https://orcid.org/0000-0001-8976-6202

Introduction

The UCL-Ventura project is a project borne out of the coronavirus pandemic. Its objective was to provide a cheap, effective solution to the dire shortage of ventilator equipment in British hospitals. From conception to delivery, the project took a little over a week, drew on medical research networks spanning countries such as Italy and China, and brought together medical  and engineering expertise from multiple organisations, key amongst them being University College Hospitals,  Mercedes AMG HPP, the UCL Mechanical Engineering Department, and the UCL Institute of Health Engineering. 

What are the factors behind the success of this interdisciplinary, inter-organisational, multi-stakeholder venture? Clare Elwell, professor of Medical Engineering at UCL, has provided an inside story outlining what really transpired throughout the project. Hers is the story of human determination and endeavour; it is a story of human creativity  and innovation in the face of a cataclysmic crisis, and above all, it is a story of ordinary, passionate individuals making the most of their diversity to defeat a problem besetting all humanity. 

My objective is slightly different, though.  All over the world, reformist engineering educators have been preaching the gospel of 21st century engineering  to exasperated students and sceptical academic colleagues. The UCL-Ventura project is an embodiment of this gospel. In this blog piece, my objective is very simple. It is to highlight some of the 21st century skill sets that were deployed in this project. It is my hope that current engineering students will use this blog piece to make connections between their  studies and this project. It is one thing to talk of interdisciplinarity, collaboration and resilience, and another to actually point out and demonstrate their application in a real-life project. Increasingly we are exposing our students to short-duration, intensive, multidisciplinary projects as part of their studies. This blog piece, read in conjunction with Clare Elwell’s story will serve as a helpful case study to guide them as they prepare for these projects. For the engineering educator, my hope is that the UCL-Ventura project will serve as an excellent case study on 21st century engineering practices, and as a template for the development of realistic short duration, multidisciplinary student projects. 

What is 21st century engineering practice?

As many writers have pointed out, 21st-century engineering practice is fundamentally different from engineering practices of the past.This is because the world has become increasingly more complicated and complex. A large part of this is our increasing dependence on technology.  It is no longer possible for any one discipline to address all the problems, issues, or questions that we now face in the 21st century. Instead, problems now require interdisciplinary approaches that draw not only from engineering disciplines, but from the humanities and the physical, biological and social sciences. Not only that, the effective resolution of emerging 21st century problems now often requires a global approach that brings together knowledge and expertise from individuals and organisations drawn from different backgrounds, cultures and countries.  The current Covid-19 pandemic is a case in point. It is not just a medical problem for any one country; it is an all-embracing problem affecting all aspects of humanity, and spanning all the countries of this world. 

Essential Attributes and Skills for the 21st Century Engineer 

Most of the research on the essential graduate attributes and skills for those aspiring to become engineers in the 21st century emphasise that in addition to being technically sound, 21st century engineers should have a broad knowledge-base that goes beyond their field of specialisation, and they should also be equipped with a range of personal and interpersonal skills to enable them to carry out their roles (Abdulwahed et al, 2013). In general, such attributes and skills may include: teamwork, communication, inter/multidisciplinary knowledge, analytical thinking, ingenuity, creativity, technological innovation, business and management skills, leadership, ethics, professionalism, as well as understanding work strategies (National Academy of Engineering, 2004). 

Overview of the UCL-Ventura Project

This project required individuals from various organisations to come together  and contribute their expertise at various phases of the project. To start with, when the urgent need for ventilators became known,  Mervyn Singer, a professor of intensive care medicine at UCL Hospitals drew from his knowledge and expertise to identify an appropriate device type. In addition, he also had an awareness of someone with the engineering skills necessary to deliver the device – Tim Baker from the UCL Mechanical Engineering Department. 

Tim Baker has collaborated extensively with Andy Cowell and Ben Hodgkinson from Mercedes AMG HPP on the student Formula 1 project. As a Formula 1 company, Mercedes AMG HPP have expertise in fast track design and prototype manufacturing, and Tim was aware that this expertise was critical to the success of the project. From within Mercedes AMG HPP, Andy and Ben identified Jamie Robinson, Alex Blakesley and Ismail Ahmad as the people to lead on the fast track design and prototyping task. All three are UCL graduates.

 A team of engineers from UCL Mechanical Engineering and from the UCL Institute of Health Engineering was assembled to work alongside the  Mercedes AMG HPP team. Given the urgency of the situation, this collaborative team of UCL and Mercedes engineers were able to reverse engineer an existing product and have it ready for production within 24 hours. This required resilience and determination from everyone concerned. The fact that  Jamie Robinson, Alex Blakesley and Ismail Ahmad are UCL graduates may also have been a significant factor as the team needed to gel together and get up to speed almost from the very start.`

Before being put on clinical trials, the design had to be approved by the UK’s Medicines and Healthcare Products Regulatory Agency (MHRA). Regulatory approval is normally a very lengthy process, but the team were able to get this done within a few days. Credit for this was down to the familiarity of members of the team with the regulatory process, which led to the team’s decision to focus on reverse engineering a previously approved off-patent device, as opposed to making one from scratch. Another reason for this rapid regulatory approval may be down to the ability of the  UCL Institute of Healthcare Engineering to tap into its partnerships with organisations and colleagues within the UK health system.

Unpacking the Skills and Attributes Deployed in the UCL-Ventura Project

The design and development process of the UCL-Ventura breathing aid consisted of several sub processes, some running in tandem and some running in parallel. Examples include design, ordering of components and subsystems,  manufacturing, fabrication and assembly, testing, documentation, and clinical trials. Effective project management and coordination was therefore critical, and the UCL Institute of Healthcare Engineering drew from its experience to provide this. 

Clearly, the success of this project rested almost entirely on effective collaboration and team-working. The individuals and organisations that were brought together have worked closely, on and off,  for many years on several other projects. The assembly of the team was therefore not a random act, but was based on a clear understanding of what each partner would bring to the project. In the classroom, we are sometimes guilty of positing collaboration and team-working as one-off events. Clearly, this is not so. It takes time, money and effort to build effective collaborative partnerships within and beyond engineering, and this project succinctly demonstrates why this is a useful endeavour.

This project also demonstrates that the success of a collaborative project such as this one is dependent on access to various knowledge domains. For instance, the success of this project required knowledge of intensive care medicine, and of ventilators in particular. It also required fast track design and rapid prototyping expertise, product documentation and manufacturing knowledge. This is what we typically refer to as technical know-how.

