In this extra post, Anna Pilz discusses her participation in the Postgraduate Certificate in Academic Practice (PgCAP). Her assessment task for the option course on ‘Working with PGRs’ led her to develop a project for the Principal’s Teaching Award Scheme (PTAS) on ‘Co-Creating a New Development Needs Analysis for PGRs’ (January 2024-July 2025). Anna is an Academic Developer and Trainer in the Institute for Academic Development.

Soon after I had taken on a new role as Academic Developer at the Institute for Academic Development in autumn 2022, I enrolled on the PgCAP programme. I was motivated not only to gain professional accreditation via a Fellowship of the Higher Education Academy, but also to reflect on my teaching practice and pedagogical approaches within the context of my new role that involved designing and delivering training for postgraduate researchers and research staff across career stages.

One of my responsibilities in the role is to convene a three-week online course on “Getting Started with Postgraduate Research” for Master by Research and PhD students who are just at the beginning of their research degree journey. The PgCAP option course on ‘Working with PGRs’ therefore appealed to me. For the assessment, I chose to write a review report of the current use of Training Needs Analysis (TNA) at the University of Edinburgh to gain an understanding of current institutional practices within the context of sector-developments and scholarship on doctoral education.

Reviewing the use of Training Needs Analysis

One of the key elements of doctoral education is an emphasis on postgraduate researchers’ training and development. Within the Researcher Development Team, we had already identified the need to revisit the existing TNA. A TNA is a person-centred, reflective activity in form of a self-assessment, described by scholars as a key ‘pedagogical tool designed to assess doctoral researchers’ strengths as well as weaknesses’ (Elliot et al 2020, 149). Therefore, it is relevant right from the start and throughout the researcher degree journey by reflecting on strengths, development needs, intentions, and opportunities.

To understand current practices at Edinburgh and students’ experiences, I looked at the Postgraduate Research Experience Survey (PRES) from 2023. Although satisfaction with supervision was at 86.5% at the University of Edinburgh in the 2023 Postgraduate Research Experience Survey, the question regarding the extent to which the ‘Supervisor helps identifying training’ reached the lowest satisfaction rate (76.3%) out of the four questions put to PGRs. The PRES survey highlights the need to involve both PGRs and supervisors in the process of engaging in training, development, and career conversations.

This is also reflected in wider sector conversations, including in UKRI’s Economic and Social Research Council’s 2022 review on “Strengthening the Role of Training Needs Analysis in Doctoral Training”, which signals supervisors’ crucial role in enabling effective use of TNAs as a tool. For any Training Needs Analysis, then, resources and training needs to be considered for supervisors, too. As Adams et al (2022) recommend, it’s important to ‘set clear expectations for supervisory input’ and offer ‘training to support supervisors in navigating DNA conversations’. This may involve addressing unconscious biases about career goals as well as emphasising that development needs ought to be integrated throughout and not only at the start or the end of a research degree journey. My review thus concluded that any revised TNA and associated processes and resources needs to engage and focus on both user groups: PGRS and supervisors.

Shifting to a Development Needs Analysis

Postgraduate research students arrive at the University of Edinburgh with their own personal set of skills and aptitudes, cultural attitudes to learning, understandings of the Higher Education and research landscape, professional experiences, and have a variety of career aspirations. Scholars have proposed the productive concept of a ‘doctoral learning ecology’, which is based on the understanding that ‘the doctoral journey takes place simultaneously within and across several domains of learning, namely, discipline, institution, workplace, and the person’s lifeworld.’ (Elliot et al 2020, 148)

The ‘doctoral learning ecology’ invites a wider conceptualisation of development than the term ‘Training Needs Analysis’ allows. My review thus followed UKRI’s ESRC recommendation to adopt the term Development Needs Analysis (DNA) (Adams et al 2022). A good DNA equips PGRs for whatever direction they want to take during and after their research degree. Successful engagement with a DNA – both through reflective self-assessment and through conversations with supervisor(s) – can affect a sense of control, ownership, and empowerment for their career trajectories.

