Teaching and Learning Strategies

Abstract

There is not one type of curriculum that fits all institutions nor all students. The engineering programs are developed based on various learning principles and cultural conditions and represent different learning ecosystems. This chapter and chapter 7 address different learning principles which can be taken into considerations when developing a student centred curriculum has to be developed. We are emphasizing exemplarity, variation, and reflection as basic learning and design principles, followed by types of more active learning strategies for lectures, inquiry-based, studios, problem-based, and project-based approaches to learning, and we emphasize teamwork and diversity of thinking as core elements to address complex socio-technical systems.

In order to develop a curriculum, there is not one size that fits all institutions nor all students. The engineering programs are within various universities and various cultures and represent different learning ecosystems. Even at one institution, one size does not fit all. Students are diverse, with diverse backgrounds and diverse individual learning styles. Therefore, there is a need for emphasizing exemplarity, variation, and reflection in engineering education practice, and in the following, we argue for the importance, but also the challenge, of leaving a reductionist approach to learning and moving to a more open and dialectic approach, where societal needs and end-user requirements are intertwined with aspects of learning. To address these new challenges, we propose more active learning strategies for lectures, inquiry-based, problem-based, and project-based approaches to learning, and we emphasize teamwork and diversity of thinking as core elements to address complex socio-technical systems.

1 Exemplarity

One of the biggest concerns for moving to more active learning systems is that students do ‘lose important technical scientific learning outcomes’ when they are working on projects. It is a concern that should be addressed as there will be a change in learning outcomes when changing the teaching and learning methodologies. In the first chapters of the book, the argument for new learning outcomes, such as understanding complex problems and being able to participate in complex problem solving, will involve more active learning, inquiry-based learning, and team-based projects. That will involve new learning outcomes responding to the contemporary challenges.

In Chap. 5, new approaches to learning framed by social constructivism were presented together with new learning principles. One of these principles is exemplarity. Exemplarity is a well-known concept in German educational literature and there are several understandings. Negt used the concept to say that the problems students are working on should be exemplary to the societal problems (Negt, 1971; Servant‐Miklos et al., 2019). This was a way to indicate that science should reflect the societal problems and that the general education should prepare students to become critical citizens. Another understanding is formulated by Wagenschein who emphasizes that the learning outcomes should be exemplary to the overall learning outcomes formulated in the curriculum (Korsgaard, 2019; Wagenschein, 2000). This is a question of selecting the content/scientific knowledge that is exemplary and aligned to overall learning outcomes. If students are working on problems, the methodologies they are using in analyzing the problem and solving the problem should be exemplary to the overall learning outcomes.

For example, problem and project based (PBL) curricula have variations in the intended learning outcomes. Lectures give an overview and a preliminary understanding, whereas projects mostly have learning outcomes at the level of analyses and syntheses, which are higher levels in the Bloom taxonomy (Bloom, 1956). This is an extremely important principle for PBL curriculum design, to allow space and freedom for the students’ choice of problems, to practice a co-constructed curriculum. It will require that the learning outcomes are formulated in more general terms of methods and methodologies. This does not mean that students learn less, they learn methodologies and methods which they can apply on new problems.

However, many colleagues might question whether student learn particular learning outcomes from the textbook. There are many responses to this. One is to say that when students are more active, they use more time for learning and will not lose anything. Research indicates that the graduates from PBL curricula value the projects and cases more as they remember what they have learned (Kolmos et al., 2020b; Schmidt et al., 2009). Another response is that when students have learned the knowledge relevant to analyzing and solving one problem, they have the potential for transferring or transforming their knowledge to a new problem (Bertel et al., 2022).

There is no doubt that students remember the learning from the projects more than the learning they have from taught courses (Kolmos et al., 2020b). The reason is, of course, that the students are actively working with the content more than in the taught courses. So, the deep learning in the projects should, on the one side, be exemplary for the overall learning outcomes in the curriculum, and on the other side, students should have possibilities of influencing the direction of learning.

2 Variation in Learning

Variation is a core principle in evolution and an important force as it allows natural selection within specific species. As humans, we all belong to the same species; however, we all look different because of differences in genes. We talk about genetic variations which is an embedded understanding of development. But rarely do we talk about variation in students’ learning, except for the different learning style tests that have been applied to a certain degree for creating awareness of individual preferences.

Variation in learning is a concept derived from phenomenography, which has added inspiration to many educational and instructional learning methods. Variation theory assumes that individuals understand and reflect on the world from their own perspectives. The learning takes place when students are ‘capable of being simultaneously and focally aware of other aspects or more aspects of a phenomenon’ (Marton & Booth, 1997, p. 142).

