Understanding Access to Learning Opportunities in Collaborative Projects: Gendered Social Hierarchies in Student Teams

Academic Orientation Filter

Fowler and Su (2018) then argue that students interpret individual-level factors (i.e., their interests, skills, and prior experiences) through an academic orientation filter, which entails their motivations for learning. For example, students who might wish to take on a particular learning opportunity but are motivated by performance-avoidance due to a fear of being perceived to be incompetent by their peers, might be less likely to take on the very tasks they wished to pursue. Another example entails students who lack prior skills and experiences but are mastery oriented (i.e., motivated to master new skills or knowledge), which might thus make them more likely to pursue related tasks.

Fowler and Su (2018) note that the academic orientation filter is not socially neutral. Rather, societal norms, such as racialized and gendered performance expectations, inform students’ academic orientations, applying different social pressures on students to behave and perform, particularly in teamwork settings. For example, the authors describe the “performance-avoidance” orientation, wherein students are motivated to avoid appearing incompetent or less intelligent than their peers. McGee and Martin (2011) noted that these pressures to “always be on point” are an undue “burden” that some students, particularly those from minoritized backgrounds, used “to rescue themselves from being judged as less worthy or less capable of academic excellence” (p. 1367). Thus, the social pressures students experience, which are informed by racialized and gendered societal norms, might inform students’ academic orientations and behaviors in teamwork settings.

The academic orientation filter is important as an analytical lens for examining the role of equity issues in student teams. Simply measuring or examining students’ academic motivations alone might miss the ways racialized and gendered performance expectations shape students’ individual and collective behaviors in teams. For example, numerous scholars have charted racial/ethnic and gender differences in students’ engineering self-efficacy beliefs (e.g., Concannon & Barrow, 2009; Henderson et al., 2022) and motivational influences (e.g., Kolmos et al., 2013). However, simply noting the differences does little to explain how and why race/ethnicity and gender shape interactions, decision-making, task allocation, and ultimately students’ learning in collaborative projects.

In engineering project teams, individual characteristics like race/ethnicity and gender often translate into performance expectations, where White and male status carry with them societal assumptions about competence and intelligence, particularly when compared to students from minoritized racial/ethnic and gender backgrounds (Joshi, 2014; Keough et al., 2021; Nguyen et al., 2020). These performance expectations entail social consequences. For example, the consequences of “being wrong” or appearing ill-prepared might be harsher for minoritized students than their White male counterparts, leading some minoritized students to prioritize performance-avoidance or the obsessive need to “always be on point” as described by McGee and Martin (2011). Thus, understanding how these pressures appear in collaborative project teams, as well as the strategies students might employ to navigate them, is an important area for educational research.

Team Negotiation and Task Allocation

Finally, the patterns of tasks allocated to students in collaborative projects is interpreted as an ongoing negotiation among team members. Literature on teamwork in collaborative projects outlines several teamwork processes that shape team negotiation and decision-making. For example, Casakin & Badke-Schaub (2015) point to shared mental models (i.e., task models, process models, team models) that inform individual and collective approaches to design processes, where individual and collective understandings about design tasks, appropriate methods and practices for solving specific tasks, and knowledge about other team members’ roles and abilities, inform team behaviors. Others have examined communication patterns, team climate, psychological safety, team conflict, and team self-regulation (e.g., den Otter Emmitt, 2007; Stempfle & Badke-Schaub, 2002). All these lines of literature point to the idea that individual behaviors are best understood in the social and cultural context of the team.

Still, this study was concerned with the ways that social power dynamics and inequities might inform how students, both at the individual and team level, think about and determine task allocation. For example, existing research documents the ways that characteristics like racial/ethnic or gender identity carry with them societal performance expectations related to racialized and gendered assumptions about competence, intelligence, prior experience, and general ability (Paik & van Swol, 2021; Simpson et al., 2012). In engineering contexts, these assumptions often point to implicit beliefs that Whites and males are expected to be more competent in engineering tasks (e.g., design tasks, building tasks) than their counterparts from minoritized racial/ethnic and gender backgrounds (Keough et al., 2021). These assumptions are often manifested in task allocation, performance outputs, and patterns of influence in teams (Engle et al., 2014; Keough et al., 2021).