The project could not have been successful without access to aspects of  expertise that we typically denigrate as soft skills. This includes creativity and innovation, two skills without which the idea of a ventilator could not have been brought into reality. It also includes an awareness of what is possible and what is not possible, from both a technical and regulatory viewpoint, which was important in the team deciding to go for an off-patent device as their starting point. Knowing who could do what and at what point was also important. This is network-domain knowledge that is acquired through years of developing, building and expanding professional relationships within and beyond organisations. 

Another aspect which was critical to the project was communication. This communication is both intra and inter-disciplinary , and is both within-organisation and inter-organisational.  Communication skills shared by the team enabled the transfer of knowledge from one disciplinary area to another,  and helped to facilitate a shared understanding of what needed to be done and when. The effectiveness of communication within this project team depended, in part, on the ability and willingness of team members to learn for each other, and their preparedness and ability to teach others (impart) what they knew. This falls under the umbrella of informal learning, and highlights why the ability to engage in self-directed learning is an important attribute in real-life projects.

Lessons to take forward

Can the skills exhibited in this project be taught, as Shuman et al (2005)  asked at the beginning of the 21st century? The answer is certainly yes, but how can they be taught? Certainly, these are skills for practice, and as skills for practice they are best taught through practice. This is the reason why team-based projects are now a standard staple within engineering schools. The real question, however, is how effective are current approaches to team-based projects within engineering schools? Clearly, the design and implementation of such projects is not as easy as taking a walk down the path. However, practice within engineering schools seems to indicate otherwise. Almost as a routine, academics are assigned to design and lead team-based project learning without the requisite training and support. And with regard to the assessment of such activities, how certain are we that the assessment is fit for purpose? Too many times, I have witnessed  assessors adopt a confetti approach to the awarding of project marks. What is the meaning of these marks – certainly no one knows for certain. So if anything, the UCL-Ventura project, alongside many other projects that have been rolled out during this coronavirus crisis, should force us to rethink and re-evaluate the way we do team-based projects. There is a long way to go, and these projects are a useful template to adopt and learn from. 

References

National Academy of Engineering. 2004. “4 Attributes of Engineers in 2020.” The Engineer of 2020: Visions of Engineering in the New Century. Washington, DC: The National Academies Press. doi: 10.17226/10999.

Shuman,L, Besterfield-Sacre, M. and  McGourty ,J. (2005). The ABET Professional Skills – Can They be Taught? Can They Be Assessed? The Journal of Engineering Education, Vol. 94, No. 1 

Abdulwahed, M., Balid, W., Hasna, M. O., & Pokharel, S. (2013). Skills of engineers in knowledge based economies: A comprehensive literature review, and model development. In Proceedings of 2013 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) (pp. 759-765). IEEE.

https://orcid.org/0000-0001-8976-6202

Interdisciplinarity is now all the buzz within engineering schools. First, it was the research funding bodies demanding interdisciplinary research. Now it is industry, governments and engineering professional institutions demanding interdisciplinary education. Interdisciplinary research is hugely challenging, not least because the current university system remains clustered around individual disciplines, and mono-disciplinarity remains the modus operandi in day-to-day academic practice.  Interdisciplinary engineering education raises the challenges faced by engineering schools even further.

There are two main reasons for this state of affairs. The first reason is this: academic training and support structures designed to prepare engineering academics for 21^st century higher education practices remain in short supply. The second reason is the prevailing belief that academics do not really need any pedagogic training at all.

The purpose of this blog piece is two-fold. First, it is to answer the question from the individual engineering academic: “What is interdisciplinary education, and how can I get started?” Second, it is to answer the question from directors of education: “How do we develop a truly interdisciplinary engineering curriculum?”

Why engineering education has to become interdisciplinary?

Engineers routinely deal with interdisciplinarity in their practice. For instance, the design of an everyday product like a motor vehicle requires the integration of knowledge and skills from disparate disciplines such as mechanical, electronic and computer engineering, battery technology and energy systems, environmental and sustainability engineering, and ergonomics. As Meyers and Ernst (1995) observed over thirty years ago, engineers have had to become interdisciplinary because their job requires it. Hence, for engineering, interdisciplinarity is not, and has never been an option. It is only that engineering education has so far managed to get away without incorporating interdisciplinarity for so long. However, as so many engineering education researchers have observed, this head-in-the-sand approach is no longer tenable in the 21^st century.

As many writers have pointed out, 21st-century engineers have to adopt interdisciplinary approaches to deal with the critical challenges that they have to resolve. It is no longer possible for any one discipline to address all the problems, issues, or questions associated with these challenges single-handedly. Mahmud (2018) attributes the complexity of such challenges partly to the convergence of distinct technologies originating from different sectors, such as the energy, transportation, health and telecommunication sectors. According to Mahmud, this convergence has given rise to increasingly interdependent, complex socio-technical systems that demand interdisciplinary expertise.  Engineering education has to step up and impart interdisciplinary skills to its graduates.

What is interdisciplinary education?

Currently, disciplines educate and equip students with the disciplinary knowledge and skills they need to address and solve problems in their specific discipline-oriented areas of expertise. For instance, following graduation, a telecom engineering graduate  would concentrate on resolving telecom problems; a mechanical engineer on solving mechanical engineering problem, and a chemical engineer on solving chemical engineering problems. If a problem simultaneously requires the resolution of mechanical, chemical and telecom problems, a standard approach would be to bring together individuals with these skills to form a multidisciplinary team.  In this case, the chemical engineer would focus on the chemical engineering aspects of the problem; the mechanical engineer would focus on the mechanical aspects, whilst the telecom engineer would focus on issues relating to telecommunications. This is the standard multidisciplinary approach.

For complex, interdependent systems, however, the team would need to integrate their disciplinary skills, knowledge, experience and insights, and synthesise this into a shared body of knowledge that enables them to gain a more indepth understanding of the problem at hand. This process requires the individual team members to learn from each other, to shed off discipline-based misconceptions, and to develop a new understanding and awareness of the problem at hand based on a synthesis of knowledge from the individual disciplines. As  Kuldell (2007)  suggests, this process requires the whole team to fully embrace this newly synthesised body of knowledge as the basis for understanding and tackling the problem, together with all the challenges and uncertainties inherent in this new body of knowledge. This is in contrast to maintaining multidisciplinary viewpoints that persist in viewing the subject as an amalgam of their individual disciplinary knowledge. This approach is termed interdisciplinarity, and is best defined as follows:

 Interdisciplinarity is a process of answering a question, solving a problem, or addressing a topic that is too broad or complex to be dealt with adequately by a single discipline or profession… [It] draws upon disciplinary perspectives and integrates their insights through construction of a more comprehensive perspective (Newell, 1998; p.393-4).