The object of learning is important as learning is always about something. Learning is the capability to do something with specific content and in some contexts. Learning is interpreted as a change in the way something is done, seen, experienced or understood, and education is aimed at developing learners’ abilities to handle various situations, to analyze and solve different problems, and to act effectively according to one’s purposes and the conditions of the situation (Kullberg et al., 2017; Marton, 2006).

Variation influences learning in many ways. For the individual learner, it is important to be aware of ways to create a varied approach to given content. In teams, it will be important for individual learners to bring in their awareness of varied approaches and get into dialogues of understanding. For institutions, it is important to create a curriculum that allows students to experience and learn content knowledge in different ways. This will mean a variation in teaching and learning methods for students to experience different ways of learning and collaborating (Fraser et al., 2006; Linder & Fraser, 2006).

This is in line with the statement that there is not one correct way for engineering education to respond to the integration of complexity—there are multiple ways for the learner to form a comprehensive understanding of a phenomenon. With a variety of learning situations, the learner could challenge the understanding and learning from one situation to another and from applying one method to another. Variation in the teaching and learning methodologies is important as students experience learning of the disciplines in various ways.

Most curricula are organized as a series of courses (subjects, units) of which a certain percentage will be compulsory and the rest elective courses. The teaching within each of these course blocks is also very much alike, with textbooks, assignments, and perhaps some groupwork and assessment. Lately, the flipped classroom has swapped the lecturing part and the activity part so that the activity/assignment part takes place in the classroom instead of the lecturing part (Reidsema et al., 2017).

The variation in most curricula is to be found in the content more than the learning methodologies and we argue for more variation in learning methodologies within student-centered learning and even within the application of projects in education. Students learn from the way they are learning either at an aware or unaware level. If they have only experienced individual learning in education, they will have a harder time learning to collaborate in their work. If they have never tried to analyze problems, they will have a harder time identifying and dissecting a problem or a user need.

Even if academics are aware of the dynamics in learning, the norm of the curriculum is mostly organized in replicable stereotypes of taught courses, normally embracing oral lectures, individual assignments, tests, and assessments. We are, therefore, facing a discrepancy between how we think about learning and the curriculum systems, which are very much alike from one course/discipline to another and part of a cultural inheritance in academia transferred through experiences from one generation of teachers to the next. The way we have been taught and the way we have experienced learning form our basic beliefs in learning. These are difficult to disrupt.

However, if we want to educate engineers who can navigate and cope with complex socio-technological systems, there must be a disruption in a way that enables students to move outside the classroom and combine theory and practice.

3 Variation Goes with Reflection

Variation in learning goes hand in hand with reflection. If we just apply more variation without comparison and reflection, students might get more confused. Students need to learn to compare their learning experiences to make sense of it and develop their inner understanding. This counts for all knowledge and competencies. In the learning of theories, students might compare concepts. In the learning of methodologies for analyzing problems, students might compare various methodologies to choose the one that will be aligned with the problem. In learning generic or meta-competencies, the students might compare their experiences of, e.g., project management methods and knowledge sharing in the teams to learn how to contribute to efficient teamwork (see the next chapter).

The experiential learning process in which the learner has reflected on their own experiences will lead to a meaningful inner understanding—but the language, the concepts, and the articulation are dependent on already existing language. Therefore, engineering students also must gain a conceptual understanding of learning and be able to distinguish between different types of experiment and reflection processes. This is important as if we only let the students experience and compare experiences, they might stay as novices or take a very long time to learn core scientific concepts. It is a dialectic process between practice and theory and a type of experiential learning (Kolb, 1984).

Schön characterizes three different types of experiments and reflection processes (Schön, 1983):

• (1) The explorative experiment, which is very much the trial-and-error process.

• (2) The move-testing experiment, whose purpose is to test and compare experiences.

• (3) The hypothesis-testing experiment, which is much more theoretically founded.

The explorative experiment implies a type of common-sense reflection, where the primary aim is to test for establishing awareness. Move-testing takes its point of departure in intended action and thus implies a comparative reflection. The hypothesis-testing experiment also implies generalization of experiences and conceptualization, because experiences must be analyzed before new actions are taken (Kolmos et al., 2004).

Taking the urgency of the grand challenges of our time into consideration, explorative experiments are not sufficient—but it is also important to stress that they cannot be left out of the decision-making process, to increase the pace of innovation. More likely, it is an iterative and agile process between different types of active experimentations that are needed, and therefore, students should not only be capable of conducting different experiments, but they should also be able to adapt the overall experimental design to the situation at hand.