In this research, I conceptualized tasks to be both opportunities to participate in the collaborative design process (e.g., opportunities to contribute ideas during team meetings), as well as the opportunities taken by individuals or a subset of the team to complete portions of the team project separately from the team (e.g., the person assigned to review presentation slides). Moreover, the tasks allocated to students were documented across three broad categories – design, building, and documentation tasks. For example, design tasks included opportunities to communicate, clarify, and refine design ideas during the ideation process, as well as the technical tasks related to the design process such as developing the team’s computer-aided design (CAD) model. Building tasks included opportunities construct various elements of the team’s design (e.g., laboratory work). Finally, documentation tasks included tasks such as developing reports and presentations for class assignments, as well as notetaker roles during team discussions and testing. Importantly, the MITA model suggests an iterative process of understanding team-level task negotiation in which the types of learning experiences afforded to students (i.e., the tasks allocated to them), shape their resulting skills, interests, and self-efficacy beliefs, which in turn informs their later academic orientations, later team negotiation, and later task allocation. Thus, students allocated CAD modeling tasks are more likely to develop skills, interests, and confidence related to CAD modeling, which inform later academic orientations and resulting task allocation.

This research focused on these broad categories of tasks due to their importance as learning opportunities in design teaching and learning (Dym et al., 2005), as well as existing research suggesting that characteristics such as race/ethnicity and gender can shape the degree to which students gain access to these opportunities. For example, Engle and colleagues (2014) argued that negotiation in student-led discussions consists of four components: (a) the negotiated merit of each student’s contributions, (b) each students’ intellectual authority, (c) the degree to which students have access to the conversational floor, and (d) students’ spatial privilege. However, existing research suggests that even when students have relatively equal access to the conversational floor, the types of communication tasks they take on differ along gendered lines (Hirshfield & Koretsky, 2017). This underscores the need to focus not just on the degree to which people have the floor, but the actual tasks they are completing when they have the floor, a central goal of the MITA framework. Guided by the MITA framework, the goal of this study was to examine how students allocate tasks on teams, as well as to examine how power dynamics shape the relationship between students’ initial predispositions, academic orientation, team negotiation processes, and ongoing task allocation.

Research Context

This research was part of a larger, mixed-methods study examining teamwork processes in engineering design project teams. While the focus of this article is task allocation in student design teams, the goal of the broader study was to understand how power dynamics in teams shaped teamwork processes (e.g., communication, idea uptake, idea enactment, design decision-making). The context for this study was a first-year engineering design cornerstone course, ENG1, at a large, Midwestern, public university. ENG1 is described as a team-based design-build-test-communicate course. ENG1 was delivered around an underwater vehicle design project, wherein 12 design teams consisting of four or five students all attempted to address a design challenged centered on remotely operated vehicles (ROVs). Each team in the course was assigned a peer mentor – a student who had taken ENG1 in a prior semester – who could offer feedback and guidance on the team’s design process.

Teams were formed by the instructional team based on several pedagogical goals, such as a desire to avoid isolating women with all-male teammates, team diversity, living arrangements, and safety concerns. I selected three teams—one per section of the course—based on invitations (i.e., only teams that expressly invited me to observe were considered for the study), and diversity along several dimensions (e.g., racial/ethnic, gender, epistemic diversity). The three focal teams were also representative of the course. For example, five of the 12 teams were all-male (e.g., The Yachtsmen), and the other seven teams consisted of at least 2 female students (e.g., Team Mobula and Team Surge).

The course was designed around a set of deliverables organized to facilitate individual and team-level design processes. For example, at the beginning of the project, and prior to students meeting their project teams, students were asked to submit an Individual Design Proposal (IDP) of their initial ROV design ideas. Following the submission of their IDPs, students were assigned final project teams where they began by completing an initial “Meet the Team” assignment during which instructors asked students to discuss their prior experiences, the skills they brought to their team, the skills they hoped to refine or gain, and any passions they brought to the project. Next, drawing on their IDPs, each team was to develop a Preliminary Design Review (PDR) presentation by narrowing their individual ideas into a collection of 2–3 preliminary ideas. Finally, student teams were to produce a Detailed Design Review (DDR) and Critical Design Review (CDR), which entailed narrowing their ideas to a single concept.

As the project progressed, students began to delegate tasks (e.g., design, building, and communication tasks) in their teams. The tasks were generally delegated to address important deliverables (e.g., building assignments, the Design Reviews described above, the course-ending competition). The goal of this research was to document the process by which students negotiated and ultimately delegated tasks in their teams.

Data Collection

This work draws on ethnographic data, where I used three strategies for data collection: (a) ongoing observations of three focal engineering design teams, (b) one-on-one post-experience interviews with members of the focal design teams, and (c) reflections from the course’s peer mentors. The full corpus of data included observations of 45 team meetings lasting anywhere from one to five hours. I also conducted interviews with all 15 members of the focal teams. Finally, nine of the 12 peer mentors submitted electronic journals, although two of the three peer mentors who did not submit journals were peer mentors to focal teams.