So what then is interdisciplinary education? It is an educational process whereby learners draw from two or more disciplines to advance their understanding of a subject or a problem beyond what is achievable from any single discipline (Mahmud, 2018). In so doing, the learners integrate and develop information, concepts, methodologies and procedures from  the individual disciplines to gain new knowledge, understanding and skills so as to be able to explain or solve problems (Holley, 2017). This form of learning is necessarily active, self-directed learning.

What factors should you consider when implementing an interdisciplinary curriculum?

The first thing to remember when planning an interdisciplinary engineering curriculum is this:  University teaching is organised around the disciplines, and disciplines have different ways of disseminating, organising and thinking about the knowledge that underpins them. Because of this, individual disciplines have different approaches to teaching, and this applies to individual disciplines within engineering as well. Entwistle (2009) sums up this dilemma as follows:

There is a logic that holds together the various strands of a discipline or topic area, and there is a logical connection between the intellectual demands of the subject and how best to teach it.

The outcome of this is that academic staff engaging in interdisciplinary teaching are susceptible to reverting to their normal discipline-based teaching. Hence, if close attention is not paid to the process of designing and implementing the interdisciplinary curriculum, students on the receiving end of the curriculum will perceive their learning as a disparate, disjointed set of modules drawn from different disciplines (Foley, 2016). At a minimum, therefore, to be successful, an interdisciplinary curriculum should endeavour to create a cohesive, integrated approach that both staff and students can invest in (Kuldell, 2007).

A second consideration is that most engineering programmes are offered at undergraduate level. At this level, students mostly view themselves through the lens of their individual disciplines. They have come to university to specialise in their particular discipline, and anything other than their discipline is likely to demotivate them. Hence, the primary pursuit of students at this level is the mastery of their discipline’s approach to problem solving. How then can one can one resolve this dilemma?

Holley (2017) suggests that to be successful, an interdisciplinary curriculum should provide learning environments that allow students and academic staff from different disciplinary backgrounds to engage in scholarly conversations around issues of shared interest and importance, while also exploring connections between their majors and other sources of knowledge and experience. Within the classroom, adopting an overarching topic, theme, or problem can help to establish bridges of shared understanding between the different disciplines. With regard to pedagogy, adopting a research-based, problem solving approach may be the best approach to fostering interdisciplinarity (Kuldell, 2007).

Attention to the development of an interdisciplinary curriculum should  also focus on out-of-class activities. Lattuca et al. (2017) suggest that students should be encouraged to participate in co-curricular activities and experiences that are inherently interdisciplinary. For instance, in their study of students perceptions of interdisciplinary learning,  Lattuca et al. (2017) observed that there was a positive correlation between students perceptions of interdisciplinary learning and their participation in non-engineering clubs and activities, study abroad, and humanitarian engineering projects. This suggests that providing opportunities for students to engage in interdisciplinary activities both within and outside the classroom helps to provide a supportive environment in which students can develop their interdisciplinary skills organically.

Concluding remarks

This overview does suggest that achieving interdisciplinary education is difficult. Whilst this is true, achieving success is not beyond the realms of possibility. What this means is that implementing interdisciplinary education requires commitment and endeavour from both senior management and academic staff. To date, there is no proven cookbook approach to implementing interdisciplinary education within engineering. However, the topic is currently receiving considerable attention from engineering education researchers. This means that increasingly, we are now able to identify evidence-based approaches that can help us in our endeavours to implement interdisciplinarity within engineering education.

REFERENCES

ENTWISTLE, N. 2009. Teaching for understanding at university: Deep approaches and distinctive ways of thinking, Palgrave Macmillan.

FOLEY, G. 2016. Reflections on interdisciplinarity and teaching chemical engineering on an interdisciplinary degree programme in biotechnology. Education for Chemical Engineers, 14, 35-42.

HOLLEY, K. 2017. Interdisciplinary curriculum and learning in higher education. Oxford Research Encyclopedia of Education.

KULDELL, N. 2007. Authentic teaching and learning through synthetic biology. Journal of Biological Engineering, 1, 8.

LATTUCA, L. R., KNIGHT, D. B., RO, H. K. & NOVOSELICH, B. J. 2017. Supporting the development of Engineers’ interdisciplinary competence. Journal of Engineering Education, 106, 71-97.

MAHMUD, M. N. 2018. Interdisciplinary Learning in Engineering Practice: An Exploratory Multi-case Study of Engineering for the Life Sciences Projects. University of Cambridge.

MEYERS, C. W. & ERNST, E. W. 1995. Restructuring Engineering Education: A Focus on Change: Report of an NSF Workshop on Engineering Education, Division of Undergraduate Education, Directorate for Education and Human ….

NEWELL, W. H. 1998. Interdisciplinarity: Essays from the Literature, College Entrance Examination Board.

https://orcid.org/0000-0001-8976-6202

Although problem based learning (PBL) has been around for almost 50 years, following its introduction and development in medical education at McMaster in the late 1960s and early 1970s, it has remained relatively unknown until fairly recently. Putting this into perspective, in the period 2000 – 2010, very few engineering academics had ever heard of PBL – fast forward to today, and PBL has become almost synonymous with the term “engineering education”.

However, despite the ubiquity of PBL within the engineering academic community, it remains a challenge to come across academics who are comfortable in effectively implementing PBL in their own practice. A key reason for this may be the failure of academic development centres to keep pace with the rapid adoption of 21^st century-focussed curricula within engineering education, and within higher education in general.  Another contributory factor may be the prevailing, and thoroughly misplaced, belief that academics do not really need any pedagogic training at all.

For the engineering academic needing to get up to speed with PBL-oriented pedagogic practices, it may be a challenge just to have an idea of where to turn to. In this piece, I highlight some of the online resources that an individual can access.

1. Problem based Learning: Northern Illinois University, Faculty Development and Instructional Design Center – This is a short guide that explains what PBL is, how it differs from traditional teaching, and how you can get started implementing PBL in your own teaching.
2. Introductory Course On PBL In Higher Education – Free Online Course: Aalborg Centre for Problem Based Learning In Engineering Science And Sustainability Under The Auspices Of UNESCO (UCPBL) – A free online course that gives an overview of the PBL process as well as links to pertinent resources.
3. The Aalborg Experiment: Project Innovation in University Education. This is an evaluation of PBL implementation at Aalborg University carried out in 1994. Although somewhat dated, it gives important insights into the practical issues that need to be addressed if PBL implementation is to be successful.
4.  Problem and Project Based Learning: Goodhew, Peter. “Teaching engineering.” The School of Engineering’s Active Learning Lab at The University of Liverpool(2010). In section  5.4-5.6 of this textbook, which is available for download, Peter Goodhew gives pertinent instructional advice on how one can get started on problem and project based learning. He also provides relevant engineering examples that one can try out.
5. Problem-Based Learning:Stanford University, Speaking of Teaching Newsletter Archive. This is a short guide on PBL that provides guidance on designing problem sets, structuring PBL classes, and offers advice on assessment design.
6. The Tutor in Problem Based Learning: A Novice’s Guide: This is a fairly comprehensive guide from McMaster University outlining how you can design and implement PBL. It also provides common problems that arise in PBL implementation and offers suggestions on resolving them.
7. Revolutions and Re-iterations: An Intellectual History of Problem-based Learning: Virginie Servant PhD Thesis – Who were the “Founding Fathers” of PBL? What were the issues and arguments they had to grapple with? How did PBL come to be the way it is today? If you are grappling with any of these questions, this thesis is your best starting point.