But reflection before, in and on a situation raises the question of what students are meant to reflect on. Argyris and Schön distinguish between two types of reflection depending on the attention given to the reflection process: single- and double-loop learning (Argyris & Schön, 1997). Single-loop learning concerns reflection on activities in accordance with established rules and procedures, and the question is whether we did things right and what we need to do to correct our actions for better alignment with the rules and procedures. Double-loop learning considers a deeper reflection concerning the rules and procedures. The questions are much more about whether we are doing the right things, or whether they could be done in a better way. Do the rules and procedures need to be revised?

Inspired by Argyris and Schön, a conceptualization of ‘triple-loop learning’ has developed (Tosey et al., 2012). Triple-loop learning contains a critical reflection on the underlying assumptions leading to the governing values. More philosophical questions concerning how we decide what is right and whether other values would suggest more radical innovations come into play. If we accept the argument that critical thinking and sense-making are critical for future engineers, even double-loop learning is insufficient, and students must be assisted to reflect on more fundamental and inherited values.

Furthermore, the on-reflection processes can, by analyzing a longer series of similar experiences, be used for more strategic development of competencies. According to variation theory, there should be a sameness and difference in the situations from which learning is transferred from one situation to the next. The sameness makes it possible to recognize patterns in the contexts or methodologies and to allow for bringing experiences and knowledge from one situation to another. The difference gives the opportunity to advance learning and might guide the learner to alter understandings and combine different learning tracks; otherwise, learning will remain on the same track. For the individual learner, the ability to reflect and create transformation of, and progression in, learning is a crucial part of creating lifelong learning paths. We will come back to this transformation process and the generic competencies needed in this regard in the following chapter.

4 New Learning Environments

During the last 15 years, there has been a trend toward more flipped classrooms, meaning that students read/watch material before a class and are more active in the classroom when they meet with their classmates and during lectures. The tradition for university courses is a lecturing part combined with some exercises where students apply the concepts. There is no doubt that even if more active learning methodologies are applied, the role of the instructor is essential in framing, presenting, guiding, responding, and knowing in a new type of reflective and dialogue-based classroom.

Open spaces with appropriate tools have been advocated as enablers for such progressive pedagogies. In particular, the concept of using the classrooms for reflections, collaborative work, discussions, and activities opens the possibilities to ask the learners to study and review the content of their class ahead of the convening time of class (Reidsema et al., 2017). At the class, they devise questions and present their reflections. The instructor provides explanations and examples and generates further discussions. The instructor also gives opportunities for peers to comment and answer questions.

The class members become accustomed to teamwork and support each other. This pedagogy was well articulated by Nechkina (1984) who said ‘…let pupils extract new things from autonomous reading of a textbook, which has been created accordingly. Allow them to consider it, then discuss it with their teacher at school and come to a united conclusion’.

Thus, the concept of the flipped classroom was born. The study before the class time includes several forms of learning such as oral, visual, and listening (Mazur, 1997). This requires that the instructor makes detailed preparation for the work outside the classroom to make sure that there are well-defined steps to reach a common learning that can be used to execute projects and joint exercises at the class time. In addition, questions need to be designed to test the students’ abilities to define and critique their hypothesis and find solutions in steps of gradual difficulty.

During COVID-19 times, online learning and working became popular and enabled flipped classrooms and made them more accessible. Students got used to learning and discussing topics on the screen. Students showed leadership and agency to learn through their own research and investigations. One may note that there are more courses that already built on discussions and reflections and thus flipped classrooms are readily practiced. Flipped classrooms and peer-to-peer learning have been practiced for different types of class levels and materials, including topics in science and engineering, and it has shown some good positive outcomes (Miller et al., 2018). One of these outcomes is breaking the separation between the learner and instructor and better sociability.

Some students prefer to pick topics that are new or that seem to be a combination of new and old topics. The probability of getting involved in a new topic is low, while picking a familiar topic seems to be much higher. Trying to understand and solve problems that students have never experienced before requires more than lecturing. Since social challenges are different among different societies, students tend to get involved in external activities through volunteering in local communities, for example, to satisfy their desire in addressing social challenges and help in improving the society.

COVID-19 is an example that students loved working on because they experienced it and lived the challenge in their everyday life. Experience is a very important factor in evolving students, and it offers them the mindset to weighing potential solutions to find the right one. By teaching students some theoretical concepts, they can learn how to solve problems and map challenges, but the solutions might not be realistic or a good fit. Fitting a solution in a system requires a solid understanding of the system and its relationship to other systems, which comes from experience and involvement.