Data Analysis

Qualitative data analysis proceeded in three stages. I began by transcribing handwritten fieldnotes. After transcribing fieldnotes, I followed the systematic process outlined by Emerson and colleagues (1995) for facilitating the data analysis in ethnographic studies. First, I read and re-read fieldnotes and reflective journals to elaborate and refine initial jottings given further observations over the course of the term. Moreover, I documented my reflections in analytical memos, returning to my memos over time to elaborate on ideas, or put ideas in richer context given other analytic memos or analyses, as I analyzed further data.

Second, I engaged in an iterative process of coding the fieldnotes, interviews, and peer mentor journals. Since this research was part of a larger study that included an examination of the types of tasks students took on in collaborative project teams, I began by using a set of a priori codes that reflected how tasks were delegated in each team, patterns of communication and decision-making, and epistemic cognition at both the individual and team levels. For example, code examples capturing the tasks students were delegated included the task types (e.g., design-related tasks, building tasks, technical communication tasks), and how tasks were delegated (e.g., granted, taken, avoided, denied). I documented the reading and coding processes, and ideas, themes, and descriptions I developed during the coding and analysis process in reflective memos. Example codes are listed in Table 2 below.

Finally, I synthesized findings across data sources. For example, I compared students’ reflections about task delegation in the focal team interviews to patterns of task delegation documented in my fieldnotes, noting similarities and differences in students’ reflections from my own commentary. I also compared peer mentor observations to my own observations to ascertain if patterns I documented in my fieldnotes were also observed in the non-focal teams. An example of the synthesis process is presented in Table 3 below.

As I developed themes surrounding the role of prior experiences, skills, self-efficacy, academic orientation, and demographic characteristics (e.g., race/ethnicity, gender) in collaborative projects in engineering, I returned to all qualitative data sources to separate discrepant evidence, noting places where my expectations about the role of these factors appeared to undermine the theoretical framework and existing literature (i.e., subjectivist and intertextual reflexivity) (Alexander, 2003; Denzin, 1997). Finally, while the coding and analysis process was undertaken alone, I engaged in reflexive practice by discussing the analysis process and emergent findings with research collaborators and advisors.

Characteristics of the Task Allocation Process: The Role of Prior Experiences and the Academic Orientation Filter

Unsurprisingly, students’ prior experiences played a key role in their design thinking and working relationships, including the ideas they pursued at the individual and team levels, as well as the process of negotiating task allocation. Some examples mirrored a straightforward interpretation of the role of prior experiences in students’ academic orientation and task allocation, where students with prior mastery experiences in a task (e.g., coding), were more likely to take on related design tasks (e.g., coding tasks), due to their familiarity and confidence with those tasks. For example, Cam’s teammates quickly identified him as an expert in SolidWorks®, and as a result, the team asked Cam to take on CAD modeling tasks that required SolidWorks® skills. Cam, who shared that he had “a fair bit of CAD experience,” reflected on the role his prior skills played in his working relationships:

That Cam was delegated the CAD modeling tasks due, in part, to his prior skills and self-efficacy is unsurprising. However, Cam’s prior experiences be understood in the context of his team, as well as the skills, self-efficacy, and academic orientations of his teammates. For example, Seth expressed that he had struggled with early CAD modeling assignments, and had hoped to avoid the CAD modeling tasks during the project:

The MITA model posits that students’ individual prior experiences and related self-efficacy beliefs inform their academic orientation, as well as team negotiation of task allocation. This finding supports the MITA model’s presupposition. Seth’s prior experiences and related lack of confidence, as well as his perceptions regarding Cam’s prior experiences, informed his academic orientation (i.e., performance-avoidance), making it more likely that Cam, the student with more prior experiences and confidence, would take on the CAD modeling tasks.