It is no longer enough for higher education to focus just on the transmission of information and the retention of facts. Rather, we now expect students to come out of higher education equipped with a range of high-level skills and abilities such as:

• critical thinking skills to enable them to handle and interpret concepts, evidence and ideas
• the ability to think and act as experts
• innovation and creativity to enable them to produce original insights and valuable knowledge for the benefit of society (Imperial College, 2019).

In addition to adopting learning and teaching techniques that foster the development of these high-level skills, we also need to transform higher education assessment practices as well. Within engineering mathematics, where the end of year exam still predominates, this requires careful selection and design of exam questions to ensure students are assessed for competence in both the basic and high-level mathematical skills. In this piece, I look at two taxonomies that aid the development of such exam questions. These two are the Mathematical Assessment Task Hierarchy (MATH) taxonomy (Smith et al., 1996) and  Galbraith and Haines’ Mechanical-Interpretive-Constructive taxonomy (Galbraith and Haines, 2000).

The MATH taxonomy

The MATH taxonomy groups mathematical skills into three groups, A, B, and C.

Group A skills

Smith et al defined Group A skills as the standard, routine procedures taught to students along with the factual information they have to recall. They further divided this group into three categories:

• Factual knowledge – the ability to recall previously learned information in the way in which it was given
• Comprehension – the ability to use standard techniques to solve a problem. This includes the ability to recognise symbols in a formula and to substitute into the formula using information learnt previously.
• Routine procedures – the ability to use routine procedures to solve a given problem in the same way previously learnt to solve similar problems. This requires students to have worked on similar problems beforehand.

Group B skills

Group B skills focus on the ability to use mathematical information in new ways, for example, applying routine procedures to new situations. These skills fall into two categories:

• Information transfer – includes the ability to transform mathematical information from one form to another, for example from verbal to numerical (or vice versa), or from algebraic to graphical etc. This category also includes the ability to explain the relationships between component parts of a mathematical problem, the ability to recognise whether or not a particular formula or method can be used in a new context. In addition, this category also includes the ability to explain mathematical processes, including summarising in non-technical terms for non-mathematical audiences.
• Application in new situations – the ability to choose appropriate methods and information and apply them to new situations. This includes modelling real life situations, extrapolating new procedures to new situations, and proving previously unseen theorems and results.

Group C skills

Group C skills are the skills that enable students to apply previously learned concepts to the analysis and solution of mathematical problems for which no routine procedures have been provided. These skills fall into three categories:

• Justifying and interpreting – the ability to justify and/or interpret a given result, or a result derived by the student. This includes proving a theorem in order to justify a result, the ability to find errors in reasoning, recognition of unstated assumptions, and recognising the limitations of a model and being able to decide on the appropriateness of a model.
• Implications, conjectures, and comparisons – the ability to make comparisons, with justifications, in different mathematical contexts, as well as the ability to draw implications and make conjectures, with justification, when given or having found a result.
• Evaluation – the ability to judge the value of material for a given purpose based on definite criteria. This includes the ability to make judgements, the ability to select for relevance, the ability to argue coherently on the merits, or otherwise, of an algorithm, organisational skills and the ability to rearrange information and draw previously unseen implications from it.

The Galbraith & Haines’ taxonomy

Galbraith and Haines arrived at their taxonomy independently of the Smith et al MATH taxonomy. However, after reviewing both taxonomies they suggested that their taxonomy could be interpreted as a summary of the MATH taxonomy (Galbraith and Haines, 2000).

Like the MATH taxonomy, the Galbraith & Haines taxonomy also has three levels, although they have a different terminology. The three levels are the mechanical, interpretive and constructive levels.

Mechanical skills

These skills refer to the routine use of mathematical procedures as cued by the wording of the question. They are equivalent to Group A skills in the MATH taxonomy.

Interpretive skills

These skills refer to the ability to retrieve and apply conceptual knowledge. They are equivalent to Group B skills in the MATH taxonomy.

Constructive Skills

These skills refer to the ability to arrive at a solution or conclusion using a range of mechanical and interpretive tasks without the necessary guidance for doing so. This essentially involves the construction of a solution rather than simply selecting between given alternatives. These skills are equivalent to the Group C skills on the MATH taxonomy.

Concluding remarks

Most mathematics exams only assess Group A skills, with only a few assessing Group B skills as well, and virtually none assessing Group C skills (Brown, 2010). However, Pounteny et al (2002)  suggest that we should be teaching undergraduate students up to the level of Group C as this is the skill level at which students are able to demonstrate “understanding to the point of justifying and explaining knowledge, being able to evaluate actions, and the development of new knowledge”. These, according to Pounteny et al, are the skills that we associate with being a mathematician and a problem solver. Moreover, it is clear that these are the skills expected of higher education graduates in the 21^st century. Hence, to ensure that the end-of-year exam remains relevant in the 21^st century,  we have to ensure that a significant proportion of the exam questions is  pitched at the Group C / Constructive level.

References

BROWN, R. G. 2010. Does the introduction of the graphics calculator into system-wide examinations lead to change in the types of mathematical skills tested? Educational Studies in Mathematics, 73, 181-203.

GALBRAITH, P. & HAINES, C. 2000. Conceptual mis (understandings) of beginning undergraduates. International Journal of Mathematical Education in Science and Technology, 31, 651-678.

IMPERIAL COLLEGE. 2019. Project Xeper -The future of engineering teaching [Online]. Imperial College,.  [Accessed 28/07/2019 2019].

POUNTNEY, D., LEINBACH, C. & ETCHELLS, T. 2002. The issue of appropriate assessment in the presence of a CAS. International Journal of Mathematical Education in Science and Technology, 33, 15-36.

SMITH, G., WOOD, L., COUPLAND, M., STEPHENSON, B., CRAWFORD, K. & BALL, G. 1996. Constructing mathematical examinations to assess a range of knowledge and skills. International Journal of Mathematical Education in Science and Technology, 27, 65-77.

