression through the experience provided students with many opportunities to exhibit self-regulated learning as they planned for and tried to make sense of the results of their design.

The instructional experience was structured similarly by both teachers: a design challenge was introduced and students were scaffolded through design process steps such as defining the problem, research, brainstorming, prototyping, and iteration to develop their solution. The specific challenge given was to construct a soft robotic gripper to assist a farm operation to be more efficient in handling delicate produce without damage. Teachers’ initial instruction described the underlying principles of pneumatics and demonstrated fabrication of the gripper, which was through cast silicone adhered to a layer of cotton fabric to constrain inflation and produce curvature in the gripper. Fabrication was aided by the use of a reconfigurable mold that allowed students to adjust several design variables, such as the length and configuration of inner air chambers (see Figures 2, 3, 4). However, both teachers also expressed that iteration should be part of the experience, as teams initially worked on producing gripper “fingers” as a test of their function, then scaled the production to a completed gripper.

Figure 2

Figure 3

Figure 4

Once the challenge was introduced, student work on the project proceeded for 5–6 days on a block schedule (i.e., 90 min classes). Daily routines began with class-wide instruction and discussion before allowing students to work at their own pace and document their work in design notebooks. There were occasional class-wide check-ins at the end of the day by the teacher, to get a sense of students’ progress overall and address potential concerns. During work time, students were free to move through the classroom space to acquire materials or ask questions of the teacher or peers and the teacher moved through the class to interact with students. Assessment for the project was based on documentation in the design notebook which included evidence of the final robotic gripper performance.

Data Collection

A variety of information sources were used to understand student processes of self-regulation while designing, as summarized in Table 1. The use of several perspectives, called triangulation, is addressed by many researchers as a means to strengthen qualitative research (Creswell & Poth, 2018; Merriam, 1995). First, observations and field notes were completed as a “nonparticipant” (Creswell & Poth, 2018, p. 168), with limited interference with student groups in order to observe students’ natural regulation strategies and reactions to failure. After each observation the notes were digitized and summarized in a researcher memo.

Second, think-aloud audio recordings were maintained for each student during the project. Think aloud protocols are commonly used in design research (e.g., Atman et al., 1999; Daly, Yilmaz, et al., 2012; Mentzer et al., 2015) and have also been used in self-regulation studies (Baker & Cerro, 2000; Dixon & Johnson, 2012; Winne & Perry, 2000). Since students were working in pairs, very few prompts were needed to ask students to share their thinking while working—collaborative discussion was the status quo. In fact, Goldschmidt (1995) noted that team conversation offered similar insight to thinking. Compared to analysis through observations or student work alone, verbalization of thinking offers greater insight into design reasoning and strategy use (van Someren et al., 1994, p. 4).

Third, students were required to keep a design notebook that documented their work and was one of the main assessment procedures, as previously mentioned. Given the timing of the study, students were already familiar designing and documenting their work. As such, the design notebooks represented an opportunity to “corroborate and augment evidence from other sources” (Yin, 2014, p. 107). Some students referenced design process steps in their notebooks or reviewed previous projects to ensure they included the necessary elements in their current work.

Finally, two weeks after the soft robot design lessons concluded, semi-structured interviews were conducted with student pairs as a means of corroborating my account of the experience and to uncover additional information related to design and self-regulation acts that was not directly observed. These semi-structured interviews served as a form of member checking, to build trustworthiness in the research (Creswell & Poth, 2018), as well as providing another information source. The conversation touched on the approaches used while designing and recounted the various attempts that the teams had made (with images of their artifacts for reference). When discussing each sequential design version, teams were asked whether or not the attempt was successful. I interpreted their explanations and labeled each attempt as a success, a mitigated success (i.e., partly successful), or a failure (Sleezer et al., 2016). The semi-structured interviews were conducted with the two teams in each class together, and lasted less than 20 minutes.

Data Analysis

Analysis of qualitative data began informally from the moments of observation, through reflection and researcher memos (Creswell & Poth, 2018), with more formal analysis beginning after the observations had concluded. As a preliminary step to analysis, I compiled design process information from each pair through iterative milestones of design and labeled outcomes, as reported in the follow-up interviews. Photographs of design artifacts were put in order and described according to their design configuration and any deficiencies in performance. Daily progress in the design notebooks was also annotated to clarify the goal setting, planning, and evaluation conducted throughout the experience.

