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Learning analytics won’t close awarding gaps. Better teaching will.

by David Mather

Universities have invested heavily in understanding awarding gaps. We know more than ever about differential outcomes between student groups. We have sophisticated learning analytics platforms, increasingly accurate predictive models and growing confidence in our ability to identify students who may disengage, withdraw or underperform. This is important work, as it has helped institutions move away from reactive support towards earlier and more informed intervention.

Despite this, awarding gaps remain stubbornly persistent. Perhaps that is because we have spent the last decade using data to identify students rather than improve teaching. If we have become better at identifying students who are likely to struggle, why have we made less progress in changing the outcomes we are trying to improve?

Perhaps the answer lies in the purpose we have given our data. Learning analytics has become one of higher education’s greatest success stories. Universities now know more about their students than at any point in the sector’s history. Attendance, engagement with virtual learning environments, assessment patterns and a range of demographic indicators provide increasingly sophisticated pictures of student behaviour (Romero & Ventura, 2024). Most institutions now use this information to answer a single question: who is most likely to struggle?

It is an important question, but I am no longer convinced it is the most educational one.

Learning analytics has become remarkably good at supporting institutional decision making. It helps us identify educational risk, target student support and improve continuation. However, prediction is not pedagogy. Identifying students who may struggle is not the same as understanding how teaching might change their trajectory. Awarding gaps are not created by dashboards. They emerge through complex interactions between curriculum, assessment, expectations, recognition, belonging and students’ experiences of learning. They will not be closed by increasingly sophisticated prediction alone. Therefore, the assertion of this article is simple: better teaching closes awarding gaps. The role of data should be to improve teaching rather than improve prediction.

The distinction that I have presented became clear to me long before I joined higher education. My career began in further education, where I taught and led in sixth form colleges. Like many colleagues working in that sector, I became accustomed to using ALPS, a value added system that compares students’ outcomes with those expected from their prior attainment (ALPS Education, 2025). The numbers themselves were rarely the most interesting part of the conversation – the pedagogic conversations they created were.

Course teams did not gather simply to identify students who might miss their target grades. They asked why one assessment had produced stronger outcomes than another. They debated whether curriculum sequencing was helping students build confidence. They compared approaches to feedback and questioned whether different teaching strategies had influenced progress. Data became a catalyst for professional dialogue. It encouraged teachers to think critically about pedagogy rather than simply monitor performance.

One observation emerged repeatedly. Students at either end of the attainment spectrum were visible. Those achieving well continued to receive stretch and challenge. Those facing significant barriers rightly attracted additional support. However, those in the middle of those two poles often received much less attention. These were students who attended lessons, completed assignments and achieved respectable grades. They were progressing, but they had begun to plateau. Their potential remained largely hidden because nothing within the data suggested they required intervention.  I have become increasingly convinced that higher education contains the same group.

Earlier this year I described those between the two poles as the silent middle (Mather, 2026a). My argument then focused on belonging. Universities have become increasingly effective at identifying students who excel and students who experience significant challenge. Those in between often remain unnoticed because they continue to progress through their programmes without attracting concern. Progress, however, should never be mistaken for fulfilled potential. My earlier articles have led me to a conclusion that the silent middle is not simply a group of overlooked students – they reveal something more fundamental about the questions we ask of our data.

Learning analytics tells us who is likely to struggle. It tells us much less about where teaching is most likely to make the greatest educational difference. Those are not the same question. One helps universities manage institutional risk. The other has the potential to improve educational practice. The distinction is significant because universities cannot close awarding gaps through prediction alone. They will close them by improving teaching, strengthening relationships and creating learning environments in which more students can succeed. Learning analytics has an important role to play in that ambition, but only if we stop asking it to tell us merely who needs support and start asking what it can teach us about teaching itself. The question is not whether universities should continue to invest in learning analytics (they should). The question is whether we have become too comfortable allowing learning analytics to define the educational problem.

For the past decade, learning analytics has largely been framed as an exercise in prediction. Institutions seek to identify students who may withdraw, fail or disengage so that support can be targeted before those outcomes become inevitable (Romero & Ventura, 2024). This represents an advance over retrospective approaches that identified concerns only after students had already fallen behind. Early identification and timely-bound support are crucial. The sector should not lose either.

However, prediction is not an educational strategy. Prediction tells us which students are most likely to experience difficulty. It tells us very little about what educators should do differently. A dashboard cannot redesign a curriculum, improve formative feedback, strengthen relationships between students and staff or create cultivate belonging. All of those are fundamentally pedagogical acts.

Awarding gaps emerge through pedagogy as much as they emerge through student characteristics. Curriculum design, assessment practices, expectations, feedback, representation and relationships all influence whether students flourish or merely progress. All of this means that learning analytics should be judged not simply by its ability to predict outcomes, but by its ability to improve teaching.

