Interdisciplinary Engineering Education: Difficult, but not Impossible

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 21st 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 21st 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.


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.


Want to get started on PBL? Some instructional resources

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 21st 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. Instructional guide on Problem based Learning: Northern Illinois University, Faculty Development and Instructional Design Center – This is a short 4 page guide that explains what PBL is, how it differs from traditional teaching, and clearly spells out the roles of both the instructors (tutors) and students within a PBL environment.
    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. Book sections on Problem and Project Based Learning: Goodhew, Peter. “Teaching engineering.” The School of Engineering’s Active Learning Lab at The University of Liverpool(2010). In 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.

Rethinking the Engineering Mathematics end-of-year Exam

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 21st century. Hence, to ensure that the end-of-year exam remains relevant in the 21st century,  we have to ensure that a significant proportion of the exam questions is  pitched at the Group C / Constructive level.


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.


Adoption of Computational Modelling in Introductory Engineering Course Modules: A Case Study

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:


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.


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.


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.


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.


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.


Computational modelling and analysis, visualisation, undergraduate engineering education

Mathematical Resilience: The Holy Grail for Engineering Mathematics

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.


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.

Engineering education: Now is the time to change

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 21st 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.

Inaugurating a pioneer in engineering education research, Dr. Bill Williams

Professor Bill Williams and Professor John Heywood are, undoubtedly, two of the finest engineering educators that Europe has ever produced.

Ireland by Chance

img_2510 Bill’s workshop on getting published in EER

Thanksgiving Day had a different look and feel this year. Here in Dublin, we welcomed Dr. Bill Williams to give his inaugural lecture as Visiting Professor in DIT’s School of Multidisciplinary Technologies.

Bill is an energetic and knowledgeable colleague, a close friend, and an excellent mentor to me. We have been working together on various projects since the day we first met, at a SEFI conference in 2012. Bill hosted my 2013 visit to five universities in Portugal, and we are currently co-editing a special focus issue of the journal IEEE Transactions on Education, the second special focus issue we’ve organized together. Because Bill has been so helpful in supporting my development over the years, I wanted others at DIT to benefit from his knowledge, experience, and helpful advice as well. He’s got a can-do attitude that is uplifting and infectious. And so…

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Addressing the Engineering Skills Gap: Lessons from the SEFI 2017 Conference Proceedings

(Talk presented at the 6th Annual Symposium of the UK & IE Engineering Education Research Network at the University of Portsmouth, 01-02 Nov, 2018)


Since the turn of the century, employers have consistently expressed concern on graduate work-readiness in general, and a perceived lack of engineering and professional skills amongst engineering graduates in particular (Royal Academy of Engineering, 2007, Wakeham, 2016). This is despite efforts by engineering education providers to address these concerns. This study is an attempt to shed light on how individual engineering educators and institutions are addressing the perceived lack of engineering and professional skills amongst their students. It is hoped that findings from this study will stimulate sharing of best practice and hopefully foster sector-wide collaborative approaches to dealing with the problem.

For the purposes of this study, I used content analysis (Hsieh and Shannon, 2005) to review the contents of the abstracts of all the articles presented on the engineering skills thematic track at the SEFI 2017 conference. The European Society for Engineering Education, SEFI, is a network of engineering educators across Europe, and its annual conference is a key event that is highly regarded within the engineering education sector in Europe and beyond (European Society for Engineering Education (SEFI), 2017b). Given the profile of SEFI within Europe and beyond, the deliberations at SEFI conference can give an indication of the current state of engineering skills provision within European engineering education.

Purpose of this study

The purpose of this study was to achieve four objectives. First, I sought to identify the key engineering skills that are currently the focus of attention within the engineering education sector. Second, I wished to identify at which level of the engineering education curriculum that these engineering skills are being considered. Specifically, I wished to identify whether the focus of the authors was at pre-university level, undergraduate, masters, PhD or early career graduate level.

Third, I wished to establish the unit of strategic focus of engineering skills provision, specifically, whether it is at module level, programme level, departmental or institutional level. Given the hierarchical ordering of education provision within engineering, a preponderance of provision at module level would indicate that the primary driver for engineering skills provision is mainly the individual academic, whilst a preponderance of provision at programme level or higher would indicate a more strategic departmental and/or institutional approach towards the provision of engineering skills.