From report to cross-university PTAS project

Having shared the report with Fiona Philippi, Head of Researcher Development at the IAD, and – on her suggestion – with the Doctoral College Forum, I started to develop an application to the Principal’s Teaching Award Scheme. To improve PGRs’ satisfaction, enhance the student experience, and ensure the quality of doctoral education in line with sector developments, I wanted to co-create a new Development Needs Analysis for the University. With its wider professional and career emphasis, the new DNA would benefit from collaboration with the Careers Service, and Sharon Maguire (Assistant Director, Careers Service) was soon on board. To connect the DNA with institutional priorities, we also recruited Laura Bradley, Dean for Postgraduate Research in the College of Arts, Humanities, and Social Sciences.

Our new DNA must be applicable across disciplines. It’s been important to identify pilot communities that would allow us to test and evaluate the use of our new DNA by both students and supervisors. Tom MacGillivray, Co-Director of the Precision Medicine Doctoral Training Programme, and Kimberley Czajkowski, Graduate Officer in Classics in the School of History, Classics, and Archaeology, joined the project to pilot our DNA and resources in their respective contexts. With the team assembled, we drafted and submitted our PTAS application. The project was timely as it aligns with the University’s strategic priorities as set out in the 2024 Postgraduate Research Cultures Plan.

Following the successful outcome of the application, we recruited Majdouline El hichou, a PhD student in GeoSciences, as our Research Assistant. So, what all started as part of my own professional development, resulted in a project all about professional development, and created a professional development opportunity. You can read about how we established learning needs among both the PGR and supervisor communities, and why and how we co-created a draft of a new Development Needs Analysis in Maj’s blog, out next week.

References

Adams, Elizabeth, Scafell Coaching and Joanne Neary, Strengthening the Role of Training Needs Analysis in Doctoral Training (UKRI, Economic and Social Research Council, 2022). https://www.ukri.org/wp-content/uploads/2022/06/Strengthening-the-role-of-TNA-Report-April-2022.pdf.

Elliot, Dely L., Søren S.E. Bengtsen, Kay Guccione, and Sofie Kobayashi (2020), The Hidden Curriculum in Doctoral Education (Palgrave Macmillan).

The University of Edinburgh (2023), Postgraduate Research Experience Survey (PRES).

Anna Pilz

Dr Anna Pilz (she/her) is an Academic Developer and Trainer in the Institute for Academic Development at Edinburgh. She designs and delivers training for researchers across career stages ranging from 1:1 support to online resources and workshops. As a first-generation academic, she is passionate about building communities and aims to enable, facilitate, and encourage conversations about research processes in all their shapes, sizes, and forms. She inaugurated and leads on the University’s Researcher Realities initiative.

In this post, Prof Noel Entwistle introduces crucial insights gathered from his research into student learning dynamics at The University of Edinburgh. Exploring how teaching environments influence the study approaches of students, the findings reveal the pivotal role of teachers in fostering deep connections among concepts and enhancing overall comprehension. This post belongs to the Oct-Nov Learning & Teaching Enhancement theme: Engaging and Empowering Learning at The University of Edinburgh.

The university has been adjusting to the patterns of teaching and learning both before and since the experience of COVID, and during that process has drawn on ideas from many sources. Some of these came from research into curriculum design, ways of teaching, and the learning experiences of students. Much of this research, however, is hidden away in research journals and often makes the findings and their practical implications unclear for university teachers. I thought it might be useful to bring together a set of ideas couched mainly in everyday language, looking at teaching and student learning and influences on them. The research I have been involved in is mainly related to the academic aspects of student learning, but there are important links to be made with other aspects of the student experience.

Some years ago, an educational research project in this university (ETL Project) set out to explore the students’ experiences of teaching and learning in eight universities and four contrasting subject areas, and how different teaching-learning environments influenced students’ approaches to studying. The starting point was to find out what university teachers expected their students to have learned by the time they graduated. Obviously, there were very different specific answers across the disciplines, but most teachers seemed to be looking beyond ‘taken for granted knowledge’, towards the distinctive ways of thinking and practising in their discipline, crucial to using that knowledge. And, for this, students will need to become aware of the effects of their ways of studying.