 

https://orcid.org/0000-0001-8976-6202

Abstract for paper presented at the 8th Research in Engineering Education Symposium (REES 2019) held in Cape Town, South Africa, from 10–12 July 2019. The extended abstract can be downloaded HERE:

CONTEXT

Traditionally, mathematics and engineering principles course modules constitute the bulk of undergraduate teaching in the first and second year of the engineering curriculum. These introductory course modules play a critical role in the study of engineering by laying the foundational engineering and mathematics principles that underpin the study of more advanced engineering modules in the latter stages of the undergraduate programme. However, students often perceive these foundational modules as being disconnected from the “real” engineering that they came to study, and this often leads to heightened student dissatisfaction and disengagement from further engineering studies. To alleviate this situation, most engineering education researchers and educators have advocated the adoption of computational modelling within these foundational engineering course modules.

PURPOSE

MATLAB has been an integral part of teaching in first and second year engineering mathematics at University College London since 2013. This has improved student engagement with mathematics and raised their awareness of its relevance within their chosen engineering disciplines, and subsequently led to the adoption of MATLAB in other introductory course modules.. However, this adoption has not been uniform.  In the study that I report in this paper, I set out to establish the reasons why MATLAB adoption patterns are so variable amongst first and second year introductory engineering course modules. Specifically, I sought to establish the pedagogic and learning environment structural features that facilitate or impede the successful adoption of MATLAB as a teaching tool within the early-stage foundational engineering course modules.

APPROACH

To address these research questions, I adopted a multi-dimensional case study approach that incorporated interviews with students and lecturers in the first and second year of undergraduate engineering across five engineering departments, peer observation of teaching, evaluation of student performance and feedback, and interviews with programme leaders and directors of education. To facilitate this analysis, I framed the study as an innovation diffusion case study. This enabled me to evaluate the data and to frame the outcomes using a variety of innovation diffusion models that have been applied to the study of curriculum change in various educational establishments.

RESULTS

Whilst the study is still very much a work-in-progress, key findings that are emerging are largely consistent with emerging views of innovative learning diffusion within educational practices. Specifically, my findings point to the need for effective learning support mechanisms that incorporate both top-down and bottom-up approaches to driving innovation. The study also highlights the detrimental effects of institutional focus on disciplinary research on the adoption of innovation in learning and teaching practice. In addition, the study also highlights the need to open up the undergraduate education landscape to emerging educational practitioners such as learning technologists and education administrators.

CONCLUSIONS

The teaching and delivery of first and second year undergraduate engineering course modules can be substantially improved by incorporating computational modelling, visualisation and analysis techniques. However, this adoption process is not straightforward, and requires cultural and structural changes within engineering schools if it is to be effective. For this to happen, institutional leaders have to demonstrate a willingness to go beyond rhetoric by actively advocating and supporting the adoption of innovative practices within the undergraduate engineering curriculum.

KEYWORDS

Computational modelling and analysis, visualisation, undergraduate engineering education

https://orcid.org/0000-0001-8976-6202

Mathematics can pose significant challenges and obstacles to students who are in the early phases of their studies at university (Hernandez‐Martinez and Williams, 2013). This also includes engineering students, even though they, along with the rest of the engineering community, believe that the study of mathematics is indispensable to the study and practice of engineering (Sazhin, 1998). Students find the transition to university mathematics difficult for a number of reasons. One of these reasons is the lack of mathematical resilience, the subject of my discussion today. For this, I shall be relying on the work of Sue Johnston-Wilder and Clare Lee, who have popularized the concept of mathematical resilience within pre-university mathematics education.

Mathematical resilience

An individual is said to be resilient if they achieve “good outcomes in spite of serious threats to adaptation or development”(Masten, 2001). Previously, educators and researchers believed that resilience was an attribute that only a few people possessed. However, Masten’s studies have since debunked this notion. Instead, her studies posit that, far from being exclusive, resilience is an ordinary trait that any individual can acquire. This new understanding renders invalid prevailing deficit models of intellectual ability. Indeed, using this new understanding, Yeager and Dweck (2012) have shown that the resilience of students improves when they are redirected to see intellectual ability as “something that can be developed over time with effort, good strategies, and help from others.”

Johnston-Wilder and Lee use the construct of mathematical resilience to describe “a learner’s stance towards mathematics that enables pupils to continue learning despite finding setbacks and challenges in their mathematical learning journey” (Johnston-Wilder and Lee, 2010). Often, learning mathematics is associated with negative emotions, such as avoidance, anxiety and helplessness (Lee and Johnston-Wilder, 2017). These negative emotions can be exacerbated by poor mathematics teaching methods, enduring cultural beliefs that mathematics is only for the gifted few, and fixed mindset beliefs that if someone is not good at mathematics, then there is no way they can improve their mathematical ability, even if there is good teaching and support (Lee and Johnston-Wilder, 2017). Mathematical resilience challenges these negative notions, and seeks to engender within the minds of learners the knowledge that that they can grow their mathematical abilities (Lee and Johnston-Wilder, 2017).

Characteristics of mathematically resilient students

There are four characteristics for students with mathematical resilience. These are growth mindset, personal value of mathematics, knowing that mathematics requires struggle and knowing how to recruit support in pursuing (Lee and Johnston-Wilder, 2017).

Growth mindset (Yeager and Dweck, 2012): The notion of growth mindset suggests that the brain is malleable. Consequently, students with a growth mindset believe that their mathematical abilities improve with practice, persistence and support from others (this includes peers and academic staff).

Value: Mathematically resilient students believe that mathematics is a valuable subject and is worth studying. Such values can be fostered within engineering by making explicit the connections between mathematics and engineering theory and practice.

An understanding of how to work at mathematics: Mathematically resilient students are aware that one has to persevere if they are to make progress in learning mathematics. They are aware that everyone experiences difficulties and challenges when learning mathematics; that mistakes are part of the learning process, and that one has to persevere and learn not to succumb to the negative emotions that are a part of learning something new.

Knowing how to gain support: Mathematically resilient students are aware that learning mathematics can be challenging, and that sometimes they need to seek support in order to get ahead. They are also aware of the support structures for mathematics within and outside their university. They are also aware, and adept, at constructively working and studying with each other.

Lessons for mathematics educators

Teaching mathematics is not just about delivering subject content. It is also about developing a supportive, inclusive, collaborative learning environment that challenges and encourages students to do the best they can. Indeed, achieving this is the holy grail of learning and teaching in engineering mathematics.

References

HERNANDEZ‐MARTINEZ, P. & WILLIAMS, J. 2013. Against the odds: resilience in mathematics students in transition. British Educational Research Journal, 39, 15.

JOHNSTON-WILDER, S. & LEE, C. 2010. Mathematical Resilience. Mathematics Teaching, 218, 38-41.

