Controlled Chaos: How Structure Enables Interdisciplinary Teamwork in Mega Classes

Practice Paper Recommended citation: Truscott, F., & Smith, L. (2025). Controlled Chaos: How Structure Enables Interdisciplinary Teamwork in Mega Classes. In Kangaslampi, R., Langie, G., Järvinen, H.-M., & Nagy, B. (Eds.), SEFI 53rd Annual Conference. European Society for Engineering Education (SEFI), Tampere, Finland. DOI: 10.5281/zenodo.17631525. This Conference Paper is brought to you for open access by the 53rd Annual Conference of the European Society for Engineering Education (SEFI) at Tampere University in Tampere, Finland. This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License. Controlled Chaos: How structure enables interdisciplinary teamwork in mega classes F. R. Truscott a, 1 and L. Smith b1 a Centre for Engineering Education, UCL, London, UK, ORCID: 0000-0001-91532077 b University of Pretoria, Pretoria, South Africa, ORCID: 0000-0001-8022-0000 Conference Key Areas: Engineering skills, professional skills, and transversal skills, Building the capacity and strengthening the educational competences of engineering educators Keywords: Teamwork, mega-scale teaching, structured learning, engineering education ABSTRACT As engineering education increasingly integrates teamwork and interdisciplinary learning, scaling these experiences to accommodate the large cohorts typical of engineering programmes presents unique challenges. Smaller courses benefit from organic, instructor-led interactions that are typical of teaching interdisciplinary team projects and common in literature; extremely large modules (900+ students) demand structured frameworks to ensure consistency, engagement, and skill development as well as making efficient use of the limit resources available. Prior research highlights the importance of self-directed learning and team dynamics at smaller scales, but the role of structured processes in supporting these at scale remains underexplored. This study builds on work previously done by the authors on the impact of extremely large-scale cohorts on the pedagogy of interdisciplinary team projects by unpacking how structure can facilitate effective teamwork in two large-scale engineering modules—one in the Global North and one in the Global South. 1 Corresponding Author F. R. Truscott and L. Smith [email protected] and lelani[email protected].za 1 INTRODUCTION Engineering education is increasingly expected to equip graduates with the competencies needed to address complex, interconnected global challenges— challenges such as climate change that cannot be solved through technical knowledge alone. In response to demands from industry, professional bodies, and society, curricula are incorporating more opportunities for students to develop professional skills such as teamwork, problem solving, communication, and ethical decisionmaking. These competencies are central to the sustainability frameworks outlined by UNESCO (2017) and align with calls for more socially embedded, systems-oriented ways of thinking in engineering (Strachan et al., 2022; Graham, 2018). Interdisciplinary team projects have emerged as a powerful pedagogical vehicle for fostering these skills, requiring students to collaborate across disciplinary boundaries, confront ambiguity, and integrate diverse forms of knowledge (Lattuca et al., 2004; Kolb, 2015). However, the implementation of these learning activities demands a significant shift in the teaching approach. Unlike traditional lecture-based formats, interdisciplinary team projects require the practitioner to walk the learning journey with students, offering facilitation, coaching, and empathetic support rather than simply transferring knowledge (Jung et al., 2005; Lekalakala-Mokgele, 2010). The social and emotional dimensions of this work are substantial, as students must navigate interpersonal dynamics, conflict, and discomfort while working in diverse teams—often outside their disciplinary or personal comfort zones. Practitioners must be prepared to support these affective dimensions of learning, which are as critical to the success of the project as the technical elements. This teaching mode requires a distinct skill set— one that may be unfamiliar to educators trained in more content-driven, individualistic approaches to teaching. As such, interdisciplinary teamwork is not only pedagogically rich, but also professionally demanding, especially when scaled to extremely large cohorts. The Role of the Practitioner and the Structural Challenge Interdisciplinary team projects shift the role of the educator away from content delivery toward facilitation, guidance, and emotional support (Truscott & Smith, 2024). Rather than focusing solely on technical outcomes, practitioners must walk alongside students as they navigate the social and affective dimensions of teamwork—managing group dynamics, learning to collaborate, and coping with the emotional complexity of peer engagement (Truscott et al., 2021; Smith & Trent, 2021). These responsibilities demand a teaching skill set that many engineering educators are not formally trained in. When modules scale to 900 or more students, the demands on the practitioner intensify, with the perceived impossibility of providing the kind of individualised support these projects seem to require. This has led to assumptions that