Modes of thinking

Research had described two very different modes of thinking – deep and surface – but interviews with students showed that most of them also had a distinctive intention when approaching their studying. Students either adopted a deep approach, looking for a personal understanding, or a surface approach, geared to remembering knowledge in exams. This research gradually influenced teaching practice to put more stress on understanding and help students see how to develop a personal understanding of topics. For students, the nature of an academic understanding is often far from clear, but research interviews showed how visualisation could be used to bring ideas together into a satisfying personal understanding, as this final-year student did in her preparations for Finals.

Reading and re-reading and going to different sources of information, patterns become familiar, helping you make sense of new things that you haven’t met before, getting to see why this question is important while another one is not, or that this theory is more likely than another. Then, I [must] see the ideas in my visual space, according to how I know them, how I can picture them.  … In the end, I come to realise how everything is really related and I’m able to connect everything together and, when it comes, it is not as if I were looking for it – it just happens.

Other students also used mind-maps to organise their thinking before exams or writing an essay. Encouraging students to draw their own mind maps in a class or tutorial, followed by discussion among other students, helps them to see connections in developing an understanding. However, there are some theories or concepts that students find particularly difficult. These often involve important breakthroughs in thinking within a discipline, called threshold concepts. If students don’t grasp such ideas, they are likely to struggle with later parts of the course. This effect has implications for both teaching and course design in making sure such concepts are given sufficient teaching time and attention for students to make sense of what comes next in the syllabus.

Effects of teaching

Looking more broadly at recent changes in approaches to teaching, like those being introduced in this university, we see the importance of engaging students more actively in lectures and tutorials, with the involvement of tutors being particularly important. As a psychology student explained:

[The tutor] keeps my interest alive by presenting, not only the content, but also what matters for her. Experiences, personal understanding, knowledge – it’s all there. Teaching is about her relationship with the subject. Such tutors make me feel that studying this subject is worthwhile and I’m following her perspective to join in these explorations, to let my see, through her eyes, the issue at hand – a ‘meeting of minds’ perhaps?

Of course, a single extract from an interview cannot be convincing, just illustrative, but the ETL project also gave a lengthy inventory (scored questionnaire) to all the students taking part. Groups of items provided scores on five different experiences of teaching and three aspects of their own approaches to studying. These were then compared with self-ratings of the knowledge acquired and interest and enjoyment. ‘Knowledge acquired’ was shown to be linked with higher deep approach and lower surface approach scores, as expected, and there was a closely similar pattern for ‘interest and enjoyment’, suggesting that these aspects are equally important to the students. The following items show examples of what students were responding to.

• I was prompted to think about how well I was learning and how I might improve.
• We weren’t just given information; staff explained how knowledge is developed.
• This unit encouraged me to relate what I learned to issues in the wider world.
• Staff tried to share their enthusiasm about the subject with us.
• Staff were patient in explaining things which seemed difficult to grasp.
• Students’ views were valued in this course unit.

Bringing together more of the interview responses with the inventory findings provides support for the ways in which teaching and learning are being developed over the last few years in Edinburgh. Running out of blog space now… but it is important to keep in mind that the effects of teaching on learning depend on many other factors than those mentioned here, as can be seen in the mind map, below, while an article covering the same topics is available as complementary to this blog.

Image credit: author

Noel Entwistle

Noel Entwistle is Professor Emeritus of Education at The University of Edinburgh and previously was the Bell Professor (1978-2005). Before that he was Professor of Educational Research (1970-1978) at the Lancaster University. He is a Fellow of the British Psychological Society, the Scottish Council for Educational Research, and the Society for Research into Higher Education and has honorary doctorates from the Universities of Gothenburg and Turku. He was Editor of the British Journal of Educational Psychology and Co-ordinating Editor of Higher Education. He also served as President of the European Association for Research on Learning and Instruction.

In this post, Dr Vassilis Galanos continues his exploration of metrics, its place in Higher Education, and the impact of the Research Excellence Framework on our work practices. This post is part 2 of 3, and belongs to the Hot Topic theme: Critical insights into contemporary issues in Higher Education.