LEE, C. & JOHNSTON-WILDER, S. 2017. Chapter 10 – The Construct of Mathematical Resilience. In: XOLOCOTZIN ELIGIO, U. (ed.) Understanding Emotions in Mathematical Thinking and Learning. San Diego: Academic Press.

MASTEN, A. S. 2001. Ordinary magic: Resilience processes in development. American psychologist, 56, 227.

SAZHIN, S. 1998. Teaching mathematics to engineering students. International Journal of Engineering Education, 14, 145-152.

YEAGER, D. S. & DWECK, C. S. 2012. Mindsets That Promote Resilience: When Students Believe That Personal Characteristics Can Be Developed. Educational Psychologist, 47, 302-314.

https://orcid.org/0000-0001-8976-6202

In a book published in the year 2000, entitled Learner-Centered Assessment on College Campuses: Shifting the Focus from Teaching to Learning, Mary Huba and Jann Freed asked the question:

 “Tomorrow’s citizens, tomorrow’s leaders, tomorrow’s experts are sitting in today’s college classrooms. Are they learning what they need to know? Are faculty using teaching methods that prepare them for future roles?”

Then, the main mode of teaching in engineering schools, and pretty much everywhere else within the university system, was the time-honoured, traditional, teacher-centred, lecture model. The main characteristic of this method was the reciting of material from textbooks, and passive, rote-learning from students. Then, the main assessment method was the end of course exam. And this was characterised by a tendency to prioritise rote learning as opposed to informed engagement with the study material.

Fast-forward to the year 2019, Mary Huba and Jann Freed would have to rephrase their question to ask:

 “Today’s citizens, today’s leaders, today’s experts are sitting in today’s college classrooms. Are they learning what they need to know? Are faculty using teaching methods that prepare them for present and future roles?”

Why? Because tomorrow has arrived. And it doesn’t look pretty.

Unlike in 2000, this time around changes are sweeping through higher education, and new forms of student-centred, active learning approaches are now in vogue. Yet, the traditional approach, characterised by lectures, passive learning and exams still dominates in large swathes of the university system.

As engineering academics, we can easily forget that the world that our students are graduating into is no longer the same world that we graduated into thirty or forty years ago. Occasionally, we do get shocked when the evening news announces, yet again, the collapse of a high street brand name. However, come the next day, we go back to our academic duties in the world-acclaimed universities that we belong to, and continue teaching our students the same way that we have taught them in the past ten years. We are used to this, and we like it. We know our lecture notes by heart, to the extent of even knowing when to make that dramatic pause, and when to recite that yesteryear joke.

Our lectures might be boring, but they are predictable, and our exams are also boringly predictable. Our students love it. Our exams have not changed in years. Yes, exams, because we do not believe in any other form of assessing our students. Continuous assessment is anathema to us. And our students pretty much know the questions that will appear in our end of year exams – last year it was this question from this paragraph in the lecture notes, the year before it was this other question, and the year before that it was this other question, so this year it is definitely going to be this question. If you are not in the know, and you look at the exam, it may look as if only the Einsteins and Brunels of this world can crack it.  But guess what, by the time the students come to the exam, they will have practised similar examples over and over again – for them it is a matter of recall, and nothing more. And they go on to get first class and upper second class degrees in Engineering, but they are definitely short of the preparation they need to be effective in industry.

Employers have no choice but to hire our not-fit-for-purpose graduate engineers. They are the only offering on the plate, and there is nowhere else to turn to.  They are the product of a well-honed, boringly predictable, first class engineering education, but they are barely of any use to the engineering world of today. We, the engineering academics know it; the students know it, the government knows it, parents know it, and the employers know it. That’s how things  are, but for how long?

Today’s complex world is crying out for nimble-minded graduate engineers whose education has given them the ability to critically analyse, assess and offer viable technological solutions to the myriads of engineering problems that routinely crop up every day. Instead, our graduates, bred on traditional engineering education, are hopelessly incompetent. In all their university lives, they have never had to face the challenging, unpredictable, academic problem sets meant to prepare them for the bold world of 21^st century engineering. Suddenly, faced with the reality of modern day engineering, their first class and upper second-class degrees lose their shine. Even the illustrious names of the universities that offered them these degrees suddenly look very unimpressive.

The world has to move on, and even now, employers are beginning to look elsewhere for the talent that they need. That talent might not come from an acclaimed engineering school, or from a world-class university, but it will be the talent needed for the engineering world of today. Engineering education has to change; otherwise, our current engineering schools are doomed.

The retail world has been here before us. Century-old retail giants have been collapsing left, right and centre, whilst newer, more technologically attuned retail enterprises take over. If the engineering school of today does not adapt to the changing winds, then a new breed of engineering school will take over, and we and our students will soon go the way of the dinosaurs, only at a much faster pace.

https://orcid.org/0000-0001-8976-6202

In everyday conversations, the UK weather is by far the safest bet for non-controversial small-talk. This is because the variability and relative unpredictability of UK weather easily ensures that you can find loads of points of agreement with your fellow conversationalist. Within engineering education we also have another favourite topic for small talk – this is the A Level mathematics qualifications. This is because, until recently, it was relatively easy to find something within the A Level mathematics qualifications that any two members of the engineering academic community could agree to mourn about. This is all about to change, however. A Level mathematics has recently undergone a complete transformation, and the refurbished qualifications are already being delivered in schools, with the first batch of A level graduates expected to start their university studies in September 2019.

Why the A Level mathematics qualifications had to be redesigned

Over the past thirty years or so, there have been wide ranging complaints about the quality and perceived shortcomings of A level mathematics. Michael Gove, the then Secretary for Education, adequately summarised these shortcomings in a letter that he wrote to the Chief Executive Office of Qualifications and Examinations Regulation [DfE, 2013]:

• A level mathematics fails to provide the solid foundation that students need to prepare them for degree-level study and for vocational education
• A level mathematics students lack the deep understanding and/or the necessary skills to make connections between mathematics topics, and between mathematics and other subjects
• Assessment practice in A level mathematics is overly structured and encourages a formulaic approach to mathematical thinking instead of using more open-ended questions that require advanced problem-solving.
• Universities have had to adapt their teaching approaches to include remedial mathematics tuition for underprepared first year undergraduates.

A survey of mathematics departments within universities also revealed the following shortcomings [ALCAB, 2014]:

• The mathematical thinking of the most able students is not developed.
• The distinction between A and A* grades, i.e. the top most grades, seems to be based on the avoidance of careless slips rather than genuine mathematical ability.
• It is not clear what applied mathematics students have learnt.
• Current statistics provision tends to focus on routine calculations at the expense of interpretation and understanding.