Finally, I wished to identify current and emerging trends in the provision of engineering skills within the engineering education curriculum. Given the continued perceived gap between the engineering skills expected of graduates by employers, and the actual skills that graduates actually bring with them into industry, this study will be of interest to a number of stakeholders in engineering education. These stakeholders include policy makers in government and industry, employers, providers of engineering education, individual academics and students.


Content analysis can be defined as “a research method for the subjective interpretation of the content of text data through the systematic classification process of coding and identifying themes or patterns” (Hsieh and Shannon, 2005, p.1278). In this analysis, I focussed on three items, namely the type of engineering skill(s) described in the article, the educational level where the skill is delivered, and third, the strategic level of focus, be it module, programme, departmental or institutional level. Using these categories, I reviewed all the abstracts of the 36 articles presented at the SEFI 2017 conference and published in the conference’s proceedings (European Society for Engineering Education (SEFI), 2017a) .

With regard to the type of engineering skills, I used an inductive process whereby I extracted and categorised the skill types as I went through the abstracts. As a result, I came up with a list of  ten categories which, amongst others, included communication skills, leadership and teamworking, employability/engineering and professional skills, innovation and/or  entrepreneurship, critical thinking, Industry 4.0 skills, problem solving and design skills.

Results and Findings

The 36 articles presented at the SEFI 2017 conference focussed on ten skill categories. Thirty-one of these articles reported on engineering skills education at undergraduate level, whilst three focussed on masters’ level. One other article was directed at improving employability and professional skills for doctoral students, whilst another looked at the acquisition of professional and engineering skills in the workplace by early career engineering graduates.

Six of the 36 articles focussed on the issue of engineering skills within engineering education curriculum in general. Of the remaining thirty articles, fourteen focussed primarily on module-level interventions, whilst nine looked at programme-level implementation of engineering skills education,  and  three focussed on the implementation of engineering skills across the entire engineering education provision of departments, faculties and institutions. The remaining four articles looked at the delivery of engineering skills by means of co-curricular projects.

The main focus of interest of the 36 articles was on employability and professional skills. This was followed closely by innovation and entrepreneurship skills. Together these four categories constituted almost 50% of the articles presented on the engineering skills thematic track at the SEFI 2017 conference. Critical thinking, problem solving, communication, leadership and teamworking skills were also covered extensively at the conference. Together, these skills were covered in 41% of the articles that were presented. In addition, English language competence was also cited as important engineering skill.  Generally, coverage of these skills at the SEFI 2017 conference is consistent with the range of skills that employers perceive to be lacking in recently graduated engineers (Royal Academy of Engineering, 2007, Wakeham, 2016).

The analysis of the SEFI 2017 conference abstracts relating to engineering skills also suggest the emergence of new skills that engineering academics are focussing on. For instance, two articles focus on the development of graduate engineering skills for the emerging 4th industrial revolution, Industry 4.0 (Lasi et al., 2014).  In addition, it appears that academics are beginning to pay attention to the development of appropriate pedagogies for delivering engineering skills within the curriculum (Kersten, 2018), as evidenced by the coverage of the topic by two of the 36 articles in SEFI 2017 conference proceedings.


Findings from this study indicate that whilst academics primarily focus on the delivery of the key skills that have been identified by employers as inadequate in current and past engineering graduates, some are beginning to look at the provision of engineering skills that may be required in future workforces. The study also suggests that curriculum interventions aimed at improving engineering skills are mainly carried out at the level of the module by individual academics. However, as some of the abstract contents suggest, the issue of engineering skills is increasingly being addressed at programme level or departmental and institutional level. This may therefore suggests that academic managers and leaders are increasingly paying attention to the issues pertaining to graduate skills and work-readiness that employers have been raising consistently over the past few years.