such experiential, skills-based learning cannot be effectively delivered at scale. However, as institutions embed interdisciplinary teamwork earlier in the curriculum and make it a core, assessed component of degree programs, large-scale modules are becoming more common, making it necessary to rethink this assumption . Why Structure Itself Needs Deeper Attention While there is substantial literature on teamwork pedagogy in small to medium-sized cohorts (80–100 students), the structural realities of delivering these projects in mega classes remain underexplored (Hernández-de-Menéndez et al., 2019; Van den Beemt et al., 2020). As Pauli et al. (2008) highlight, task disorganisation and poor group functioning often result from unclear roles and insufficient planning—issues that are magnified when direct oversight from educators is no longer feasible. In these contexts, structure is not simply a logistical necessity; it becomes a pedagogical tool. Recent work by Joyner et al. (2023) emphasises that large-scale courses require intentional alignment across learning outcomes, assessments, and instructional practices. In interdisciplinary team projects, this alignment is particularly critical, as students must not only complete technical tasks but also navigate interpersonal and disciplinary differences. This paper forms part of a broader research series examining the design and facilitation of interdisciplinary teamwork in mega classes. The first paper explored the evolving role of the educator at scale, highlighting the shift from traditional teaching to facilitation and systems design (Truscott & Smith, 2024). The second paper connected Kimpton and Maynard’s framework for successful team projects with the practical realities of teaching at mega scale, identifying key thematic areas (Kimpton & Maynard, 2023; Smith & Truscott, 2025a). The third paper provided a practical overview of module coordination and delivery, focusing on strategies for balancing automation with student-centred practices (Smith & Truscott, 2025b). This fourth paper focuses specifically on the structural design elements that underpin successful learning experiences in extremely large cohorts. It foregrounds the intentional frameworks—both pedagogical and logistical—that make teamwork, autonomy, and consistency possible at scale. In addressing a gap in the literature on how teamwork pedagogy translates into extremely large-scale environments, the authors used Kimpton and Maynard’s practitioner-focused review of teamwork pedagogy to reflect on how scale changes the demands on instructional design. From this reflection, three intersecting themes emerged as critical for practitioners: managing resources, enabling effective communication, and designing intentional structure. These themes are not discrete; rather, they require the educator to take a complex, integrated approach to course design and facilitation. Large-scale courses require intentional structuring to ensure coherence and alignment across learning outcomes, assessments, and instructional strategies. Research indicates that at-scale courses benefit from a more deliberate design process involving content experts, learning designers, and platform engineers, which improves course organisation and accessibility (Joyner et al., 2023). This structured approach becomes even more critical in interdisciplinary team projects, where coordination across disciplines and alignment with project goals are essential for student success. This paper unpacks one of these themes—structure—to examine how pedagogical and logistical frameworks can support learning outcomes, promote consistency across diverse teams, and compensate for the limited direct oversight typical in mega classes. It explores how structured design elements such as scaffolding tools, mentorship models, and embedded reflective prompts can help students navigate teamwork independently while maintaining alignment with course objectives. A forthcoming paper (Smith & Truscott, 2025c) will extend the series by exploring communication strategies in this context. 2 CONTEXT AND METHODS 2.1 Context This paper examines two mega-scale interdisciplinary engineering modules—one in the Global North and one in the Global South—that rely on structured design to support student learning at scale. At University College London, the Engineering Challenges module engages 900–1,000 first-year students from seven departments in a structured, interdisciplinary team project. At the University of Pretoria, the Joint Community Project (JCP) module involves 1,650 second-year students across 18 programs in service-learning projects embedded in community contexts. Despite institutional and contextual differences, both modules use intentional structural mechanisms—including vertically or horizontally integrated mentorship, shared scaffolding, and team-based teaching—to support student autonomy, facilitate interdisciplinary learning, and manage logistical complexity. The scale of these modules demands a shift from traditional instruction to systems-level coordination and embedded support, ensuring consistency, meaningful engagement, and equitable access to development opportunities. These cases provide insights into how structural design can enable effective interdisciplinary teamwork in extremely large engineering cohorts. 