Purpose of the new A Level Mathematics

According to the Department for Education, the primary purpose of the new A level mathematics qualifications is to [DfE, 2016]:

• Build from GCSE level mathematics and introduce calculus and its applications
• Emphasise how mathematical ideas are interconnected and how mathematics can be applied to model situations mathematically using algebra and other representations
• Helping students to make sense of data
• Helping students to understand the physical world and to solve problems in a variety of contexts, including social sciences and business
• Preparing students for further study and employment in a wide range of disciplines involving the use of mathematics.

Structure of the new A Level Mathematics qualifications

With the exception of further mathematics, all students studying A level mathematics now have to cover statistics and mechanics in addition to pure mathematics [Ofqual, 2016a]. This now provides universities with certainty as to the exact nature and level of mathematics covered by A level mathematics graduates.

In addition, students are now also expected to demonstrate the following overarching knowledge and skills across the entire content detailed in the A Level mathematics specification [DfE, 2016]:

• Mathematical argument, language and proof
• Mathematical problem solving
• Mathematical modelling.

Ofqual has also provided guidance on mathematical problem solving, modelling and the use of large data sets in statistics. This guidance also includes advice on assessing these key aspects, together with example questions [Ofqual, 2016c].

The new A Level Mathematics objectives and assessment format

The new A level mathematics qualifications have been specifically designed to deliver the following objectives [Ofqual, 2015]:

• Using and applying standard techniques: The new qualifications seek to ensure that learners are able to select and correctly carry out routine procedures, and to accurately recall facts, terminology and definitions.
• Reasoning, interpreting and communicating mathematically: Learners should be able to construct rigorous mathematical arguments (including proofs), make deductions and inferences, assess the validity of mathematical arguments, explain their reasoning and use mathematical language correctly.
• Solving problems within mathematics and in other contexts: Learners should be able to translate problems in mathematical and non-mathematical contexts into mathematical processes, interpret solutions in the context of a problem, and, where appropriate, evaluate their accuracy and limitations. Learners should also be able to translate situations in context into mathematical models. They should also be able to use mathematical models, evaluate the outcomes of modelling in context, recognise the limitations of models and, where appropriate, explain how to refine them.

Ofqual recommends that these three objectives should be assessed in accordance with the weightings outlined in Table 1 below [Ofqual, 2016b]:

Table 1: Assessment weighting for A and AS level mathematics

Objective	Weighting (A Levels)	Weighting (AS Levels)
Using and applying standard techniques	50%	60%
Reasoning, interpreting and communicating mathematically	25%	20%
Solving problems within mathematics and in other contexts	25%	20%

The new A Level Mathematics qualifications compared to First Year Engineering Mathematics

The new A Level mathematics qualifications are closely aligned to engineering mathematics as taught in the first and second year of university. For example, the objectives of the new qualifications closely agree with the intended learning outcomes of our first year mathematical modelling and analysis course module for engineering at University College London, whereby we state that by the end of the course module, students will be able to:

• Recognise the connections between mathematics and engineering, and how mathematical ideas are embedded in engineering contexts;
• Represent real-world systems from engineering in a mathematical framework;
• Identify and draw upon a range of mathematical concepts, including Calculus, Linear Algebra and Differential Equations to analyse specific problems and identify the appropriate mathematics to realise a solution;
• Employ appropriate computer programming and modelling techniques and statistical analysis to efficiently solve and evaluate the performance of engineering systems;
• Use estimation, approximation and dimensional analysis to reduce complexity;
• Relate the behaviour of the output of mathematical models to the underlying physical or conceptual models of interest;
• Carry our engineering problem solving both collaboratively in a team and independently;
• Present and interpret mathematical results in effective and appropriate ways to varied audiences, including non-mathematical engineering audiences.

Given such close alignment, we expect that from September 2019 onwards it will be much easier for first year students to adapt to university-level teaching. This is likely to reduce the time and effort spent by academic staff in remedial mathematics teaching. Instead, we hope that academics responsible for first and second year engineering mathematics teaching will use that time to focus on bringing engineering mathematics into closer alignment with other course modules in engineering.

References:

DfE (2013). Reform of GCE A Levels – Letter from Secretary of State to Glenys Stacey. Available at: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/278146/SoS_January_2013_ofqual_letter_alevels_v2.pdf [accessed 25 August, 2018]

ALCAB (2014). Report of the ALCAB panel on mathematics and further mathematics. The A Level Content Advisory Board. https://alevelcontent.files.wordpress.com/2014/07/alcab-report-on-mathematics-and-further-mathematics-july-2014.pdf [accessed 25 August, 2018]

DfE (2016). Content for mathematics AS and A level for teaching from 2017. Department for Education. Available at: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/516949/GCE_AS_and_A_level_subject_content_for_mathematics_with_appendices.pdf [accessed 25 August, 2018]

Ofqual [2015]. AS and A Level Mathematics and Further Mathematics: Consultation on Conditions and Guidance. Ofqual. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/481981/as-and-a-level-mathematics-and-further-mathematics-consultation-on-conditions-and-guidance.pdf [accessed 25 August, 2018]

Ofqual [2016a]. GCE Subject Level Conditions and Requirements for Mathematics. Ofqual. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/517726/gce-subject-level-conditions-and-requirements-for-mathematics.pdf [accessed 25 August, 2018]

Ofqual [2016b]. GCE Subject Level Guidance for Mathematics. Ofqual. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/514465/gce-subject-level-guidance-for-mathematics.pdf [accessed 25 August, 2018]

Ofqual [2016c]. Report on Mathematical Problem Solving, Modelling and the Use of Large Data Sets in Statistics in AS/A Level Mathematics and Further Mathematics. A Level Mathematics Working Group, Ofqual. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/481857/a-level-mathematics-working-group-report.pdf [accessed 25 August, 2018]

1. Professor Peter J Goodhew CBE FREng, Emeritus Professor of Engineering, formerly Dean of Engineering and Pro-Vice-Chancellor at the University of Liverpool

It might be helpful to clarify what engineering education is not. It is not about the acquisition of specific practical skills, however useful or interesting they might be to any individual. It is not about training people to run CFD codes or send CAD designs to a CNC machine or to grow crystals or to sign off structural steelwork. It is about the conceptual, planning and design skills which should precede all these activities. It is about imagining and understanding and predicting, as quantitatively as possible, why and how an engineering objective can be realised and delivered. (Goodhew 2014)

2. Professor Emanuela Tilley, Director of the Integrated Engineering Programme, University College London

Engineering education is no longer solely about specific content anymore or, indeed, traditional knowledge. It’s much more about processes and the students’ application of the knowledge. (AECOM 2018)

3. Professor Jeremy Watson CBE FREng, past President of the Institution of Engineering Technology

We need to train a new generation of engineers in skills that are genuinely relevant to the new industrial values of flexibility, technical advancement and on-going innovation. Single discipline specialism and theory will no longer cut it in the modern world.  (The Institution of Engineering and Technology and The Engineering Professors’ Council 2017)

4. Professor Janusz Kozinski: Founding President & Vice-Chancellor of the Hereford University of Technology and Engineering, formerly Founding Dean of York University’s Lassonde School of Engineering

The stars are aligned for an Engineering Renaissance here in the UK and throughout the world. We as educators need to seize this moment to work collaboratively with students and employers to co-create a whole new set of models to reflect their needs. In doing so, we can turn a fear of change and flux created by technology and disruption, into a new era of enlightenment for engineering education. (Koziński, J. A., and Evans, E. F., 2017).