Still, other examples suggest the relationship between prior experiences, academic orientations, and task allocation are not always straightforward, underscoring the importance of the team negotiation process. Kyle, who had discussed a desire to gain CAD modeling skills during the early “Meet the Team” exercise, and reiterated that desire during his one-on-one interview, lamented the fact that he had done less of the CAD modeling that he had hoped:

Kyle’s prior experiences informed his mastery orientation—his desire to master prior CAD modeling skills. However, the team’s perceptions of Cam’s skillsets, coupled with a desire to get Cam more involved in the design process, led The Yachtsmen to delegate the CAD modeling tasks to Cam. A similar dynamic played out with regards to coding tasks. Seth, who acknowledged that he would not be the strongest coder on the team given Paul’s and John’s prior experiences, similarly lamented missed coding opportunities due to both the COVID-19 pandemic and the fact that Paul and John were more likely to contribute to the coding tasks:

Seth similarly articulated a concern that his teammates brought more experiences with laboratory machinery, which might inform the types of tasks he was allocated:

As a result of his perceptions of his teammates’ prior experiences and skills, Seth deferred the coding and building tasks to those teammates, choosing instead to take on tasks related to the teams’ calculations, for which he believed his brought prior skills and experiences were sufficient. Moreover, Seth acknowledged that while he had wanted to grow in his laboratory skills, he and his teammates took on “part of the project that they were comfortable with,” leading to his role as the person in charge of calculations:

Importantly, Seth connected this decision to his learning in the course, suggesting that while he had gained some skills in the coding and building tasks he had deferred to other teammates, he still had not grown comfortable enough to complete the tasks himself:

Other examples similarly displayed how the assumption that prior experiences and related confidence might lead to allocation of related tasks may not be straightforward. For example, Rehman (Team Surge) shared that he drew on his prior experiences designing ROVs, and that those prior experiences gave him confidence (i.e., self-efficacy in the MITA model) that his ideas were sound:

Rehman, who acknowledged “taking a backseat” in the task allocation process, described how his prior experiences in project teams led him to a performance-avoidance orientation, not due to a lack of confidence, but due to his reflections on his prior experiences:

Other students similarly shared how prior experiences shaped their motivations for particular tasks. Kevin (Team Mobula) shared:

Kevin described organizing his work both to gain opportunities to practice driving the team’s ROV, a task he had not been able to complete thoroughly during his prior experiences, as well as to avoid onerous re-design tasks that had plagued his prior design experiences. As a result, Kevin was unwilling to pursue complex ideas, and deferred some design thinking and idea selection to his teammates with these goals in mind:

Both examples demonstrate the role prior experiences play in shaping students’ design self- and team-efficacy, as well as their individual academic orientations in collaborative projects. According to the MITA model, students’ prior experiences inform their motivations for learning (i.e., their academic orientation). For Rehman and Kevin, their prior experiences and resulting confidence led them to performance-avoidance orientations for particular tasks. Rehman’s prior experiences building ROVs led him to be unmotivated to pursue design tasks for which he had been previously heavily involved (i.e., in high school). Rehman’s academic orientation (i.e., his comfort and willingness to avoid tasks) was informed by his prior experiences in design teams. Similarly, Kevin’s prior experiences designing robots and his resulting lack of confidence that the team could complete a complex design during the term informed his avoidance orientation. Kevin’s academic orientation (i.e., avoiding more complex designs, deferring ideation tasks to teammates, organizing for testing tasks) was informed by his prior challenges in design teams in high school. Both students’ experiences appeared to shape their approach to the team negotiation process, as Rehman sought a “back seat” to his team’s task delegation process, and Kevin deferred ideation discussions to his teammates.

These findings underscore the important role of the academic orientation filter in the MITA model, as well as the complexity of the relationship between prior experiences, academic orientation, and task allocation in teams. The assumed relationship between prior experiences, skills, self-efficacy, and task allocation, where positive prior experiences and strong confidence translate into related task allocation, did not always hold in the focal teams. While some students (e.g., Cam and CAD modeling, Seth and calculations) were allocated tasks by their teammates due to their prior experiences, and some students (e.g., Seth and CAD modeling, Kevin and complex design ideas) avoided tasks due to negative prior experiences, the opposite relationship was also true. Some students (e.g., Rehman taking a “back seat”) avoided tasks precisely because they brought prior mastery experiences and therefore did not feel a strong motivation to participate in those tasks. Their academic orientations – their motivations for learning – appeared to shape their individual and collective approaches to negotiating task allocation, which this study views as important learning experiences.

The Role of Gender in the MITA Model

The core purpose of the MITA framework is to examine inequities in the ways that tasks are allocated in teams. Fowler (2020) notes that the ways inequities appear in teams may be visible (e.g., biased language such as racial slurs, intimidation, physical violence), or invisible to instructors (e.g., psychological safety, beliefs about performance expectations). Findings in this research pointed to the ways that gender influenced the factors described in the MITA model (e.g., self-efficacy, academic orientation, team negotiation). For example, the ways students described their interests, skillsets, and prior experiences was also informed by gendered performance expectations, where the assumed relationships between prior skillsets and experiences and eventual task allocation during the collaborative project (i.e., that a person with prior skills in CAD modeling might be more likely to take on the CAD modeling tasks during the project) did not hold for all students.