The distinction between prediction and teaching quality has profound implications for educational leadership. Every university leader understands that resources are finite. Personal tutors, learning developers, academic skills teams and lecturers cannot provide intensive support to every student throughout the academic year. Decisions therefore have to be made about where educational effort should be concentrated. Those decisions are often informed by indicators of risk. Students who stop attending, miss assessments or disengage understandably receive attention. That is appropriate and necessary, but I wonder whether we have overlooked another question: where is teaching most likely to change a student’s trajectory?

That question shifts the purpose of learning analytics completely. Instead of asking who is most likely to fail, we begin asking where educational opportunity exists. Instead of identifying institutional risk, we begin identifying pedagogical opportunity. Those are not competing priorities – they are different ways of thinking about educational gain.

My experience in further education suggests that this distinction is crucial. ALPS data did not conclude conversations about attainment – it started them. Teachers debated why one class had responded differently from another, why particular assessment approaches appeared more effective and which students seemed to benefit most from timely feedback or increased challenge. Data did not replace professional judgement – it sharpened it. Higher education has an opportunity to claim that mindset.

Freire (1970/2005) argued that education is transformative because it encourages dialogue, reflection and action rather than passive acceptance. His critique was directed at pedagogy, yet it also offers a useful way of thinking about educational data. Learning analytics should not become another form of educational banking in which institutions accumulate information without fundamentally changing practice. Data has educational value only when it changes the conversations educators have about teaching.

Addressing data for the purpose of informing pedagogy and practice should intersect with the conversation about awarding gaps. Universities have invested considerable effort in understanding differential outcomes across student groups. That work has transformed institutional awareness of structural inequalities and rightly challenged assumptions that degree classifications simply reflect individual effort or ability. Research has consistently demonstrated that awarding gaps emerge through complex interactions between curriculum, assessment, belonging, institutional culture and wider social structures rather than any single deficit within students themselves (Mountford-Zimdars et al, 2015). Addressing those gaps therefore requires sustained institutional commitment.

The institutional commitment I refer to is where the silent middle re-enters the conversation. Those in the silent middle are rarely identified as requiring support because they continue to engage, submit work and progress through their programmes (Mather, 2026a). They do not generate alerts because they are doing precisely what institutional systems expect them to do. Many achieve marks in the middle classifications throughout their degrees. They are neither failing nor flourishing – they simply continue. My question is: do our data encourage us to notice them? 

I suspect they do not. As such, the consequence is that some of the students with the greatest capacity for educational gain receive the least pedagogical attention. Universities rightly identify students whose continuation is at risk. We devote much less effort to identifying students whose attainment could be transformed through relatively modest changes to teaching, feedback or academic relationships.

Educational gain is not distributed evenly across a student population. Some learners require substantial pastoral support simply to remain engaged, whilst others may experience significant academic development following one timely conversation, carefully designed formative feedback or an increased sense of belonging within their discipline. If learning analytics cannot help us distinguish between those different forms of educational opportunity, then we are asking increasingly sophisticated systems to answer significantly limited questions. Perhaps the challenge facing higher education is not to build better dashboards – perhaps it is to ask more educational questions of the data we already possess.

The argument I have made in this article is not that universities should invest less in learning analytics. Institutions should continue to develop their analytical capability because understanding students’ experiences is crucial if they are to be enhanced. However, my concern is that we have become so focused on prediction that we have paid insufficient attention to pedagogy. Learning analytics has become exceptionally good at telling us which students are most likely to struggle. It has been much less successful in helping us understand how teaching itself should change. If universities genuinely want to reduce awarding gaps, I contend that we should ask different questions of our data.

First, what do the data tell us about our teaching rather than our students? Much of our current use of learning analytics encourages us to examine student behaviour. Attendance, engagement and submission patterns become indicators of individual risk. Those measures have value, but they tell us little about the effectiveness of our curriculum, assessment design or feedback practices. Every dashboard should prompt course teams to ask whether the learning environment itself requires attention before concluding that the student does.

Second, where is the greatest opportunity for educational gain? Universities have become highly effective at identifying educational risk. We should become equally interested in identifying educational opportunity. Some students require substantial pastoral intervention to remain engaged. Others may transform their academic performance following timely feedback, clearer expectations or a stronger sense of belonging. Educational gain is not distributed evenly across a student population. Learning analytics should help us understand where relatively modest pedagogical changes are likely to have the greatest impact.

Third, who have we stopped noticing because they continue to pass? The silent middle is not simply a descriptive label. It represents a challenge to institutional assumptions about success (Mather, 2026a). Students who consistently achieve marks in the middle classifications rarely appear within institutional dashboards because they generate little concern. Their continued progression can disguise unrealised potential. Universities that are serious about improving outcomes should become just as interested in students who have plateaued as those whose continuation is at risk.