In a previous post with Teaching Matters, I have written about how academic excellence evaluations such as the UK’s Research Excellence Framework (REF), claiming to measure research quality with some kind of objective precision, can foreground the development of digital machinery (such as Generative AI) that is adjustable to the REF’s objective (or better: objectifying) metrics. In this post, continuing the thread from part 1, I will connect the REF to the context of broader student and faculty numerical rankings. REF, that for many academics also stands for “Rigorously Established Fear”, often ends up fostering a competitive environment where volume trumps substance and impact is staged in wording but often not grounded in practice. As an example of this, as part of the Edinburgh Futures Institute’s Data Civics Observatory, I encountered the frustration of local communities in Edinburgh who complained about researchers using their underdeveloped neighbourhoods to justify their grant allocation, but disappeared upon the project’s end.

Niche or curiosity-driven disciplinary-questioning endeavours get side-lined while churned-out, quota-meeting research takes centre stage, especially in the context of academic-industry collaboration. Such collaboration is initially phrased as an attempt to open-up the world of Academia into the real world, but, in practice, it transforms Academia itself into a peculiar type of industry. This mirrors the rise of performance indicators in corporate bureaucracies, which seek to optimise efficiency at the expense of innovation and creativity.

This obsession with optimisation and efficiency further increases the distance between metric-driven reporting as just a symbol and as practical social change (as Matthew Archer recently showed in his 2024 book ‘Unsustainable: Measurement, Reporting, and the Limits of Corporate Sustainability,’ or, as Stanislav Andreski beautifully put it in 1970, “evasion in the guise of objectivity”; “quantification as camouflage’; and ‘techno-totemism and creeping crypto-totalitarianism”).

As an individual progresses up the academic ladder from student to staff, the REF exercise takes the emotional place occupied by the marker’s assessment and staff mentor’s supervision as the higher and sufficiently invisible entity of surveillance. This mirrors Marx’s description of a factory, which, in our case, is the university (my additions in square brackets):

“The technical subordination of the workman [read: worker, but also student, lecturer, professor, etc] to the uniform motion of the instruments of labour [including marking schemes, impact assessments, article production, grant allocation mechanisms], and the peculiar composition of the body of workpeople, consisting as it does of individuals of both sexes and of all ages, give rise to a barrack discipline, which is elaborated into a complete system in the factory [and academia], and which fully develops the before mentioned labour of overlooking, thereby dividing the workpeople into operatives and overlookers, into private soldiers and sergeants of an industrial army. […] The place of the slave-driver’s lash is taken by the overlooker’s book of penalties [including late submission penalties, resits, redundancy of academics who did not produce REFable outcomes, and more]” (Marx 2013: 293).

In the next, and final, post of this three-part series, I will conclude this conversation by situating the emergence of Generative Artificial Intelligence (GenAI) within the afore-described process of metrics-oriented culture.

Vasileios Galanos

Dr Vassilis Galanos, SFHEA is a visitor at the Edinburgh College of Art and works as Lecturer in Digital Work at the University of Stirling. Vassilis investigates historico-sociological underpinnings of AI and internet technologies, and how expertise and expectations are negotiated in these domains. Recent collaborations involved the history of AI at Edinburgh, interrogations of generative AI in journalism (BRAID UK), artist-data scientist interactions (The New Real), and community-led regeneration interfacing with data-driven innovation (Data Civics). Vassilis has co-founded the AI Ethics & Society research group and the History and Philosophy of Computing’s (HaPoC) Working Group on Data Sharing, also acting as Associate Editor of Technology Analysis and Strategic Management.

In this post, Dr Vassilis Galanos dissects what metrics really mean for students, educators, and researchers in the wider academy. This post is part 1 of 3, and belongs to the Hot Topic theme: Critical insights into contemporary issues in Higher Education.

As the heading suggests, it’s not some Matrix-like virtual reality conspiracy controlling all things academic – it’s the metrics. For about 20 years now, from undergraduate student to Lecturer, I’ve experienced numbers like student grades, attendance monitoring points, seminar participation marks, journal rankings, research excellence frameworks (REF), and citation scores as structural elements we increasingly have to face, understand, and be assessed against. Yet, at the same time, we find ourselves being less outspoken about these metrics and what they mean for our daily lives.

Following a long legacy of bureaucratic solutionism, they’re supposed to streamline and improve academic management and recognition, but often end-up reducing the – supposedly – rich, varied experience of academia to a dry set of spreadsheets, impact factor badges, and transcript competitions.