For example, during the early “Meet the Team” exercise, two women on Team Surge told their teams that they held no engineering skills, which I documented in my fieldnotes:

However, upon reflection during post-experience interviews, Stephanie indicated that she had undersold her skills due to her low self-efficacy:

When describing her lack of confidence, Stephanie centered the role that internalized gender performance expectations played in shaping her evaluations of her prior experiences and skills. Stephanie shared:

As the MITA framework suggests, examining Stephanie’s prior experiences alone does not entirely address the ways those skills and experiences shaped her individual and collective approach to the design process. Instead, the role of Stephanie’s prior experiences and skills is best understood as a function of her self-efficacy beliefs, which were informed by gendered performance expectations and contributed to her academic orientation (i.e., her preferences for gaining skills in building and later avoidance of CAD modeling tasks). Initially, Stephanie’s self-efficacy beliefs, which she believed were informed by gendered performance expectations, led her to devalue her prior experiences, which in turn informed how she represented her skills during the team negotiation process. While this supports the Fowler and Su’s (2018) claim in the MITA model that self-efficacy shapes the team negotiation process in ways that might be invisible to observers and team members, it challenges previously understood relationships between prior mastery experiences and self-efficacy.

Moreover, Fowler and Su (2018) argue that the MITA model is iterative, with prior experiences and skills informing initial self-efficacy, academic orientation, and team negotiation, and task delegation, as well as later self-efficacy, academic orientation, team negotiation, and task delegation. Stephanie’s experience in the context of Team Surge’s task negotiation underscores the iterative nature of the MITA model. Stephanie’s representation of her skills resulted in her being delegated almost sole responsibility of the team’s CAD modeling tasks. However, as the team struggled to develop their CAD model in time for an important presentation, Stephanie’s self-efficacy for CAD modeling faltered, and the task was ultimately reassigned to Ryan, which I documented in my fieldnotes:

This series of events underscores the iterative nature of the MITA model. Stephanie’s initial devaluation of her coding skills during early team discussions led her to take on tasks for which she was also not confident, resulting in negative experiences in CAD modeling, which itself resulted in a second reshuffling of task allocation in Team Surge. Over the course of the project, Stephanie’s struggles with CAD modeling negatively impacted her self-efficacy, which informed her academic orientation, leading her to avoid further CAD modeling task. Eventually, the CAD modelling task was turned over to Ryan for completion.

Gender also appeared to shape team discussions and negotiation processes that resulted in patterns of exclusion from some tasks. One such pattern occurred in moments during which the work and contributions of women were more heavily scrutinized or were changed by men without a woman’s knowledge. For example, during my observations of one Team Mobula meeting, I documented an exchange in which both Addy and Chelsea noticed that their male teammates had revised their contributions to the team’s presentations without their knowledge. Moreover, Chelsea described how she interpreted Matt’s behavior in the context of her prior engineering experiences, as well as the historical context of women’s experiences in engineering more broadly:

These incidents were part of a larger pattern in Team Mobula in which women appeared to be excluded from decisions and speaks to the ways that gendered power dynamics might shape students’ learning in invisible ways. Patterns of exclusion were, at times, physical (i.e., men making design decisions in women’s absence). For example, during one Team Mobula meeting, I documented an exchange between Matt and Chelsea, in which Matt informed the team that he had made changes to their design ahead of their presentation:

These incidents speak to the ways that patterns of marginalization can be both visible and invisible in student teams. Publicly, and visibly, it appeared Chelsea was engaged in particular tasks, contributing equally to her team’s project. Privately, however, her male teammates revised Chelsea’s and Addy’s work without her knowledge and contributions should be seen as a moment in which both women missed out on an important learning opportunity, which Chelsea appeared to interpret as a judgement of her adequacy as an engineer. Moreover, the process of learning to communicate, negotiate, clarify, and refine ideas is critical to students’ learning, particularly in engineering design (Dym et al., 2005). That some of these opportunities appeared to occur in Chelsea’s and Addy’s absence means, implicitly, that they missed out on some of the experiences critical to achieving the learning goals of collaborative projects (Li et al., 2019).

During my observations, I documented times in which I perceived Addy was being excluded, either implicitly or explicitly, from the team’s work. For example, during one team meeting, during which Team Mobula made important design decisions in Addy’s absence, I wrote:

In Addy’s absence, the team also began to assign tasks in the project, with Matt assigning himself the critical CAD modeling task and assigning Addy tasks that he noted were “the easiest way to do something if you were not here.”