Fourth, what should we teach differently tomorrow because of what the data tell us today? This is the most important question of all. Learning analytics should not conclude a conversation, it should start one. If programme teams leave meetings with a list of students to contact but no discussion about teaching, assessment or curriculum, then the data has served an administrative purpose rather than an educational one. Professional judgement remains the most powerful tool available to educators. Analytics should strengthen that judgement, not replace it.

Finally, how will we know whether our teaching has changed outcomes? Universities devote considerable resources to measuring continuation, progression and degree classifications. Those metrics are important, but they should not become proxies for educational quality. We should also ask whether students have become more confident learners, whether they participate more actively, whether they experience a greater sense of belonging (on their terms) and whether teaching has enabled them to exceed the trajectories that seemed likely at the beginning of their studies. Those are educational outcomes. They deserve to be measured with the same seriousness as institutional performance indicators.

None of the questions I have posed requires higher education to adopt a system such as ALPS. Schools, sixth form colleges and universities pursue different purposes, educate different learners and operate within different frameworks. The lesson is not the system itself – it is the educational philosophy that underpins it. Freire (1970/2005) argued that education becomes transformative through dialogue, reflection and action. The same principle should apply to learning analytics. Data should not become another mechanism for classifying students or managing institutional performance – it should encourage educators to reflect critically on their practice, challenge assumptions about learning and act in ways that improve students’ educational experiences.

My own work over the past year has explored transition, belonging, good teaching and the silent middle (Mather, 2026a, 2026b). This article has helped me realise that these are not separate conversations; they point towards the same conclusion: students succeed because teaching matters. Relationships matter, recognition matters, belonging matters and curriculum matters. Learning analytics only become educationally significant when they help us improve all of those things.

David Mather is Associate Head of School (Students) and Senior Lecturer in Educational Leadership and Management at the University of Portsmouth. His research explores identity, recognition, and transition across educational and professional contexts, with a particular interest in improving educational practice and the student experience. He is the founder of We See You, an initiative supporting Armed Forces, veteran, and Blue Light communities in higher education.


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Teaching in higher education: Connected practice for changing times?

by Karen Gravett and Simon Lygo-Baker

Why does teaching matter, and how might we understand what it means to teach in higher education, in contemporary times? This blog introduces our new book Reconceptualising teaching in higher education, published by Routledge. The book is created from our own reading, research, ideas, and practice as two academics working in the field of higher education, where we have been teaching for over twenty years in universities in the UK, America and Australia. It was inspired by our thoughts and discussions surrounding what it means to teach and the joys, pleasures and challenges that accompany our role.

At present there are many questions regarding higher education, its purpose and possible futures. For teachers too, questions remain regarding the necessity and shape of teachers’ contributions in a marketised sector mediated by artificial intelligence and pinched by precarity. And yet, this book is underpinned by our continued belief that teaching matters. We believe that meaningful teaching matters for our students, for our own development and experiences as educators, and for the futures of universities themselves. We argue that teaching provides opportunities for meaningful learning which matters for personal growth and for the development of knowledge. We believe that meaningful learning matters for the creation of new opportunities and possibilities. As bell hooks (1994) explains, fundamentally, education is about ‘the practice of freedom’. In a world where perhaps the experiences we have, the products we purchase, and the information we consume may not always seem meaningful, we believe that the connections that happen when we learn and when we teach have a power that should be harnessed and celebrated. Education matters, because not only does it open doors, but it allows us to recognise them and frame them for ourselves, offering the opportunity to challenge and evolve.

The book is designed for anyone seeking to develop their role as teachers in contemporary universities. This includes new teachers as well as those of us who still have questions and are still keen to develop and respond to our changing times. Specifically, it asks us to rethink our role and the directions we typically follow and suggests the need to disrupt these and to rethink our role as teachers, to take a different path, talk to someone new, or see things a different way. Viewing higher education from new positions can help us to reimagine our role and discover or reclaim the pleasure of teaching. 

To do this, our book challenges the traditional view of teaching as an individual act. Instead, it frames teaching as a relational and situated practice, built on connections with others. Secondly, we explore teaching as an affirmative and emotional endeavour that can inspire others and lead to joyful and generative moments of connection. Lastly, the book positions teaching as a critical practice, where educators are encouraged to embrace uncertainty, question assumptions, and let their approaches evolve. These ideas are all interwoven with practical insights into contemporary areas of practice, including assessment, learning spaces, feedback, digital education, artificial intelligence, learning design, belonging and inclusion, to develop ethical and relational pedagogic approaches. Specifically, the last chapter examines a wide range of key issues, for example feedback frustrations or student engagement, in order to examine how as a reader you might be able to develop approaches and ideas that work for you in responding to some of these challenges. We hope that readers will find the book useful and look forward to continuing conversations around what it means to teach in changing times.