As a person who studies the history of the internet in parallel with artificial intelligence (AI) (and an avid social media user myself, turning my life into an open experiment), I’ve seen the rise of social media metrics like ‘likes’, ‘follows’, and ‘faves’ being established as a “free-for-all” venue for numerical recognition. I have also seen how they further normalise our obsession with numbers, converging with the proliferation of AI and algorithmic technologies to amplify and entrench this metric-driven culture. When you add Generative AI into the mix, the metrics game shifts into hyper-drive with an efficiency that an Orwell-Huxley hybrid couldn’t have predicted.

For the past six months, I’ve spent time with Karl Marx’s The Capital, volume 1, so I decided to dissect what these metrics really mean, using insights from surveillance studies, Marxian economics, and the quantified self, with a nod to the history of numerical classifications from mathematics to economics. To complete the pun: from the Matrix, to metrics, to Marx.

Grades as assessment

Grades are the old standby for assessing students, neatly categorising their efforts and even identities into A, B, C, and “better luck next time.” Or, to use a term co-constructed by Hélène Cixous (1975, 1994: 29) and Jacques Derrida (1979: 97), they encapsulate the education of a phallogocentric system – one that is at the same time serving a masculine (phallocentric) ideal of military rankings and the dominion of rationality (reasoned logic as Logos, that is, logocentric). This creates a linear trajectory in which there is less space for winners and those in higher ranks.

Grading turns the wonderfully messy process of learning into bite-sized numbers, much like fast food turns diverse cuisines into generic meals – always with the opportunity to pay a bit more in order to have access to luxurious gastronomy. This simplification often strangles creativity and critical thinking. For the imaginative and divergent thinkers, it’s like being shoved into a production line where only uniformity gets rewarded.

The politics of such numerical simplification finds its roots back to the early applications of mathematics in standardising measurements for trade and commerce as well as military precision. Here’s Marx:

“The division of labour, as carried out in Manufacture, not only simplifies and multiplies the qualitatively different parts of the social collective labourer, but also creates a fixed mathematical relation or ratio which regulates the quantitative extent of those parts […]. It develops, along with the qualitative sub-division of the social labour-process, a quantitative rule and proportionality for that process” (Marx 2013: 241).

The presentation of presence

Attendance records act as the school’s hall monitor, ensuring students physically show up. Digital systems like biometric scans offer precise tracking but also inch dangerously close to a Big Brother type of oversight. This constant scrutiny is more than just checking who’s present – it’s a subtle method of enforcing compliance and cultivating a culture of stress and control. The evolution of such monitoring systems can be linked to the development of bureaucratic systems in the 19th century, which relied on statistical data to manage and control populations. Interestingly, this enforcement of being present in fear that attendance is being monitored, is transformed within social media environments into “fear of missing out” (FOMO).

The presentation of presence as something to compete for is an interesting parallel between (a) attendance monitoring as part of one’s entertainment/leisure lifestyle, and (b) the joy of education as an enforced evil that is effected only by attendance supervision. Marx again:

“An industrial army of workmen, under the command of a capitalist, requires, like a real army, officers (managers), and sergeants (foremen, overlookers), who, while the work is being done, command in the name of the capitalist. The work of supervision becomes their established and exclusive function” (Marx 2013: 230

(Keep in mind that the French word “surveillance” literally translates into “supervision” or “overseeing” – worth considering every time you have a “supervision meeting” with your dissertation supervisor or your line manager).

The power of citations

For faculty, journal rankings and citation metrics are the currency of the academic marketplace (as it is very precisely put in everyday vocabularies). Top-tier publications and a heap of citations bring career benefits like tenure and grants. But navigating this numbers game often means playing it safe, avoiding the unconventional or interdisciplinary work that might not score high on the metrics scale. This focus on numeric evaluation echoes the econometric models that gained prominence in the 20th century, emphasising quantifiable data over qualitative insights. As an extension of econometrics, the 20th century saw the evolution of bibliometrics, scientometrics, and infometrics, as a quantifiable measure of impact of research.

Compounding the issue, social media metrics like ‘likes’ and ‘followers’ further normalise academics’ predisposition towards popular, mainstream topics that satisfy the instantaneity of a present-oriented appreciation of science. This is often at the expense of deeper, more substantive inquiries, which extend into the past and future. Indeed, the academic culture behind creating ‘tweetable’ abstracts of abstracts (“threads”) after an attention-grabbing title that is meant to be retweeted indicates the time pressure under which scholarly content is produced, disseminated, and consumed – “content” in the recent social media flavour of the word.