Addy’s exclusion from the meeting was part of a pattern that some of her teammates acknowledged. While Addy was clear that she did not feel like her teammates had treated her differently on racialized or gendered grounds, her teammates acknowledged both distrust of her work and a collective failure to fully include her in team discussions. For example, Max, reflecting on this and other incidents that occurred during the project, acknowledged they “didn’t do a good job of bringing her into discussions” and recognized that some experiences may have left her “feeling like she didn’t really belong.” These findings suggest the team negotiation process is another place in the MITA model in which gender might shape teams’ task allocation decisions.

Gendered Power Dynamics and Task Allocation in Teams

Other patterns of marginalization and exclusion included patterns of interruption well-documented in literature on women’s experiences in male-dominated spaces like engineering. During my observations, I began to document what I called a “culture of interruption,” which entailed moments during which opportunities to contribute ideas to team discussions were cut short by interruptions. For example, during one laboratory session, I wrote:

Interpreting Matt’s and Ryan’s behaviors was a methodological challenge. First, I understood that Matt and Ryan were unlikely to report that their behaviors (e.g., interruptions, changing working) occurred because Chelsea and Stephanie were women. Still, both Chelsea and Stephanie interpreted the incidents in the broader context of women’s’ experiences in engineering. Here, I engaged in reflexive practice – intertextual reflexivity, in which I situate my interpretations in broader literature, and subjectivist reflexivity, in which I engaged in self-critique (Alexander, 2003; Denzin, 1997). While I acknowledged that it might not have been Matt and Ryan’s intentions to enact normative sexist behaviors against the women in their teams, the women in their teams clearly interpreted these experiences within a broader context. Just as Chelsea pointed to the role of gender in the meaning she made of Matt’s repeated revisions to her work without her knowledge, Lauren and Stephanie also pointed to the role of gender in their interactions with their teammates. In their interviews, both men and women pointed to patterns of interruption during team negotiation, discussing how such patterns informed their team working dynamics. Lauren (Team Surge) shared:

On reflecting on these incidents, Ryan acknowledged patterns of interruption, and pointed to lessons from instructors about the experiences of women in engineering:

This research presumes opportunities to communicate, clarify, and refine ideas in collaborative project teams, particularly with those who are different, as important learning experiences (Kuh, 2008). Taken collectively, patterns by which women were excluded from decision-making, and the thinking and discussions decision-making entails, and patterns of interruption undermine women’s opportunities to participate in those important learning experiences equitably. These findings suggest the relationship between self-efficacy, academic orientation, team negotiation, and task allocation is muddied by the role of gender and gendered power dynamics in teams.

Gendered Strategies for Exerting Influence over Task Allocation

I also documented strategies that women employed to exert influence over task allocation and team decisions. One such example, which I documented during my observations of the three focal teams, entailed moments in which women, who were strategically placed on teams with at least one other woman, “cleared the floor” for other women by subverting attempts to undermine or interrupt their women counterparts’ ideas. For example, during one team meeting, while Lauren (Team Surge) began advocating for an idea, Ryan, her male counterpart, attempted to interrupt Lauren’s justification. In that moment, Stephanie, a female student on Team Surge, stepped in to support Lauren – “Wait! Lauren wasn’t done.” – thereby clearing the floor for Lauren’s ideas to be heard.

The pattern by which women appeared to work together to take on particular learning opportunities was one of several seemingly strategic approaches I documented in my fieldnotes. For example, during one team meeting, when Team Surge was clarifying their ROV frame concept, two male students, Rehman and Danish, contributed similar ideas for a modification to the frame. I documented the exchange in my fieldnotes:

I returned to these moments during interviews following the conclusion of the project, and members of Team Surge reflected on the role of gender in their team working dynamics. Stephanie shared:

While reflecting on Team Surge’s discussion about their frame design, Lauren shared how she had discussed her ideas with Stephanie first to clarify her ideas, making it more likely that she would be heard on her team:

These moments were also documented in other teams in both my observations and peer mentor reflections on non-focal teams. For example, one peer mentor described a clear pattern of divides on their assigned team:

These gendered divides in task allocation had at least two effects on team working processes that I documented in my fieldnotes. First, such divides ensured that some aspect of the project uniquely belonged to the women on the team. Second, these gender divides also ensured patterns of interruptions, argumentation, and marginalization by men ceased. Collectively, these strategies underscore the importance of considering gender in the MITA model, where task allocation is informed, in part, by both gendered patterns of exclusion and marginalization, as well as women’s strategies for resisting those patterns of exclusion and marginalization during the team negotiation process.