References

Gravett, K and Lygo-Baker, S (2026) Reconceptualising teaching in higher education: Connected practice for changing times Routledge

hooks, b (1994) Teaching to transgress: Education as the practice of freedom Routledge

Dr Karen Gravett is Associate Professor of Higher Education, and Head of the Surrey Institute of Education at the University of Surrey, UK, where her research focuses on the theory-practice of higher education. She is Executive Editor for the journal Teaching in Higher Education, and a member of the editorial board for Learning, Media and Technology. Karen’s latest books are: Gravett, K and Lygo-Baker, S (2026). Reconceptualising teaching in higher education: Connected practice for changing times, Gravett, K (2025) Critical Practice in Higher Education, and Gravett, K (2023) Relational Pedagogies: Connections and Mattering in Higher Education.

Simon Lygo-Baker works as a Senior Lecturer in Clinical Education at King’s College London and has previously worked in the University of Wisconsin, USA and the University of Surrey, UK. He has previously worked on developing curricula with refugees, asylum seekers and other socially excluded groups, as well as working for a number of years in academic development.


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From ad hoc to constructive: the ABC levels of GenAI integration in business education

by Qianqian Chai and Xue Zhou

Introduction – the challenge of GenAI integration in business education

Since the release of ChatGPT, Generative Artificial Intelligence (GenAI) has rapidly entered higher education. Business schools, with their strong ties to industry and emphasis on applied skills, provide a particularly important setting for examining GenAI’s role in curriculum design. Yet, while adoption has expanded quickly, the educational outcomes of GenAI integration have not been consistent (Kurtz et al, 2024). Across cases, educators identified both benefits and risks, including engagement, skills development, and overreliance. This unevenness suggests that rather than reflecting a single trajectory of adoption, early practice appears to involve different approaches to integration. The central issue is not whether GenAI is used, but how different approaches shape outcomes.

This blog draws on our recent study of GenAI integration in business modules at a UK Russell Group university (Zhou et al, 2026). Through a qualitative analysis of 17 educator cases across 24 modules, we examined how GenAI was incorporated into curriculum design, and how different approaches were associated with distinct benefits and challenges. Using the lens of constructive alignment (Biggs, 1996), we identified three patterns of integration: ad hoc, blended, and constructive, which together form the ABC levels for understanding how GenAI is integrated into curriculum practice. We use these levels to explore why some approaches appear more educationally effective than others. In particular, this blog will offer research-informed insights into how GenAI can be integrated more effectively and sustainably in business higher education. While the cases are drawn from business education, the patterns identified and principles of constructive integration have wider relevance across disciplines where GenAI is increasingly embedded in curriculum design.

ABC levels of GenAI integration in the business curriculum

Our analysis identified three levels of GenAI integration: ad hoc, blended, and constructive. Table 1 outlines these distinctions across key dimensions.

Table 1 ABC levels of GenAI curriculum integration

Constructive integration represents a qualitatively different approach, grounded in constructive alignment, where intended learning outcomes, teaching activities, and assessment are deliberately designed to develop and evaluate students’ ability to use GenAI critically and effectively. At this level, GenAI is not an optional or supporting tool, but an integral component of disciplinary learning, with a clear pedagogical purpose and coherent role across the curriculum.

By contrast, ad hoc integration is characterised by occasional and isolated use, where GenAI is introduced as an optional or experimental tool without being planned into the broader curriculum design. Blended integration moves beyond this by incorporating GenAI into selected learning activities or tasks, giving it a more purposeful pedagogical role, but its use remains only partially embedded. Both approaches therefore fall short of the coherence and strategic alignment that define constructive integration.

The distinction between these patterns is therefore not simply a matter of more or less GenAI use, but of how GenAI is positioned within the curriculum: as an experiment, as a support, or as a capability to be deliberately developed. Although developed from business education contexts, this typology offers a lens that can be applied more broadly to understand how GenAI is positioned within different disciplinary curricula.

Why constructive integration matters

Across the cases, GenAI integration generated benefits and challenges across students, educators, and institutions. At the student level, reported benefits included stronger engagement, confidence, and employability-related skills, while the main risks centred on overreliance, inequality, ethical concerns, and ineffective outputs. For educators, benefits included efficiency gains, professional learning, and improved teaching performance, but these were accompanied by increased workload and the need to redesign activities and assessments. At the programme level, GenAI enhanced curriculum relevance but raised concerns about academic standards.

Figure 1 shows that these benefits and challenges were not distributed evenly across the three patterns of integration. Constructive integration displayed the strongest and broadest benefits, while ad hoc and blended approaches showed narrower gains alongside more exposed challenges. In other words, the issue is not whether GenAI brings value or risk, but how curriculum design shapes the balance between them.

Figure 1 Trade-offs of GenAI integration: challenges (red) vs benefits (green)

What makes constructive integration different is not the removal of challenge, but the stronger presence of educational value. In the study, constructively integrated cases were linked more clearly to student engagement, capability development, employability, and curriculum relevance because GenAI was embedded through aligned outcomes, activities, and assessment, rather than added on as a tool or support. Importantly, these cases also showed stronger educator development, including pedagogical reflection and confidence, despite workload pressures. This suggests constructive integration enhances both student outcomes and educator learning by embedding AI within coherent curriculum design.