5 Inquiry-Based Approaches to Learning

Accepting that an increased focus on end-user requirements and societal needs should be a priority in technological innovation, as argued in Chap. 4, also implies acceptance of the importance of contextual learning in engineering education. From a systems approach, the core of contextual learning is being able to point to the most relevant contextual aspects to consider as well as the most relevant relations in and across established boundaries. Furthermore, the ability to situate the knowing, acting, and being in the T-shaped graduate (Chap. 4) is also a competency of its own. The educational design might therefore be quite different from more traditional designs grounded in an academic disciplinary approach (the I-shaped graduate) (Heikkinen, 2018). In the following, we will argue that both inquiry, studio, and problem-based learning approaches can offer considerable support to change engineering education in a way that supports T-shaped professionals for the future.

Chinn et al. (2021) define inquiry as ‘finding things out’ under the following six conditions, stating that inquiry is an act where:

• (1) One is, in fact, gaining new ideas or new knowledge.

• (2) Active work is involved in thinking through and working conclusions out.

• (3) Considerations (evidence) are made to reason through to a conclusion.

• (4) Those involved have the authority to express their own interpretations, suggest new ways to approach areas of concern and reach their own conclusions (epistemic agency).

• (5) There is some degree of complexity in the reasoning involved.

• (6) Engagement moves beyond the individual or team to a broader community.

Therefore, inquiry implies a creative, collaborative, and active process that combines theory and practice for complex reasoning and epistemic agency. This approach to knowledge construction can be seen in scientific practices and that inquiry-based learning (IBL) is more often seen as practice within science education than within engineering education (Kolmos et al., 2021). In engineering education, IBL is more embedded in educational models that reflect professional practice (such as problem-based learning models) or parts of professional practice (such as design-based learning models).

In any case, inquiry-based approaches stand in contrast to approaches that are mostly concerned with corroborating dominant understandings or reconstructing what has previously been constructed. Inquiry can be seen a trajectory for transformative learning. Asking questions about a situation and seeking information as and when required becomes central, not only to knowing the why and what but also to creating new ideas of what could be (Holgaard et al., 2017). These ideas are evident in a situation where the dominant technological trajectory is far from reaching the ambition of a circular economy.

In engineering education, there is, however, no universal educational model to deal with the challenges of rethinking our society. It is really a question of how we can bring complexity into education in ways where students can learn to handle complexity, both in terms of analyzing and understanding the dynamics in complex problems, but also to find solutions. Learning can become a transformative process that moves beyond the individual, with an ambition to transform surrounding communities as well.

Transformations of society call for the part of inquiry focusing on new ideas and new knowledge, which underlines creativity as a central component in fostering change in technological systems in society. Csikszentmihalyi (1988) offers a systems approach to creativity and argues that dynamics in social institutions and cultural symbols must be in place in a system to foster creativity. Thereby, creativity builds on contextual knowledge. Cropley notes, it is a paradox that even though few would disagree that creativity is an essential component in technological innovation, ‘many leaders, managers, professional practitioners, and educators are either apathetic to creativity or uncertain of how to exploit it in practice’ (Cropley, 2016, p. 156).

From a social-constructivist perspective, Sawyer argued that creativity is basically a collaborative process (Sawyer, 2008), which is aligned with the understanding of technological innovation as a distributed process that includes several actors. It is a dialectic process between individual agency and collaboration in the inquiry process, between constructing and co-constructing new knowledge, new technologies, and new systems that include different technological trajectories. The collaborative nature of inquiry is important, as complexity and system approaches require organizations and interdisciplinary teams of engineers to work together. It is, therefore, an ever-increasing requirement for engineering education to educate engineers who can work, collaboratively as well as individually, and to participate in dynamic and agile work processes. This dynamicity calls for active learning to capture the nature of technological systems—as noted above, inquiry requires active work.

In a (re)introduction of active learning in Engineering Education, Lima et al. (2017) propose that ‘Active learning is learning which engages and challenges students using real-life and imaginary situations where students engage in such higher-order thinking tasks as analysis, synthesis, and evaluation. In active learning environments students are engaged in meaning-making inquiry, action, imagination, invention, interaction, hypothesizing and personal reflection’ (page 3).

In a study of the impact of active learning, Freeman et al. (2014) performed a meta-analysis of 225 studies, which reported data on examination scores or failure rates when comparing students’ performances in undergraduate science, technology, engineering, and mathematics courses, under traditional lecturing versus active learning. The study showed that, on average, students’ performances in examinations and concept inventories increased under active learning, whereas the odds ratio for failing decreased. Heterogeneity analysis indicated that these results hold across STEM disciplines. These results, together with the fact that international organizations like UNESCO stress the need for many more STEM experts in the decades to come, provide strong arguments for active learning methodologies in STEM education.