5. Massachusetts Institute of Technology

5. Successful engineering education change is characterised by (1) a leadership style that articulates a clear educational vision and demonstrates a personal commitment to establishing a new paradigm for engineering education at the institution; (2) a distinctive ‘spirit’ or culture of collegiality and common purpose that pervades the faculty, (3) student engagement in and understanding of new educational approaches, (4) in-house development of new tools and resources to support and advance the educational approach. (Graham 2018)

References

AECOM. (2018). The future of infrastructure: Expert opinions from around the world on the challenges and opportunities ahead. AECOM.

Goodhew, P. J. (2014). Teaching Engineering: All you need to know about engineering education but were afraid to ask, London: Royal Academy of Engineering.

Graham, R. (2018). The global state of the art in engineering education. Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Koziński, J. A., and Evans, E. F. (2017). “An Engineering Renaissance”, New Approaches to Engineering in Higher Education. London: The Institution of Engineering and Technology, The Engineering Professors’ Council.

The Institution of Engineering and Technology, and The Engineering Professors’ Council. “New Approaches to Engineering in Higher Education.” Presented at New Approaches to Engineering in Higher Education, London.

We are now approaching the end of the year, and attention has shifted to the next year. Barring unexpected departures, most departments have probably now concluded their hires for the next year. And as for you, congratulations if you are one of those who have secured your first academic position. You should give yourself a pat on the back. After all, you have made it ahead of at least seventy other competitors who applied for the same role.

By now your new department will have been in touch with you to discuss your teaching for the next year. You would have demonstrated in the interviews both your passion and your growing expertise in engineering education practice. Naturally, you will be eager to demonstrate and cement your position as an innovative teacher who aspires to excellence. A word of advice, however: teaching quality can be highly subjective, and excellence in education is context-dependent. So before you rush ahead to implementing all the whistles and bells that you can think of in your education practice, you need to first consider the following three questions:

• What exactly is it that you will be teaching?
• Who exactly will you be teaching?
• Who else will be teaching with you?

What exactly is  it that you will be teaching?

Your new Head of Department may have contacted you to tell you of the good news. They will also have discussed with you the course modules that you will be teaching. However, it’s not just that you will be teaching some specific course module, like Maths101, Fundamentals of Engineering 103 or some other course unit. Even if you may already have some experience delivering these course modules, and you are confident that you will do a good job, you really need to delve deeper into the exact course content that you need to deliver.

Engineering departments are not all the same. They have different missions, and different teaching philosophies, beliefs and values. You may have heard that there is a visible curriculum, and there is a hidden curriculum.  The two are not necessarily the same, and what matters most is the hidden curriculum. It is this hidden curriculum that is in tune with the goals and aspirations of the department, and  the one that determines the specific topics that you should cover, the depth that you are expected to go in each topic, as well as how exactly you are to assess the students.

Degree programmes normally comprise course modules that feed into each other, or that provide knowledge and skill sets that are designed to complement each other. In this way,  module units help to achieve a coherent set of learning outcomes at each level of the degree programme. In practice, the written down syllabus does not provide the whole story. A lot of assumptions, implied and non-implied go into the development of a degree programme. It is your duty to find out all this, and then work out what you need to deliver in your assigned course module. Start talking to people within the department, and go through the historical records of your assigned course modules.

Who exactly will you be teaching?

Again, students are not all the same. Different universities tend to attract different types of students. As a result, students attending a particular university may have expectations that are quite different from those attending some other university. For instance, students at a highly selective, traditional university may have been primed for highly mathematical and theoretical content. They may even be expecting “the sage on the stage” approach to teaching, together with the traditional exam-oriented assessment format. They may even be more focussed on the mark they are going to get, and not necessarily on what they actually learn in your class. In such a situation, rushing to implement fancy stuff like team-based, collaborative learning may serve only to alienate them from your teaching.

On the other hand, more innovative, forward-looking universities usually attract students who are keen to experience “real” engineering practice. Such students usually expect a hands-on, design-based approach that integrates theory and practice. In such an environment, adopting a hands-off “sage on the stage” approach is likely to hasten your departure from the university.

Who else will be teaching with you?

You need to find out quickly the nature of your soon-to-be colleagues. Are they conservative and suspicious of any innovations in education practice? Or are they open to new ideas in education, and enthusiastic enough to experiment with the new? Or are they somewhere in between? You need to style your teaching accordingly. If you must innovate, only do so when you have gained your colleagues’ respect. You may be eager to get good student feedback, but if this ends up exposing your colleagues’ not-so-good teaching, you will have no-one to thank you. Instead, your excellent teaching may be cruelly re-cast as some form of dumbing down, or worse. On the other hand, there may be colleagues in your new department who are passionate about their education practice. If this is the case, get to know them, learn from them and partner with them in the noble goal of ensuring good departmental education practice.

Concluding remarks

How then should you conduct yourself in the first few months and weeks of your new academic career? Simple, if the department is conservative, then tread carefully, gain the respect of your colleagues and students, engage them in discussions on education practice. Think carefully through any changes that you wish to make, and establish a consensus amongst both students and academics alike. If you must make changes to the curriculum, or to your own education practice, then consider going first for those changes guaranteed to generate positive outcomes with high visibility. Then, as you win support, you can go on to implementing bolder changes. If, on the other hand, your department has a culture of innovation and excellence in education, then tuck in, and learn as much as you can, and studiously incorporate this learning into your own practice.

Either way, let the pursuit of excellence in education practice be your primary goal. Expand your networks with other like-minded engineering educators, both within your institution and elsewhere. Take every opportunity to learn about engineering education, and don’t shy away from teaching others as well. Be an engaged member of your department when it comes to matters relating to education and the student experience. Do this consistently, and over time, you will  become an integral member of both your  department and the world-wide community of engineering educators.  Then you will become established in higher education.