EUROPEAN SOCIETY FOR ENGINEERING EDUCATION (SEFI). Proceedings of the 45th SEFI Annual Conference 2017. In: ROCHA, J. C. Q. J. B. J., ed. 45th SEFI Annual Conference 2017, 2017a Azores, Portugal. SEFI — Société Européenne pour la Formation des Ingénieurs

EUROPEAN SOCIETY FOR ENGINEERING EDUCATION (SEFI). 2017b. SEFI [Online].  [Accessed 21 September 2018].

HSIEH, H.-F. & SHANNON, S. E. 2005. Three Approaches to Qualitative Content Analysis. 15, 1277-1288.

KERSTEN, S. 2018. Approaches of Engineering Pedagogy to Improve the Quality of Teaching in Engineering Education. In: DRUMMER, J., HAKIMOV, G., JOLDOSHOV, M., KÖHLER, T. & UDARTSEVA, S. (eds.) Vocational Teacher Education in Central Asia: Developing Skills and Facilitating Success. Cham: Springer International Publishing.

LASI, H., FETTKE, P., KEMPER, H.-G., FELD, T., HOFFMANN, M. J. B. & ENGINEERING, I. S. 2014. Industry 4.0. Business & Information Systems Engineering, 6, 239-242.

ROYAL ACADEMY OF ENGINEERING 2007. Educating engineers for the 21st Century. London: Royal Academy of Engineering.

WAKEHAM, W. 2016. Wakeham Review of STEM degree provision and graduate employability. London: Great Britain. Department for Business, Innovation & Skills


Teaching-only Academics in a Research Intensive University: From an undesirable to a desirable academic identity

Abstract of my Doctor of Education (EdD) dissertation – available at:

Teaching-only academics now constitute a significant proportion of the academic staff in UK higher education. This thesis is a three-part study in which I sought to contribute to a more indepth understanding of the teaching-only academic role. I did this through an investigation of the career trajectories, perceptions, work-related experiences and academic identity constructions of teaching-only academics working in a research-intensive institution in the UK.

In the first part of the study I carried out a systematic review of the literature on teaching-only academics in the UK, Australia and Canada. In the second part of the study I investigated the virtual identity of teaching-only academics at the UK research-intensive institution. I did this by undertaking an analysis of how these teaching-only academics self-represented and projected themselves on their institutional webpages. In the third part of the study I carried out a life-history analysis of senior teaching-only academics in the engineering faculty of the case study institution.

A principal finding from this thesis, which is collaborated across all the three parts of the study, is that the teaching-only academic role is a non-homogeneous role comprising individuals who come from different backgrounds, have followed different career trajectories into the role, and have different academic identities. Findings from this thesis also suggest that whilst teaching-only academics were introduced as an institutional response to the demands of the RAE/REF, the very act of creating the role has further exacerbated the separation between research and teaching, and between undergraduate and postgraduate teaching. Specifically, undergraduate teaching within the case study engineering department now tends to be the responsibility of teaching-only academics, with research-and-teaching academics increasingly focussing on research and postgraduate teaching. This separation has implications for research-led teaching, particularly in research-intensive institutions.

The thesis also reveals that despite the pre-eminence of research, teaching remains important within the university, and individuals on the teaching-only academic role are able to accumulate substantial, and valued, teaching-related academic capital. This capital, in turn, is enabling them to secure and advance their positions within the same institution, and to pursue career advancement through seeking employment in other higher education institutions.

The new A Level Mathematics Qualifications and their implications on Engineering Education

In every day 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.


DfE (2013). Reform of GCE A Levels – Letter from Secretary of State to Glenys Stacey. Available at: [accessed 25 August, 2018]

ALCAB (2014). Report of the ALCAB panel on mathematics and further mathematics. The A Level Content Advisory Board. [accessed 25 August, 2018]

DfE (2016). Content for mathematics AS and A level for teaching from 2017. Department for Education. Available at: [accessed 25 August, 2018]

Ofqual [2015]. AS and A Level Mathematics and Further Mathematics: Consultation on Conditions and Guidance. Ofqual. [accessed 25 August, 2018]

Ofqual [2016a]. GCE Subject Level Conditions and Requirements for Mathematics. Ofqual. [accessed 25 August, 2018]

Ofqual [2016b]. GCE Subject Level Guidance for Mathematics. Ofqual. [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. [accessed 25 August, 2018]