In a recent MIT report, student-centered learning models like problem- and project-based learning (PBL) were identified as among the core responses to contemporary challenges, leading to engineering institutions like MIT, Stanford, Harvard, Purdue, Chalmers, Delft, Twente, Aalborg, and many more institutions implementing PBL in various ways in their existing curricula (Graham, 2012, 2018). A recent review of PBL in engineering education, however, indicates that the most common application of projects is within existing discipline courses rather than across courses, or at curriculum level (Chen et al., 2021).

It is a trend that more and more student-centered learning methodologies are applied in courses, and a few universities have also reorganized the curriculum to become more student-centered and project-based. Project-based learning has become popular, and there are design courses with project work as the main learning component. However, more systemic institutional changes to problem- and project-based learning imply a more fundamental change where real-life problems become the starting point and the navigator for learning. Such approaches recognize that understanding a problem and the way to approach different types of problems becomes of central concern in fostering transformative learning and learning for transformation of societies.

6 Variation in Problem-Based and Project-Based Approaches to Learning

Fundamentally, PBL implies that the problem is the starting point for the learning process, with emphasis on the multidisciplinary approaches. There are different approaches to conceptualizing the so-called problem.

The Cynefin framework (Snowden & Boone, 2007) provides a way of distinguishing various problems by characterizing problems as simple, complicated, complex, or chaotic. This classification was also discussed in Chap. 2 of this book. While simple problems can be handled with engineering fundamentals, complicated problems require expert behavior as there are multiple right answers.

For complex problems, the problem itself is not well defined. In fact, many of these types of problems relate to what Rittel and Webber called ‘wicked problems’, as there is no definitive formulation of the problem, and therefore, the solution space is totally open (Hadgraft & Kolmos, 2020; Rittel & Webber, 1974).

While complex problems call for a problem analysis, taking into consideration the context in which the problem exists, chaotic problems are characterized by being situated in a context that is so unstable that the boundaries between problem and context become blurred. Chaos management is a related concept as the context is highly unpredictable. Chaotic problems typically emerge from disasters in terms of human loss, as in the case of the COVID-19 pandemic.

Jonassen describes characteristics of problems depending on their structuredness, context, complexity, dynamicity, and the domain specificity of the problem (Jonassen, 2011). For each of the characteristics, there is a variation from the very structured to the ill-structured, from practical problems closely interrelated with real-life situations to abstract theoretical problems related to still unknown contexts, from simple to chaotic problems as in the Cynefin framework, from stable to fluctuating problems, and from disciplinary to interdisciplinary problems. The same problem can of course contain several of these characteristics, which furthermore increases the complexity of characterizing the problem.

It is an iterative process of identifying, analyzing, and formulating the problem which is consistent with the design process (Holgaard et al., 2017). This notion emphasizes that in a PBL environment, which includes real-life complex problems, students not only need to learn to solve problems, but they also need to learn how to identify them. A problem can take its starting point in an unsatisfactory situation—a challenge, a lack of attention to a yet unexplored potential, or an uncertainty about the actual challenges and potentials embedded in each situation. These three starting points form a so-called problem triangle (Fig. 6.1). The interrelations in Fig. 6.1 illustrate that regardless of the starting point in the triangle, a problem design process will at some point touch on all three dimensions.

Fig. 6.1

Problem triangle

Based on this work, Holgaard et al. (2020) presented a process of managing the problem design process in an entrepreneurial PBL environment. In an entrepreneurial setting, an initiating idea is the starting point for stating and initiating the problem as a discrepancy between an actual state and the vision embedded in the idea. The problem thereby is seen as the gap between the so-called pains in the actual state and the potential ‘gains’ if the idea is realized in the given context (see Fig. 6.2).

Fig. 6.2

Problem triangle in an entrepreneurial context

Understanding the problem as such is then seen as one dimension, whereas the validation of the idea to create value in a business context is seen as another. For other problems, which might move beyond a business context, the validated potential can be translated to other institutional framings, e.g., research or governmental institutions.

The overall idea, however, is that a problem is a discrepancy between what is and what could be, and the ‘what could be’ inevitably is related to a value proposition. Foresight, which is described in Chap. 2, is a way to reflect the past, the present and the future. A problem in this view is a social construction and, thereby, a carrier of the values embedded in the practices, institutions, and discourses of the co-constructors. This view underlines the importance of contextual knowledge as emphasized in the previous sections. It also explains the different perspectives expressed by stakeholders about the given situation or the proposed solutions.