2.2 Methods This study builds on prior research (Truscott & Smith, 2024; Smith & Truscott, 2025a and b). Using autoethnographic writing (Ellis & Bochner, 1999; Choi, 2012) and structured shared narrative inquiry (Chase, 2005) to explore interdisciplinary teamwork in mega classes, enabled us to question assumptions, surface hidden patterns and iteratively refine themes. The facilitators engaged in iterative reflection and documentation, enriched by first-hand observations from institutional visits. These reflections focused on the intentional design of structural elements that support learning at scale. Comparative analysis of study guides, assessment rubrics, and coordination tools was then brought in, to highlight alignment between structure and learning outcomes. Emerging themes were synthesised into practical strategies for managing complexity and promoting student autonomy in large-scale interdisciplinary modules. 3 RESULTS AND INSIGHTS Teaching teamwork in a mega class environment requires a structured approach that reduces reliance on direct lecturer-student engagement. With over 900 students, structure becomes a tool that can be used to guide and support staff and students through the project as well as provide a space for independent learning and development of personal skills such as resilience. The biggest challenge is ensuring that students know what they need to do, how to do it, and why it matters, without requiring constant oversight from teaching staff. 3.1 Observations from UCL Structure is essential to running Engineering Challenges successfully. This is due not only to the large number of students but also to the size and complexity of the teaching team. Engineering Challenges is integrated into the degree programmes of seven departments within UCL's Engineering Faculty. As a result, parts of the module must be tailored to align with each department’s curriculum. To support this, each department assigns 1-3 staff members (departmental leads), who adapt the content to suit their discipline and complement the broader degree programme. While this tailored approach ensures relevance within each department, it also means that most of the teaching team (around 15–20 academic staff) focus on their departmental areas. Consequently, the module lead is the only team member with a dedicated focus on the entire module. For the interdisciplinary team project, departments are paired up and teaching teams forms from those assigned by the departments. The structure of Engineering Challenges is designed to guide and support both students and staff throughout the module. Working alongside the module’s communication strategies, this structure signals the next stage of the project, reducing the need for constant reminders or nudges from the module lead. For example, the marks split for the two parts of Engineering Challenges (30%/70%) reflect the time split for the two parts (3 weeks/7 weeks). Where possible, we have simplified the structure to ensure each step is clear and part of a logical progression. However, given the module’s scale and complexity, only so much streamlining is possible. Since much of the teaching team has a narrow departmental focus, they may not see the full complexity of the module. While this simplifies delivery to some extent, it can also mean decisions are made without considering the module’s broader structure, occasionally overlooking why certain actions are restricted. The module lead’s role is to maintain this balance—allowing departmental leads the flexibility to tailor content while ensuring the module remains coherent. The module’s structure also models real-world project dynamics and teamwork. A core aim of Engineering Challenges is to teach students how projects function and how to collaborate effectively, particularly in interdisciplinary contexts. Structural decisions have been designed to mimic these processes, providing a clear pathway for students—particularly those with little prior experience in project work or teamwork. For example, we have added a requirement for teams to meet once a week and discuss the progress of the project to mimic the information flow needed in teamwork. Structure works in tangent/conjunction with in-classroom teaching and assessment to communicate this information in as many ways as possible. Assessments are strategically placed at key project milestones, mirroring typical decision points or client interactions. For example, before teams enter the lab to conduct experiments, they must present their plan as a group. This models the need for teams to make decisions about resource use (e.g., lab time) and reflects common client-facing milestones. The choice of assessment method is crucial to the smooth running of a module of this size, with logistics playing a key role in assessment design. For example, providing timely feedback on written work at scale is a significant challenge. This challenge is amplified during mid-module assessments, where marking hundreds of reports alongside teaching, answering emails, and other academic duties is only possible if you don’t need to sleep. Using in-person assessments mid-module has allowed us to avoid this problem. Using structure in these ways has also reduced the need for routine staff-student interactions about next steps or basic tasks. This frees staff to focus on more complex issues, such as team mediation or support for students with particular needs. By encouraging students to manage straightforward questions or challenges independently, the structure promotes problem-solving skills and resilience, while addressing the challenge of limited staff time per student. The module lead’s job becomes one of curation of staff and student experiences. 