How constructive integration is achieved

Table 2 presents examples from the modules in this study, showing how GenAI was constructively integrated into existing pedagogical strategies without requiring curriculum redesign.

Table 2 Constructive GenAI Integration into Existing Pedagogical Strategies

Taken together, the cases suggest several practical principles for integrating GenAI more coherently within the curriculum. These principles are not specific to business education, but reflect broader curriculum design considerations that can be adapted across disciplines with different pedagogical traditions.

  • Integration builds on existing pedagogical strategies: GenAI should be embedded within approaches already familiar to the discipline, such as project-based or simulation-based learning, without requiring curriculum redesign (Chugh et al, 2023).
  • Sharpen the role of GenAI by disciplinary purpose: In different contexts, GenAI supported strategic analysis, research and synthesis, reflective thinking, or data interpretation. Its value depends on alignment with module aims (Zhou & Milecka-Forrest, 2021).
  • Make AI use purposeful through assessment and evaluative tasks: In stronger cases, GenAI was connected to tasks that required students to interpret, justify, compare, or critique AI-supported outputs, rather than simply using AI to complete tasks (Biggs & Tang, 2010).
  • Support deeper student engagement through scaffolding: Structured guidance, such as prompting strategies, comparison activities, and reflective tasks, enabled more critical and purposeful use (Cukurova & Miao, 2024).

Overall, constructive integration is less about introducing new tools than about redesigning existing curriculum elements so that GenAI is meaningfully aligned with disciplinary learning.

Conclusion

The ABC levels developed in our study show that GenAI integration in business education does not follow a single trajectory but ranges from ad hoc and blended use to constructive integration. The key difference lies in approach: constructive integration embeds GenAI through aligned outcomes, activities, assessment, and scaffolding. The challenges observed across GenAI integration practices suggest an urgent shift from ad hoc GenAI integration toward strategic and constructive integration in business education. In this way, higher education can support students’ employability and capability development, strengthen educators’ professional and pedagogical confidence, and enable institutions to sustain coherent, future-facing curricula.

Dr Qianqian Chai is a Lecturer in Business and Management at Queen Mary University of London and Chair of the AI in Education Innovation Sub-committee in the School of the Arts. Her research focuses on AI in higher education, including curriculum design, academic integrity, and policy. q.chai@qmul.ac.uk

Professor Xue Zhou is a Professor in AI in Business Education and Dean of AI at the University of Leicester. Her research interests include digital literacy, digital technology adoption, cross-cultural adjustment, and online professionalism. xue.zhou@le.ac.uk


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Reclaiming the academic community: why universities need more than metrics

by Sigurður Kristinsson

For decades, talk of “the academic community” has flowed easily through mission statements, strategy documents, and speeches from university leadership. Yet few stop to consider what this community is or why it matters. As universities increasingly orient themselves toward markets, rankings, and performance metrics, the gap between the ideal of academic community and the lived reality of academic work has widened. But this drift is not merely unfortunate; it threatens the very values that justify the existence of universities in the first place.

This blog explores why academic community is essential to higher education, how contemporary systems undermine it, and what a renewed vision of academic life might require.

What do we mean when we talk about “community”?

The word “community” can be used in two different senses. One is descriptive: communities are simply networks of people connected by place, shared interests or regular interaction. From this sociological standpoint, academic communities consist of overlapping groups (faculty, students, administrators, service professionals) brought together by institutional roles, disciplinary identities, or digital networks, perhaps experiencing a sense of belonging, solidarity, and shared purpose.

But in debates about the purpose and future of universities, “community” is often used in a normative sense: an ideal of how academics ought to relate to one another. In Humboldtian (1810) spirit, contemporary advocates like Fitzpatrick (2021, 2024) and Bennett (1998, 2003) envision academic community as a moral and intellectual culture grounded in shared purpose, generosity, intellectual hospitality, mutual respect, and the collective pursuit of knowledge. From this philosophical perspective, community is not just a cluster of networks to be analyzed empirically but a normative vision of how scholarly life becomes meaningful. This aspirational view stands in stark contrast to the conditions shaping many universities today.

The pressures pulling academic life apart

For several decades, developments in universities around the world have been hostile to academic community. While the precise mechanisms vary, academics report strikingly similar pressures: managerial oversight, performance auditing, intensifying competition, and the steady erosion of collegial structures and shared governance. Five threats to academic community are particularly worrisome:

Organisational (not occupational) professionalism

In her analysis of how managerial logic has co-opted the language of professionalism to justify top-down control in public institutions, Julia Evetts (2003, 2009, 2011) introduced a distinction between occupational and organisational professionalism. Occupational professionalism in academia implies membership in a self-governing community of experts committed to serving society through knowledge. Today, however, universities increasingly define professionalism in organisational terms: compliance with targets, performance indicators, and standardised procedures. The result is a hybrid system: academics retain some autonomy, but it is overshadowed by bureaucratic accountability structures that fragment communal relationships and discourage collective responsibility (Siekkinen et al, 2020).