In any case, bringing the problem into the classroom will be important—problems are the core of the learning process, and as students are the main players in solving these problems, it is important that they get the opportunity to identify, analyze, and solve problems. It might be hard to solve complex problems in education and the students might not be able to do this, but they will be able to learn how their narrower technical solution will relate to the complexity of real problems. We therefore argue that problem design should be an integral part of engineering education.

Real-life problems will not show themselves in forms that are easily accessible, and a systematic approach is therefore needed. There are different approaches to analyzing a problem, but three approaches seem repeatedly emphasized:

• (1) Overview and structure: With increasing complexity of a problem, the need for an overview of the problem field increases and the same goes for the structure of problem analysis. There are many ways to create an overview of a problem field (e.g., by mapping) and many ways to structure a problem analysis, e.g., using different models to make iterations. Furthermore, a selection of different theoretical lenses from quite different knowledge domains and different contextual layers is typically needed to focus the analysis (e.g., psychological, social, organizational, environmental, economic, political, and cultural lenses). Systems thinking, as described in Chap. 2, is a critical part of this step.

• (2) Outlook and ownership: Complex problems cannot be defined from one perspective or from the view of one researcher, actor, or student. Therefore, it is crucial that students move away from their desks, out of the university, and get a sense of real-life problems, including the activities, actors, and resources, as well as the practices, institutions, and discourses, that constitute such problems. The question is also who ‘owns’ the problems? And among the owners, a critical question is how the students relate to the problems. The people dimension is central!

• (3) Problem delimitation and decision making: Complex problems need complex decisions on unsecured ground—this means that students must be able to select and argue for different strategies and perspectives in the problem analysis, and considerations given to the formulation of criteria for this selection and decision-making process. While structure and overview represent potential paths in the inquiry process, the problem delimitation presents the chosen path. This also relates to ownership—students embrace problems with their professional identity, however mature that might be.

The most important reason for having such a systematic approach in the problem design process, besides making sure the ‘right’ problem is solved, is that attention to the process will prevent students from falling into a random solution mode too early in the process. In popular terms, if you do not know where you are going, any bus will do. For engineering students that have not yet experienced the joy of making a real-life impact as an added value of learning, the ‘bus ride’ might seem the main trigger of motivation.

With a variation in problems, there will also be variation in project types. By combining the dimensions of problems, interdisciplinarity, and team size, Kolmos et al. (2021) distinguished four basic project types: the single-discipline project, the multiproject, the interdisciplinary project, and the megaproject (see Fig. 6.3). These are ideal types as it is often difficult to draw the boundaries in real-life projects. As such, these ideal types are therefore developed for analytical and conceptual purposes only, and there might be many more variations in practice. The model in Fig. 6.3 is further developed for the case in Chap. 10.

Fig. 6.3

Variation in project interdisciplinarity and complexity of teams (Kolmos et al., 2020a)

A single-discipline project, carried out in a single project group, is the most widely used both at course and curriculum level (Chen et al., 2021). Students from the same course or educational program apply knowledge, theories, and concepts to a specific discipline problem. An example could be a group of students applying control theory while developing an anti-sway system for a ship-to-shore crane.

An interdisciplinary project can also be carried out in one project group of a small size. The team preferably includes students from different programs, whereas a more modest approach is to let students from the same program work on an interdisciplinary problem in a collaborative T-shaped approach. In engineering projects, the preliminary problem analyses are often interdisciplinary in terms of academic scope, as students use, for example, sociological methods or participatory action research to identify user needs, allowing interdisciplinary knowledge to be integrated into a project with students from the same educational program. An example could be students of media technology designing a sustainable city game for primary schools, for which they need to have knowledge of learning in primary schools, sustainable cities, and game design.

A multiproject is less common and occurs in bigger courses or clusters of subdisciplinary courses. It is characterized by several project groups working on the same or complementary elements (work packages) within the same or very similar disciplines, e.g., in larger software development projects, or when groups work in parallel on the optimization of prototypes. These types of projects require a lot of coordination among the participating project teams to ensure the quality of the common product.

The last category is the megaproject which has recently been introduced into engineering education as a new project type. The general term ‘megaproject’ covers large, long-term, and highly complex interdisciplinary projects, normally characterized by a large economic investment in, and commitment to, the development and implementation of infrastructure projects in cities, logistics such as high-speed trains, aircraft and airports, space technologies and renewable energy systems, etc. Of course, it will not be possible to mirror these very large projects in education, but it will be possible to design projects that address complex problems across disciplines. It is important that students learn to deal with complex problems in education.