In the next part of this Teaching Matters contribution, I will relate the question concerning metrics to the Research Excellence Framework (REF) exercise.

Vasileios Galanos

Dr Vassilis Galanos, SFHEA is a visitor at the Edinburgh College of Art and works as Lecturer in Digital Work at the University of Stirling. Vassilis investigates historico-sociological underpinnings of AI and internet technologies, and how expertise and expectations are negotiated in these domains. Recent collaborations involved the history of AI at Edinburgh, interrogations of generative AI in journalism (BRAID UK), artist-data scientist interactions (The New Real), and community-led regeneration interfacing with data-driven innovation (Data Civics). Vassilis has co-founded the AI Ethics & Society research group and the History and Philosophy of Computing’s (HaPoC) Working Group on Data Sharing, also acting as Associate Editor of Technology Analysis and Strategic Management.

In this post, Dr Avita Rath explores the topic of emotional labour, and its impact for those working in Higher Education. Avita is a year 3 distance learning student (MSc Clinical Education) at Edinburgh Medical School. This post belongs to the Hot Topic theme: Critical insights into contemporary issues in Higher Education.

Beyond the “Service Smile”

Behind the carefully crafted smiles, the reassuring words, and the meticulously managed demeanour lies a silent toll, a heavy burden we carry. In academia, we’re often expected to suppress our true selves, conform to a narrow definition of “professionalism,” and mask the real emotions that shape our experiences. This unseen, rarely acknowledged labour is a storm brewing beneath the surface of our work, threatening to drown us in a sea of burnout and exhaustion.

As sociologist Arlie Russell Hochschild (1983) famously coined it, emotional labour is the invisible work of managing our emotions to meet the demands of our job. It’s about regulating our feelings and expressions, often at the cost of our well-being.

While emotional labour has been studied extensively in various fields, it’s often overlooked in higher education. Why? Because we tend to conflate it with professionalism, viewing it as a natural and expected part of the job rather than a form of labour that needs to be recognised and compensated. This is particularly true in the context of marketisation, which has transformed universities into “service institutions,” where academics are increasingly expected to cater to the needs of students and stakeholders.

This “professionalism,” however, can be a double-edged sword. It often involves suppressing genuine emotions and conforming to a set of unrealistic expectations. As Ogbonna and Harris (2004) noted, the “professional” persona academics are expected to project can create a “gap” between their true selves and their public performances.

A personal journey and a broader truth

As a neurodivergent woman in a predominantly Asian academic setting, I’ve experienced this gap firsthand. The cultural taboo surrounding emotional expression, particularly for women, combined with the pressure to conform to a narrow definition of “professionalism,” created a sense of alienation and isolation. I often felt like I was performing a role, hiding my true self behind a carefully constructed mask.

Imagine, for a moment, the demanding life of a dental academic like myself, or any academic for that matter: you’re expected to be a skilled clinician, a mentor, a teacher, a researcher, and a leader – all at once. This constant pressure to excel in multiple areas fuels the need for emotional management, often at a significant cost.

This emotional strain is not simply a personal experience. It’s a pervasive issue within academia. A recent Nature poll and HEPI policy papers (Forrester, 2023; Morrish, 2021) found that 67% of academics are burned out, with counselling and occupational referrals rising by more than 100% over the past two years. This suggests that emotional labour is not just a personal challenge but a systemic problem within academia that affects our wellbeing and our ability to thrive. Moreover, the rise of “quiet quitting” – where academics are disengaging from their work by reducing their output and limiting their involvement – is another alarming sign of the impact of emotional labour in academia.

As Shuler (2007) aptly points out, “[as] scholars and practitioners… we often write as if WE are not also engaging in emotional labor” (p. 255). This is the core of the issue. Emotional labour is often seen as an intrinsic part of “caring” professions (Grandey et. al., 2013), yet it’s rarely acknowledged or valued. It is treated as an expected part of the job rather than a form of labour that needs to be recognised and compensated.