Managerialism

Managerialism prizes measurable production outputs, standardized procedures, and vertical decision-making. As Metz (2017) argues, these mechanisms degrade communal relationships among academics as well as between them and managers, students, and wider society: decisions are imposed without consultation; bonus systems reward narrow indicators rather than communal priorities; and bureaucratic layers reduce opportunities for collegial dialogue. Managerialism replaces trust with surveillance and collegial judgment with quantification.

Individualism

The rise of competition – over publications, grants, rankings, and prestige – has amplified what Bennett (2003) called “insistent individualism.” Colleagues become rivals or useful instruments. Achievements become personal currency. In such settings, it is easy to see oneself not as part of a community pursuing shared goods but as an isolated producer of measurable outputs. This ethos erodes the solidarity and relationality necessary for any robust academic culture.

Retreat from academic citizenship

Academic citizenship refers to the contributions – committee work, mentoring, governance, public engagement – that sustain universities beyond research and teaching. Yet because these activities are difficult to measure and often unrewarded, they are increasingly neglected (Macfarlane, 2005; Feldt et al, 2024). This neglect fragments institutions and weakens the norms of shared responsibility that should hold academic life together.

Troubled collegiality

Collegiality includes participatory and collective decision-making, a presumption of shared values, absence of hierarchy, supportiveness, a shared commitment to a common good, trust beyond a typical workplace, and professional autonomy. It has long been central to academic identity but has become contested. Some experience collegial labor as invisible and unevenly distributed; others see managerial attempts to measure collegiality as just another way of disciplining staff. Efforts to quantify collegiality may correct some injustices but also risk instrumentalizing it, turning a relational ideal into a bureaucratic category (Craig et al, 2025; Fleming and Harley, 2024; Gavin et al, 2023).

Across all these pressures, a common thread emerges: the forces shaping contemporary academia weaken the relationships required for intellectual work to flourish.

Why academic community matters

If community is eroding, why should we care? The answer lies in the link between community and the values that higher education claims to serve. A helpful framework comes from value theory, which distinguishes between instrumental, constitutive, and intrinsic goods.

Community as instrumentally valuable

Academic community helps produce the outcomes universities care about: research breakthroughs, learning, intellectual development, and democratic engagement. Collaboration makes research stronger. Peer support helps people grow. Shared norms encourage integrity, rigor, and creativity. Without community, academic values become harder to realize.

Community as constitutive of academic values

In many cases, community is not merely a helpful means but a necessary constituent. Scientific knowledge, as philosophers of science like Merton (1979) and Longino (1990) have long emphasized, is inherently social: it requires communal critique, peer review, and collective norms to distinguish knowledge from error. Learning, too, is fundamentally relational, as Vygotsky (1978) and Dewey (1916) argued. You cannot have science or education without community.

Community as intrinsically valuable

Beyond producing useful outcomes, community enriches human life. Belonging, shared purpose, and intellectual companionship are deeply fulfilling. Academic community offers a sense of identity, meaning, and solidarity that transcends individual achievement (Metz, 2017). In this sense, community contributes directly to human flourishing.

How community shapes academic life

Several examples show how academic values depend on community in practice:

Debates about educational values

The pursuit of academic values requires reflection on their meaning. Interpretive arguments about values like autonomy, virtue, or justice in education contribute to conversations that presuppose the collective norms of academic community (Nussbaum, 2010; Ebels-Duggan, 2015). These debates require shared standards of reasoning, openness to critique, and a shared commitment to better understanding.

Scientific knowledge and academic freedom

No individual can produce knowledge alone. Scientific communities ensure that discoveries are evaluated, replicated, and integrated into a larger body of understanding. Likewise, academic freedom is not a personal privilege but a communal norm that protects open inquiry (Calhoun, 2009; Frímannsson et al, 2022). It depends on solidarity among scholars.

Teaching as communal practice

Education flourishes in relational settings. Classrooms become communities in which teachers and students jointly pursue understanding. Weithman (2015) describes this as “academic friendship” – a form of companionship that expands imagination, fosters intellectual virtues, and shapes future citizens.

Across these cases, community is not optional; it is essential to academic values.

Rebuilding scademic community: structural and cultural change

Given its importance, how might universities cultivate stronger academic communities?

Structural reform

Universities should try to resist the dominance of market logic. Sector-wide policy changes could help rebalance priorities. Hiring, promotion, and reward systems should value teaching, service, mentorship, and public engagement rather than focusing exclusively on quantifiable research metrics. Without structural support, cultural change will be difficult.