For students to learn how to handle real-life complex problems, they must move beyond disciplinary teams. Multiprojects will help students learn to work across teams but not across disciplines. Collaborating within disciplinary settings will most likely be easier than in interdisciplinary teams—and collaborating to solve complicated problems in an interdisciplinary setting is most likely easier than solving complex problems in megaproject constellations. Therefore, there must be concern about progression in the development of competencies for team-based work, which, even in a disciplinary setting, is far from simple.

7 Studio Learning

The studio pedagogy originates from architecture, and during the last 30 years, several other disciplines have applied this way of organizing learning (Bull & Whittle, 2014; Kamalipour et al., 2014; Schön, 1984). The way it is applied in engineering education, students seek solutions for human challenges. They may work individually or as a group, but normally, they are interacting in open discussions with their colleagues, stakeholders, and clients in the studio and outside it. Constant feedback and critique are key elements of a studio.

Naturally, the physical environment facilitates the interactions and discussions. Students may work on their prototypes and designs in the studio and work is therefore viewed by all participants, instructors, and critics. The infrastructure of the studio can include engineering workshop tools as well as computer simulation tools, projectors, and whiteboards.

The studio is an environment where students learn through peer-to-peer learning, discussions, and critique, and their learning is encouraged and reinforced. When they find obstacles or devise unsuitable solutions, they are coached and critiqued by their educators, peers, and stakeholders. With these steps, knowledge is increased and tested. In the steps of ‘reinforcement learning’, students need to learn how to predict ‘how good’ is their intermediate action. Thus, their work continues to be in the form of explorations. Feedback is used to update and improve the attained knowledge, so students learn to act and create to achieve a better design state while being critiqued by their peers and outside critics as shown in Fig. 6.4.

Fig. 6.4

Supervised learning states, with peers, critics, clients, stakeholders, and the public, participating in guiding the mitigation of a complex human challenge

We call this state a ‘supervised learning’ state. As the supervised learners receive their critiques and feedback, their ‘rewards’ are obtained without a delay. In a normal classroom structure, grades are received after several days, and sometimes weeks, from the time of their exams. Here the reward, i.e., the guidance, is immediate. Technically, the students are not receiving grades, rather, they receive advice or critique. This is a critical element of a studio, pun intended!

This process of supervised guidance encourages rapid iteration toward a solution and is useful when the ‘environment’ of the challenge is unknown. As the students are engaged in a complex challenge, and with clients, they become active change agents. Their function is to observe, empathize, and create solutions guided by the clients and other stakeholders, exploring, and innovating to create the best outcomes, through several iterations.

Knowing the environment of the challenge is a significant part of the challenge. The environment may not be in a static state, and it may have (almost certainly will have) stochastic elements. In fact, in these situations, every action the student undertakes may create a new state of the challenge. This is the nature of complex challenges. By replacing the teacher with a coach/critic and the grading process with critique and guidance, the burden of knowing is reduced, and the teacher also becomes a learner by engaging with the teams as they explore solutions.

There is a significant difference between the mental state of the teacher and the critic. A teacher tells the students what to do; they know the right answer. Whereas a critic tells the students how well they did, after they perform their actions. The critic never informs in advance what to do. The critic should not believe that they know the right answer; there is no right answer. A critic can provide rich feedback about the pros and cons of the proposed solution from their point of view, which should be clearly stated.

8 Team-Based Work is Critical to Produce Inclusive Designs

Applying projects in education also implies teamwork. A project is, by definition, an endeavor to meet or solve a specific task that is time limited and needs comprehensive resources. Very seldom do we think of projects as one-person efforts, but rather as work that is done collaboratively. Therefore, the team dimension is an important part of project approaches. Composition of teams can vary enormously and therefore students should also experience this variation.

There are many theoretical frameworks for understanding teams, and the point here is just to illustrate variables and conceptual understandings of teams, which the students should be allowed to experience. Students need some experiences before they understand the theoretical models and team variables, but the important part is that they are given the opportunity to experience the variations. The project organization not only depends on the type of problem addressed but also on the work orientations of the team members involved, the accessibility of knowledge providers, available resources, etc. In any case, all project teams should aim to become high-performance teams, where the learning process and the project are experienced as an integrated and beneficial process.

Katzenbach and Smith defined five types of teams to be distinguished from a working group. A working group consists of individuals who do not share common goals with some common purpose but where the individual goals dominate, e.g., a study group of students, each with their own individual goals, but each is willing to share knowledge with each other (see Fig. 6.5).

Fig. 6.5

Team alignment levels, based on Katzenbach and Smith (2015)

Although working groups exist within the boundaries of a problem-based learning environment (e.g., when students work in parallel to learn a specific skill), working groups are not sufficient for problem-based project work, as the problem constitutes a shared concern, the solution to the problem is a shared goal and the project work is a shared practice.