The consequences of ignoring this ‘work’ in academia appear to be significant. It can lead to burnout, decreased job satisfaction, and even mental and physical health problems that eventually affect the quality of teaching and student well-being (Berry & Cassidy, 2013; Abery & Gunson 2016). We need to change how we think about emotional labour, recognise its impact, and stop this cascade of worrying reactions.

Moreover, as Bellas and Krupnick (2007) found, this burden is disproportionately weighted on women. Women are often socialised to be more emotionally expressive and nurturing. These societal expectations can lead to a “double bind” for women in academia, who are expected to be both caring and competent but are often penalised for displaying their genuine emotions. This double bind is further intensified for neurodivergent women in academia, who may face additional pressures and stigmas due to the often pervasive cultural taboos against neurodiversity.

A call to action: Valuing emotional well-being in academia

To create a more sustainable and equitable academic environment, we need to:

• Acknowledge emotional labour: Universities need to openly acknowledge the emotional labour that academics undertake and recognise its importance in the performance of educators.
• Promote well-being: Universities should offer programs and workshops focusing on emotional intelligence, self-care, and academic stress management.
• Foster open dialogue: Encouraging open communication and fostering a culture of mutual respect and understanding among faculty members can create a more supportive environment that helps to alleviate emotional distress.
• Reduce administrative burdens: Universities should strive to reduce the administrative burden on academics, allowing them to focus more on teaching and research.
• Embrace neurodiversity: Universities should actively promote neurodiversity and create a more inclusive and supportive environment for neurodivergent academics.

Embracing change for a “hopeful” future

We can move towards a future where academic institutions recognise the human cost of emotional labour. A future where universities prioritise the emotional well-being of their faculty, creating a more supportive and inclusive environment for all. A future where we can move beyond the “service smile” and embrace the full range of our emotions, bringing our authentic selves to our work.

This future is within reach. By demanding change, fostering a more empathetic and compassionate approach to academia, and advocating for a world where emotional labour is recognised, valued, and addressed, we can begin to create a more just and equitable academic environment.

References

Abery, B., & Gunson, C. (2016). This paper applies Berry and Cassidy’s Higher Education Emotional Labour model to the management of extension requests in a short space of time in a large, first year Health Sciences topic. International Journal of Allied Health Sciences Education, 6(1), 22–26.

Bellas, M. L., & Krupnick, C. G. (2007). The Costs of Caring: Examining the Relationship Between Gender, Emotional Labor, and Burnout. Journal of Women and Social Work, 22(4), 381-395.

Berry, K., & Cassidy, S. (2013). Emotional Labour in University Lecturers: Considerations for Higher Education Institutions. International Journal of Curriculum and Teaching, 2, 1-21.

Forrester, V. (2023). Fed up and burnt out: ‘quiet quitting’ hits academia. Nature, 615, 751-753.

Grandey, A., Rupp, D. E. & Diefendorff, J. 2013. Emotional labor in the 21st century: diverse perspectives on the psychology of emotion regulation at work, Routledge.

Morrish, L. 2021. Emotional Labour in the Post-Pandemic Academy. Available from: https://postpandemicuniversity.net/2021/10/31/emotional-labour-in-the-post-pandemic-academy/.

Ogbonna, E., & Harris, L. C. (2004). Work Intensification and Emotional Labour among UK University Lecturers: An Exploratory Study. Organization Studies, 25, 1185-1203.

Shuler, S. (2007). Autoethnographic Emotion: Studying and Living Emotional Labor in the Scholarly Life. Women’s Studies in Communication, 30, 255-283.

Avita Rath

Dr Avita Rath is a year 3 distance learning student (MSc Clinical Education) at Edinburgh Medical School, The University of Edinburgh. She is also a senior lecturer, academic coordinator and periodontist at the Faculty of Dentistry, SEGi University, Malaysia. She is a Common Wealth scholar, a Fellow in Advance Higher Education, UK (FHEA), and an Association of Medication Education in Europe (AMEE) member. Some of her research interests include equity, diversity and inclusivity issues in health professional education, mindfulness in dental education, and student engagement concepts. . She would like to thank Professor Gill Aitken, her Master’s supervisor, without who she would never been acquainted of this ‘invisible’ work that led to this blog post.