Cultural renewal

A healthier academic culture requires a different mindset—one that foregrounds generosity, relationality, and shared purpose. In Generous Thinking, Fitzpatrick (2021) argues that building real community requires humility, conversation, listening, and collaboration. Community cannot be mandated; it must be practised.

This requires academics to challenge competitive individualism, share work equitably, strengthen trust and dialogue, and reimagine collegiality as a lived practice rather than a managerial tool. Most importantly, it requires us to recognize ourselves as fundamentally relational beings whose professional purpose is intertwined with others.

A moral case for academic community

Academic community is not only epistemically valuable; it is morally significant. Relational moral theories argue that human flourishing depends on identity and solidarity. We become the moral human beings we are through our communal relationships (Metz, 2021).

Applying this to academia reveals that collegiality is grounded in shared identity and shared ends. Since the moral obligations created by academic relationships remain professional, collegial community does not require intrusive intimacy. Far from suppressing dissent or professional autonomy, solidarity requires defending academic freedom and academic values generally.

A relational understanding of morality thus implies that the ideal of academic community promises not only a more fulfilling and coherent sense of occupational purpose, but also a way of relating to others that is more satisfying morally than the current environment individualistic competition.

Conclusion: the future depends on community

Universities today face an existential challenge. In the rush to satisfy markets, rankings, and managerial demands, they risk undermining the very relationships that make academic life meaningful. Academic community is not a nostalgic ideal; it is the cornerstone of learning, knowledge, virtue, and human flourishing.

If higher education is to reclaim a sense of purpose, it must begin by cultivating the social and moral conditions in which genuine community can grow. This requires structural reforms, cultural renewal, and a shared commitment to relational values. Without such efforts, universities will continue drifting toward fragmentation, losing sight of the goods they exist to protect.

Rebuilding academic community is not merely desirable. It is necessary – for the integrity of scholarship, for the flourishing of those who work within universities, and for the public good that higher education is meant to serve.

Sigurður Kristinsson is a Professor of Philosophy at the University of Akureyri, Iceland. His research applies moral and political philosophy in various contexts of professional practice, increasingly intersecting with the philosophy of higher education with emphasis on the social and democratic role of universities.


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Reflective teaching: the “small shifts” that quietly change everything

by Yetunde Kolajo

If you’ve ever left a lecture thinking “That didn’t land the way I hoped” (or “That went surprisingly well – why?”), you’ve already stepped into reflective teaching. The question is whether reflection remains a private afterthought … or becomes a deliberate practice that improves teaching in real time and shapes what we do next.

In Advancing pedagogical excellence through reflective teaching practice and adaptation I explored reflective teaching practice (RTP) in a first-year chemistry context at a New Zealand university, asking a deceptively simple question: How do lecturers’ teaching philosophies shape what they actually do to reflect and adapt their teaching?

What the study did

I interviewed eight chemistry lecturers using semi-structured interviews, then used thematic analysis to examine two connected strands: (1) teaching concepts/philosophy and (2) lecturer-student interaction. The paper distinguishes between:

  • Reflective Teaching (RT): the broader ongoing process of critically examining your teaching.
  • Reflective Teaching Practice (RTP): the day-to-day strategies (journals, feedback loops, peer dialogue, etc) that make reflection actionable.

Reflection is uneven and often unsystematic

A striking finding is that not all lecturers consistently engaged in reflective practices, and there wasn’t clear evidence of a shared, structured reflective culture across the teaching team. Some lecturers could articulate a teaching philosophy, but this didn’t always translate into a repeatable reflection cycle (before, during, and after teaching). I  framed this using Dewey and Schön’s well-known reflection stages:

  • Reflection-for-action (before teaching): planning with intention
  • Reflection-in-action (during teaching): adjusting as it happens
  • Reflection-on-action (after teaching): reviewing to improve next time

Even where lecturers were clearly committed and experienced, reflection could still become fragmented, more like “minor tweaks” than a consistent, evidence-informed practice.

The real engine of reflection: lecturer-student interaction

Interaction isn’t just a teaching technique – it’s a reflection tool.

Student questions, live confusion, moments of silence, a sudden “Ohhh!” – these are data. In the study, the clearest examples of reflection happening during teaching came from lecturers who intentionally built in interaction (eg questioning strategies, pausing for problem-solving).

One example stands out: Denise’s in-class quiz is described as the only instance that embodied all three reflection components using student responses to gauge understanding, adapting support during the activity, and feeding insights forward into later planning.

Why this matters right now in UK HE

UK higher education is navigating increasing diversity in student backgrounds, expectations, and prior learning alongside sharper scrutiny of teaching quality and inclusion. In that context, reflective teaching isn’t “nice-to-have CPD”; it’s a way of ensuring our teaching practices keep pace with learners’ needs, not just disciplinary content.