One team type, defined by Katzenbach and Smith (2015), can have even less performance impact than a planned working group. This type is a so-called pseudo-team, where the members should be working on a common goal, but they do not manage to get there. They are stuck in a storming phase, without really being capable of creating norms or rules, and if they do, they do not obey them.

These types of pseudo-teams are highly damaging for students’ motivation to work in teams and are, unfortunately, far too common. A typical pseudo-team practice is to let individuals work in a parallel mode without any interaction, where an important opportunity for peer learning is missed. Pseudo-teams can, unintentionally, be encouraged by choosing a problem that is too simple, which can easily be reduced to separate tasks that can easily be solved individually; consequently, students will miss an important opportunity for learning.

When team members have some shared experiences, they can start to align their knowledge and competencies. This is a kind of a potential team where team members try their best to norm and perform but without great success. The project report will reveal that the logic is missing and that they have not been able to build in a collective reflective process. Students will always believe that they are working in a real team, but as they fail to carry out formative evaluations along the way, the mistakes will most likely be revealed too late at the summative stage of the assessment. They have aligned parts of their project work, but still there are missing links.

One example is a student group trying to solve a rather complicated or complex problem through approaches used to solve simple problems—reducing the problem through questionable assumptions and independent work packages. Sometimes, students end up with missing pieces (as some group members did not do their part in the end) or pieces that do not fit together (as they have moved in different directions). But, in contrast to a pseudo-team, students might experience that they have, in fact, learned a lot from such mistakes. Disappointment with the solution provided will, however, most likely disturb the excitement of the learning outcome.

A next stage is when team members are aligning their expectations and have a common understanding and alignment of the goals. The team members collaborate with complementary competencies and are equally committed to the common goals and the project. This type of teamwork will most likely not be the students’ first experience. On the contrary, many students will have to experience more than one ‘potential team experience’ before they are able to be as flexible as a real team requires. This is also because student groups are seldom matched together to complement competencies as in a real-life setting, as the primary concern is learning outcomes and not project deliveries. This type is also characterized by integration of reflection and adaptation in the collaborative process. Meta-competencies are needed to be in a position where you can set or develop real teams in the future as one has to be able to change perspectives.

Such teams are needed to solve complex problems, which need a synergy of different competencies and even disciplines. However, even though we might accept that real teams can only be imitated in an educational setting, the question is how we, as educational designers, can offer the best possible learning environment for creating competencies for establishing and working in a real team.

In Katzenbach and Smith’s conceptualization, the highest level is the high-performing team. All the good qualities from the real team will be present plus tacit collaborative understanding and alignment. Team members build on each other’s ideas; they are committed and enthusiastic and they reflect automatically before, in, and on the processes. They have created enough trust to dare formulate critical viewpoints and have enough faith to regard these as constructive elements in the common learning process instead of personal critique. The high-performance team also has a mutual commitment, and they can decode each other, so they can, in fact, exhibit the right degree of help to support mutual learning. The collaboration goes beyond the explicit verbal language, and tacit knowledge is an embedded and significant part of the flow in the team.

Working in teams also provides a new dimension to reflection before, in, and on action. What is special about a more team-based education is that there is the process of creating both a reflective practitioner and a reflective team. But how do we bring this into education—or at least provide students with the opportunity to experience a variation and progression of their teamwork? At least, two components in the curriculum will be necessary: (1) that the students will have the opportunity to experience more than one teamwork or project (variation criterion); (2) that the students will have the opportunity to reflect on their experiences (reflection criterion). Not many engineering programs offer these opportunities.

The above considerations of the learning processes complement a systems approach to engineering education and build on a social-constructivist view of learning as introduced in Chap. 5. This means that learning is seen as the process where knowledge is constructed collaboratively and in context. The dialectic relationship between the ones who are constructing, and the constructed, forms the basis of learning.

This social-constructivist view of learning offers an approach to complexity as it embraces diversity as well as inclusivity in the knowledge construction process. Knowledge is basically open for (re)construction by everyone, which raises critical concerns. There is a dialectic tension between diversity and convergence—in other words, between the complexities we experience in real life and the complexity we can cope with.

The degree of complexity seems overwhelming, and we introduced exemplarity, variation, and reflection as core concepts for coping with complexity in the curriculum. The learner’s ability to handle various situations and solve different problems was highlighted, and inquiry-based, studio, and problem-based learning approaches have been proposed to facilitate the transformation needed for engineering education, to cope with complexity were discussed.