3.2 Observations from JCP The key to running this kind of module successfully lies in automating support systems, structuring peer engagement, and developing a mentorship model that ensures students receive the guidance they need without overwhelming faculty resources. The most immediate challenge in a course of this size is managing communication and expectations. Even the smallest misalignment in instructions can result in hundreds of emails, making it impossible for the teaching team to respond meaningfully. To address this, a vertically integrated mentorship structure has been established. Rather than relying on direct faculty intervention, students are first encouraged to consult their teammates, then seek guidance from their assigned mentor, and only escalate issues when necessary. This structure mirrors a professional organizational model, where students are framed as junior faculty members responsible for managing their own projects, while mentors serve as senior management, providing strategic guidance without micromanaging. The teaching team, in turn, operates more like an executive board, focusing on high-level oversight rather than daily logistics. Another essential element of managing scale is the automation of student queries and information access. The JCP Online Platform plays a central role in this process, serving as a repository of structured FAQs, automated responses, and logistical guidelines that students must consult before submitting queries. By structuring communication in this way, the course minimizes unnecessary administrative burden, ensuring that faculty time is spent on critical learning interactions rather than repetitive clarifications. Ensuring that students engage effectively in team-based learning requires more than just placing them into groups. Intentional scaffolding is necessary to equip them with the skills and frameworks they need to navigate interdisciplinary collaboration. At the core of this structured approach is JCP Week, an intensive onboarding program designed to introduce students to the foundational concepts of teamwork, leadership, and professional engagement. Before JCP Week even begins, students are assigned to predetermined teams of four to five members. These assignments are made using an automated system that mimics real-world project environments, where team selection is often beyond an individual’s control. Once grouped, students engage in structured mentor-led team introductions, where they explore their backgrounds, motivations, and expectations for the course. This initial engagement sets the stage for deeper collaboration by allowing team members to identify commonalities, strengths, and potential areas of conflict. To formalize team expectations, students then participate in a group contract session, where they collectively define their shared values, establish meeting schedules, and agree on conflict resolution strategies. This process not only encourages accountability but also ensures that potential issues—such as uneven participation or missed deadlines—are addressed proactively rather than reactively. In parallel, students begin work on their project proposals, where they assess the feasibility of their assigned project, determine necessary resources, and anticipate challenges they may face when working with their community partner. These proposals are guided by structured prompts that ensure students are considering key logistical and ethical dimensions of their work. Through this process, students begin shifting from passive learners to active problem-solvers, a transition that is critical in Mega scale teamwork modules. Beyond structural preparation, students also undergo targeted professional skills training during JCP Week. Enneagram-based teamwork analysis introduces them to their own communication styles and potential friction points within their teams, while conflict management and leadership training immerse them in scenario-based exercises that force them to make real-time decisions under pressure. These activities ensure that students do not enter their project work unprepared but instead have a framework for navigating interpersonal challenges effectively. Assessment in a Mega Class must be both meaningful and scalable. With such a large number of students, traditional feedback models become unsustainable, requiring an approach that is structured, automated where possible, and designed to guide learning rather than merely evaluate performance. The assessment process in JCP is built around layered evaluation methods that integrate self-assessment, peer feedback, mentor input, and community partner evaluations. 