With each pair’s abridged design experience, think-aloud data, and follow-up interview, I used typological analysis (Hatch, 2002) to begin identifying patterns. Typological analysis is a deductive coding method that applies labels to the data from a predetermined list, then reviews these for patterns. Selected codes were based on the terminology used in the theoretical framework of self-regulation (Figure 1). The first pass through the data involved annotating the transcript for instances of each code. However, during this phase it became evident that some aspects of the study obscured routine performance (e.g., my prompting for think-aloud data differed from normal practice, and design documentation—a type of self-record—was ubiquitous). Therefore, performance elements of self-regulation were not included in further analysis.

Following labeling, collected forethought and self-reflection codes were summarized, followed by further analysis to identify patterns and themes. Hatch (2002, p. 155) noted that examples of these connections may be on the basis of similarity, difference, frequency, sequence, correspondence, or causation. Themes of behavior in forethought and self-reflective phases of self-regulation are described next, followed by a synthesis of conclusions related to patterns of success and failure while designing.

Abridged Design Experiences

Sydney and Jordyn approached the design of their gripper fingers as an experiment and varied designs strategically so that they could build an understanding of the relationship to performance. The team quickly moved on from each attempt—yet, in their frustrations, the team introduced new, crucial errors in the fabrication process that undermined each further attempt. In sum, the team produced three successful gripper fingers, three unsuccessful grippers, and one successful gripper.

Evan and Fiona similarly recognized opportunities for flexibility in the design process early on, however team decision-making was out of harmony. When their gripper was completed, it popped quickly in the testing process while picking up an object. Further negotiations between the pair reframed the expectations, moving from “So we can repair it though?” (Evan) to “But it worked, so it’s fine, we’re done” (Evan). The team had fixated on their ideas, having only one successful gripper finger out of four attempts, and one attempted gripper that was a short-lived success.

Brynn and Katelynn set early expectations based on the fact that they “didn’t think that this [project] would be that easy” (Katelynn). They asked questions of the teacher throughout the process which provided confidence to proceed. Their ability to diagnose failures in the first two fingers led to incremental improvements in their fabrication process and even supported their attention to and prevention of a potential failure mode on the third finger attempt. These preventative actions carried through to the gripper fabrication, but a small hole from an air bubble led to only partial success. Midway through class, the pair regrouped to identify problems and decide whether to change their design or do the same one over again. Because their reflection pointed to errors in fabrication (not function), they continued with their last iteration—the same design but a refined process. They made sure the mold was level and clips were firmly attached, they carefully and evenly spread materials, they made sure to mix slowly to prevent air bubbles. Unfortunately, their final design had uneven inflation despite troubleshooting. The team was able to pick up objects “for a second, or two” (Brynn) and the design was judged “successful but not quite successful” (Katelynn). The pair noted hypothetical solutions that could be carried out in another iteration, but after three attempted fingers, and two grippers—none completely successful—the team was done.

Wes and Taylor referred to earlier experiences with the design process as an orientation for the soft robotic gripper design project. They were eager to explore different arrangements as a way to experiment and see how the final operation of the design would be affected. The pair developed several configurations and tested them in sequence. Wes lingered on the design of the first finger once it was made, exploring its characteristics, even while Taylor began constructing their next attempt. The success of their first prototype excited new ideas, which were shared with neighboring design teams. The first and second fingers successfully inflated. The pair even contrived a new test of their second finger by inflating it from the other end (and holding the first point closed) to see how it behaved. Unfortunately, the team did not conduct as thorough analysis of their later designs, and moved on before successful troubleshooting. They abandoned the third finger test when it tipped sideways while curing and there were holes in the top. During construction of their first gripper the pair remembered details at critical times, which prevented some errors, but the design did not successfully inflate due to a poor seal. A second gripper was rapidly constructed, with the students noting “it’s actually a lot harder to build than you think” (Wes). The two made a cursory attempt to address errors observed in fabrication, but had low expectations for success. However, surprising the team, the gripper was partly successful—two fingers inflated well, two poorly, but it was airtight and able to pick up objects. By the end of the project, Wes and Taylor had produced two out of three successful fingers, an unsuccessful gripper attempt, and a partly successful gripper attempt. When they had lingered on analysis and troubleshooting, their attempts were successful.