Still, while women appeared to exert influence strategically by clearing the floor or pairing for design tasks, the patterns by which men exerted influence over task allocation also called attention to the mediating role of gender in task allocation and learning opportunities in engineering teams. For example, Matt, a White male member of Team Mobula, was described by team members as a “de facto team leader.” When I asked members of Team Mobula how this came to pass, one member offered that it was due to “subtle factors”, such as his friendliness and the fact that he “seemed like he knew what he was doing.” This description of male teammates occurred twice during the Design Experience Interviews with members of Team Mobula. Kevin, an Asian male member of Team Mobula, was given almost sole responsibility for a major portion of the project. Max offered that the team trusted Kevin, that the trust was “implicit,” because “we just kind of knew that he knew what he was doing.” Importantly, this “implicit trust,” which Max referred to as “a psychological question,” resulted in Kevin taking on tasks for which other students felt they lacked prior experience or confidence. Max shared:

That Matt and Kevin assumed leadership roles and tasks on their teams due, in part, to implicit assignments of competence from their teammates points to the ways gendered performance expectations shape task allocation in student teams. In engineering, where White, Asian, and male are associated with competence (McGee et al., 2022), this research suggests that these characteristics might also have translated into the task delegation to students in their respective teams.

Conversely, the patterns I described by which women exerted influence over their teams appeared to come with social consequences. For example, one peer mentor described how one woman on his team “seemed to be most confident in what was going on,” but later described the same woman as “talking down to most of her teammates” and “dominating design discussions in general,” a familiar description of women who assume leadership roles and exhibit non-stereotypical traits (e.g., assertiveness, decisiveness, confidence) (Layne, 2010).

The social consequences of gendered patterns of influence were apparent throughout my observations and students’ reflections. For example, Addy, who had not been present when tasks were delegated, came to believe that workloads had been relatively equal and expressed few concerns about the team’s working process. However, when reflecting on the team’s task delegation strategy, Matt acknowledged that Addy would “get the work that no one wants to do,” which he believed Addy was happy to do. Still, this task delegation process later resulted in concerns about workload equity, a concern Addy later expressed in her interview:

Perhaps the most jarring example of the social consequences of women’s strategies for exerting influence over teams occurred during one Team Surge meeting, which Rehman referred to as the team’s “worst day.” The team had been engaged in a contentious discussion about the dimensions of their ROV frame to finalize their CAD model and begin building tasks. As the argument became more intense, Stephanie’s insistence on completing the task, which had been delegated to her, resulted in one student, Rehman, leaving the room in frustration. Rehman reflected on the incident during his interview:

Rehman, who referred to Stephanie as “stubborn” and “freaking out,” even as he acknowledged support for her perspective, reflected normative responses to women exerting influence that might negatively affect women’s willingness to participate in engineering teams. Taken collectively, these findings suggest women’s strategies for exerting influence over task delegation and, by extension, gaining access to learning opportunities in team-based design settings, might come with particularly deleterious social consequences that shape team communication, interactions, and working processes.

Gendering the MITA Model

Existing research indicates that women are more likely to take on less technical tasks, as well as less favorable tasks, in student design teams in engineering (Fowler & Su, 2018; Hirshfield & Koretsky, 2017). This research extends those findings by examining the negotiation process to understand how individual and team-level factors shape the task negotiation and allocation process. Like their male counterparts, women pointed to their prior experiences, skills, and self-efficacy when describing their motivations for learning and eventual task allocation. However, the ways that these factors shaped the negotiation process were at times troubled by gendered performance expectations, patterns of exclusion, and a culture of interruption.

For example, this research conceived of opportunities to contribute to design tasks, such as communicating, clarifying, refining, and implementing design ideas, as central tasks, and thus important learning opportunities, in engineering design education. Dym and colleagues (2005) described design thinking as an iterative process of divergent-convergent thinking, where divergent thinking “takes place in the concept domain” where “the questioner attempts to diverge from facts to the possibilities that can be created from them,” and convergent thinking entails attempts to “converge on and reveal ‘facts’” (p. 105). However, in design teams, where problems are ill-structured and entail multiple solutions paths, concepts and answers do not have one verifiable, true answer (Dym et al., 2005; Jonassen, 2000, 2015). Thus, opportunities for the entire team to communicate, clarify, refine, and implement ideas (i.e., divergent thinking) hold critical importance for team design decision-making (i.e., convergent thinking) and, by extension, students’ learning in team settings.