4 CONCLUSIONS AND IMPLICATIONS Teaching interdisciplinary teamwork in mega classes—modules with 900+ students—demands a fundamental reimagining of the educator’s role. As discussed in Truscott and Smith (2024), the educator no longer functions primarily as a direct content deliverer or interpersonal coach, but instead acts as a designer, curator, and facilitator of structured learning environments. These environments must not only manage scale but also preserve pedagogical depth, enabling students to develop critical teamwork skills with limited access to one-on-one instruction. In this context, structure becomes a pedagogical tool rather than just an organisational one. As shown in Smith & Truscott (2025a), robust structural elements compensate for the lack of direct instructor oversight by providing scaffolding that enables student autonomy. Structure supports the delivery of consistent experiences across large and diverse student cohorts, guiding learners through clearly staged tasks, expectations, and assessments. At its best, this structured design does not only reduce cognitive and administrative load for staff—it also invites students into a more active, self-regulated, and reflective learning process. In both JCP and Engineering Challenges, the structure operates across three interwoven domains: assessment, pedagogy, and logistics. Each of these domains involves decisions that serve dual purposes: simplifying complex operations at scale, while simultaneously offering meaningful learning experiences. In assessment, this means designing deliverables that act as learning milestones rather than only grading points. For example, live feedback assessments during Engineering Challenges or the structured proposal process in JCP serve both to guide team development and to reinforce project-based thinking. Pedagogically, structure takes the form of intentional learning design, where scenario-based activities and reflective prompts develop skills like conflict resolution, feedback literacy, and ethical decisionmaking. Logistically, structure includes automated platforms, mentorship systems, and communication protocols that triage information and maintain alignment across sprawling teaching teams and student groups. This structured approach is not static; it is shaped by the educator’s values and strategic decision-making. The educator-as-designer must constantly balance competing demands—simplicity vs. depth, automation vs. connection, control vs. student agency. Designing for scale forces difficult choices. For instance, in JCP, the desire to provide personalised feedback had to be tempered by the realities of managing 1,600 reflections. Mentorship structures and online platforms were therefore introduced to redirect routine questions, facilitate peer support, and ensure that meaningful feedback happened at critical stages, even if not continuously. In Engineering Challenges, structural constraints ensured departmental leads could tailor project work without undermining the overarching coherence of the module, but this required acceptance of a level of inconsistency across student experiences. In both contexts, clarity of expectation and intention proved critical—not only to reduce administrative overload, but also to ensure students understood not just what they were doing, but why it mattered. Crucially, structure also functions as a space for students to develop independence and resilience. At this scale, it is no longer feasible for staff to step in every time a team experiences friction. Instead, structures must be built to model and prompt professional behaviours. Whether it’s through structured team contracts, regular check-ins, peer feedback, or milestone-driven assessments, the aim is to shift students from passive participants to active collaborators who can navigate ambiguity and interpersonal challenges. Structure thus becomes a silent teacher— supporting, nudging, and shaping student development in the absence of constant staff intervention. However, there are limitations and risks. Poorly designed structure can become rigid, depersonalised, or overwhelming. If too complex, it burdens staff and confuses students. If too loose, it invites inconsistency and disengagement. Educators must also grapple with students’ prior expectations—many arrive socialised into hierarchical, compliance-based systems of assessment and struggle to engage with autonomy-focused models. In both JCP and Engineering Challenges, mentors/postgraduate teaching assistants initially defaulted to controlling behaviours in assessments, until trained to embrace more facilitative roles. This illustrates the need for meta-structure: not just designing student-facing frameworks, but also scaffolding the roles of mentors and staff in a way that aligns with the learning ethos. From these experiences, several guiding principles for designing effective structure in Mega Classes emerge: • Start with clarity of purpose: Every structural element should serve a learning outcome, not just an administrative need. • Design for independence, not dependence: Structure should scaffold student self-direction and reduce bottlenecks, not create new ones. • Simplify complexity strategically: Aim for consistency without rigidity; reduce the variables that can derail the system. • Automate where possible, humanise where needed: Use platforms and protocols to streamline, but preserve moments for personal engagement. • Embed feedback within the process: Design assessments that give learning value through their completion—not only through staff feedback. • Mentor the mentors: When peer-led support structures are used, invest in their training and align them with the pedagogy.