Thematic Analysis of Design Experiences

Once codes were applied across the data sources, deeper analysis explained how successes and failures occurred for these beginning designers. Several main themes related to self-regulation in design are revealed through the focus on forethought and self-reflection.

Reflection: Evaluation Throughout the Design Process

In the theoretical framework guiding this work, the self-reflection phase includes evaluation and causal attributions related to the design work which lead to a designer’s determination about success or failure. Because of the range of information used by students when making these determinations, a broad definition of success or failure was required to respond to the design testing results or the procedural developments that occurred while designing.

More formal means of identifying failure were based on design testing milestones, described in the previous section. When designs did not work, the failure stimulated students to further inspection and reflection on underlying problems (Jackson, Godwin, et al., 2021). Problems were then documented to inform next steps. In the context of the soft robotics project, attributions for failure could be organized into three areas: materials, process, or conceptual understanding. Errors of materials, such as uneven molds, were especially frustrating for students because of their external nature. When students recognized a process-related error it was noted for later attempts and synthesized in a forethought phase of self-regulation. Finally, there were times where students missed instructions or did not have appropriate understanding to attribute the errors.

Informal evaluation by students took place throughout the design process based on in situ evidence. Examples included observations when constructing the robot, “I feel like that’s pretty good, maybe a tiny bit right there” (Jordyn); impromptu reactions to ideas, “Ours looks pretty solid if you wanted to know” (Brynn); or evaluation of brainstormed ideas, “I think we need to make it longer” (Wes). These fleeting moments tended to be based on assumption rather than tested results, but nonetheless informed the design process.

Reflection: Accumulating Information to Iterate or Interrupt Design

Students chose whether to change their soft robot designs or fabrication process based on a combination of their informal and formal evaluations and identification and attributions of success or failure. As teams navigated the results of their design tests, they referred to earlier goals, constraints, and criteria set in the design process. Initial cycles of design were most variable in terms of the design configuration, corresponding to the prefigured design tests of the forethought phase. These tests and evaluations allowed teams to converge on successful ideas: “We had one [finger] that was successful and the rest of them were varying. So [we’ll] kind of just stick with the one that worked” (Evan).

However, as teams proceeded with their grippers, the most salient information in reflection related to the fabrication process; attributions of failure were associated with the process instead of the design. Once teams had settled on a final design configuration, the main challenge became to execute that idea successfully. Sydney reflected that this point was “focused more on the changing of how we were executing the process rather than actually changing our design.” The abridged design experiences show that most teams did choose to iterate, though in different ways—some with repairs, some with new fabrication.

Implications for Educators

Although there are unique aspects of the soft robotics design context used here, several practices supporting students’ self-regulation during this study can be leveraged to help cultivate informed designers. First, teachers should provide contextual resources while students are designing that can support planning and reflection. For many of these beginning designers, limited conceptual understanding and past experiences meant that tangible materials were necessary to explore early in the design process. However, with instructional resources and the ability to collaborate on the project, technical difficulties of the project were mitigated. This finding also reinforces the importance of authentic design experiences and appropriate scaffolding and mentorship, so that students have a means to build experience (Jackson & Strimel, 2018). In authentic learning activities, the ability to refer to authentic situations (e.g., users, real-world problems) naturally offers students feedback on thinking and opportunities for planning and reflection (Strimel, 2014).

Second, teachers can encourage greater interaction with documentation in order to support learning while designing. When getting started with a new project, many of the students observed in this study referred to past projects and instruction for an initial structure. Later, students referred to constraints and criteria (set early in the design process) to write about whether their ideas were successful. They may have kept brief notes during the design process on the results of their design tests. Yet, these few opportunities represent the extent to which students referred to their documentation. Merely recording this information without referring back to it through reflection and evaluation is insufficient (Ates Akdeniz, 2023).

A chief intention of documentation is reflection (Lin et al., 1999), and the cyclical processes of design and self-regulation reinforce the benefits of using documentation in a more proactive manner. Students can be taught that the basis of informed decision making in design should be the information they have gathered from earlier stages. Then, at the transition of each iterative cycle in designing, they can be encouraged to reflect on what they have learned (reviewing past documentation and noting new insights) and what they will apply in their next attempts. Such review may work with other prompts to stimulate revisions to design in subsequent cycles of the process (Strong et al., 2020).