The utility of the MITA model in this research is that the model offers factors – gender, self-efficacy, team negotiation, and academic orientation – that muddy the widely held view of design thinking as an iterative process of convergent-divergent thinking in that the nature of divergent-convergent thinking in teams is negotiated and entails gendered power dynamics that shape whose thinking is even present, as well as whose thinking is elevated or marginalized during the negotiation process. If, as Stephanie suggested, some students “can’t finish a sentence,” or are excluded entirely from design discussions on gendered grounds, the iterative process of divergent-convergent thinking is disrupted, and students’ learning is undermined. In this study, that this appeared to happen to women more often indicates that gender is an important factor disrupting the iterative process of divergent-convergent thinking.

However, this research also suggested pedagogical approaches for mitigating the role gender might play in disrupting design thinking in students’ teams. One common pedagogical strategy for addressing patterns of marginalization for women in collaborative project teams is for instructors to assign women to teams that avoid “stranding” them alone with all male teammates (Keough et al., 2021). This research offers promising support for such pedagogical practices. In this work, women appeared to work together to exert influence over task delegation negotiations, thereby leveraging each other to gain access to learning opportunities.

Karpowitz and Mendelberg (2014) noted that “authority is the expectation of influence” and that “men and women tend to enter the room with different levels of authority.” However, Karpowitz and Mendelberg also noted one can act in a way that enhances one’s own authority and that “others can act in ways that enhance or detract from another’s authority” (p. 18). Unsurprisingly, known behaviors that contribute to the marginalization of the work and contributions of women in engineering, such as patterns of interruption and exclusion from decision-making, were exhibited in the collaborative project teams examined in this study. These behaviors threatened to undermine women’s access to important learning opportunities. However, the patterns by which women worked together, often with the effect of gaining more full participation in their design teams was one way that women enhanced each other’s authority and had clear implications for task allocation and, by extension, their learning. Still, instructors should be attuned to the potentially negative social consequences associated with the behaviors women employed to gain access to design tasks, as well as patterns of implicit bias that elevated men to positions of leadership in design teams.

Moreover, gender influenced the factors described in the MITA model in ways outside of interpersonal interactions, ways that were internal to the students in the project teams. Quantitative research is inconsistent regarding gender differences in students’ engineering self-efficacy (e.g., Concannon & Barrow, 2009; Henderson et al., 2022; Marra et al., 2009; Schar et al., 2017). However, this research indicates students’ self-efficacy beliefs stemming from their prior experiences or inexperience might be understood through a gendered lens, where societal performance expectations might lead women to underrate their skills and experiences in the social context of engineering teams. Stephanie, for example, clearly related her lack of confidence to her status as a woman in engineering and the marginalizing experiences that status has entailed. Similarly, Chelsea clearly understood processes of exclusion (e.g., changing her work without her knowledge) as typical of broader patterns of gendered marginalization in engineering.

In a study examining the racialized experiences of Black doctoral students in STEM disciplines, McGee and colleagues (2022) argued that structural racism is a source of impostorism, “described as an individual’s intellectual self-doubt and fear of failure, which is characterized by concern that others have overestimated their talents or abilities and therefore feel defrauded” (p. 488). In this view, structural issues, such as the lack of Black students and faculty in STEM, stereotyping, attrition amongst Black students and faculty, and other issues lead to anxiety about one’s belonging in the discipline. Findings in this study suggest that a similar dynamic for women in engineering might play out in collaborative projects, and perhaps engineering more broadly, where anxiety about one’s prior skills and belonging might lead women to underestimate or underreport their skills. For example, Marra and colleagues (2009) noted that “women who leave engineering consistently express less confidence in their abilities than the men and women who stay, regardless of the fact that their academic performance is the same or better” (p. 29). This research extends those findings by illustrating how such dynamics appear in interpersonal interactions, particularly in collaborative design projects, with consequences for students’ learning.

Herein lies the importance of the academic orientation filter for examining how students’ self-efficacy contributes to task allocation and student learning. If it is true that women are more likely to underrate their prior skills, the consequences for teamwork pedagogies abound. Students in this study who lacked confidence to pursue particular tasks appeared to be more likely to bring a performance-avoidance orientation to those tasks, deferring the tasks to students they believed were more competent or confident. From a pedagogical perspective, this is worrying given that some students, particularly men, were perceived to be more competent for “implicit” or “psychological” reasons – not reasons supported by evidence of their competence. Thus, important learning experiences were afforded to students based on perceptions, not necessarily evidence, of their competence, meaning some students did not fully participate, or were altogether absent, from those experiences.