The paper doesn’t argue for abandoning lectures. Instead, it shows how reflective practice can help lecturers adapt within lecture-based structures especially through purposeful interaction that shifts students from passive listening toward more active/constructive engagement (drawing on engagement ideas such as ICAP).

Three “try this tomorrow” reflective moves (small, practical, high impact)

  1. Plan one interaction checkpoint (not ten). Add a single moment where you must learn something from students (a hinge question, poll, mini-problem, or “explain it to a partner”). Use it as reflection-for-action.
  1. Name your in-the-moment adjustment. When you pivot (slow down, re-explain, swap an example), briefly acknowledge it: “I’m noticing this is sticky – let’s try a different route.” That’s reflection-in-action made visible.
  1. End with one evidence-based note to self. Not “Went fine.” Instead: “35% missed X in the quiz – next time: do Y before Z.” That’s reflection-on-action you can actually reuse.

Questions to spark conversation (for you or your teaching team)

  • Where does your teaching philosophy show up most clearly: content coverage, student confidence, relevance, or interaction?
  • Which “data” do you trust most: NSS/module evaluation, informal comments, in-class responses, attainment patterns and why?

If your programme is team-taught, what would a shared reflective framework look like in practice (so reflection isn’t isolated and inconsistent)?

If reflective teaching is the intention, this article is the nudge: make reflection visible, structured, and interaction-led, so adaptation becomes a habit, not a heroic one-off.

Dr Yetunde Kolajo is a Student Success Research Associate at the University of Kent. Her research examines pedagogical decision-making in higher education, with a focus on students’ learning experiences, critical thinking and decolonising pedagogies. Drawing on reflective teaching practice, she examines how inclusive and reflective teaching frameworks can enhance student success.


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Peer Observation of Teaching – does it know what it is?

by Maureen Bell

What does it feel like to have someone observing you perform in your teaching role? Suppose they tick off a checklist of teaching skills and make a judgement as to your capability, a judgement that the promotions committee then considers in its deliberations on your performance? How does it feel to go back to your department and join the peer who has written the judgement? Peer Observation of Teaching (POT) is increasingly being suggested and used as a tool for the evaluation, rather than collaborative development, of teaching practice.

Can POT for professional development co-exist with and complement POT for evaluation? Or are these diametrically opposed philosophies and activities such that something we might call Peer Evaluation of Teaching (PET) has begun to undermine the essence of POT?

I used to think the primary purpose of peer observation of teaching (POT) was the enhancement of teaching and learning. I thought it was a promising process for in-depth teaching development. More recently I have been thinking that POT has been hijacked by university quality assurance programs and re-dedicated to the appraisal of teaching by academic promotions committees. The principles and outcomes of POT for appraisal are, after all, quite opposite to those that were placed at the heart of the original POT philosophy and approach – collegial support, reflective practice and experiential learning.

In 1996 I introduced a POT program into my university’s (then) introduction to teaching course for academic staff. Participants were observed by each other, and myself as subject coordinator, and were required to reflect on feedback and plan further action. It wasn’t long before I realised that I could greatly improve participants’ experience by having them work together, experiencing at different times the roles of both observer and observed. I developed the program such that course participants worked in groups to observe each other teach and to share their observations, feedback and reflections. A significant feature of the program was a staged workshop-style introduction to peer observation which involved modelling, discussion and practice. I termed this collegial activity ‘peer observation partnerships’.

The program design was influenced by my earlier experiences of action research in the school system and by the evaluation work of Web and McEnerney (1995) indicating the importance of training sessions, materials, and meetings. Blackwell (1996), too, in Higher Education Quarterly described POT as stimulating reflection on and improvement of teaching. Early results of my program, published in IJAD in 2001, reported POT as promoting the development of skills, knowledge and ideas about teaching, as a vehicle for ongoing change and development, and as a means of building professional relationships and a collegial approach to teaching.

My feeling then was that a collegial POT process would eventually be broadly accepted as a key strategy for teaching development in universities. Surely universities would see POT as a high value, low cost, professional development activity. This motivated me to publish Peer Observation Partnerships in Higher Education through the Higher Education Research and Development Society of Australasia (HERDSA).

Gosling’s model appeared in 2002 in which he posed three categories of POT, in summary: evaluation, development, and fostering collaboration. Until then I had not considered the possibility that POT could be employed as an evaluation tool, mainly because to my mind observers did not need a particular level of teaching expertise. Early career teachers were capable of astute observation, and of discussing the proposed learning outcomes for the class along with the activity observed. I saw evaluation as requiring appropriate expertise to assess teaching quality against a set of reliable and valid criteria. Having been observed by an Inspector of Schools in my career as a secondary school teacher, I had learned from experience the difference between ‘expert observation’ and ‘peer observation’.

Looking back, I discovered that the tension between POT as a development activity rather than an evaluation tool had always existed. POT had been mooted as a form of peer review and as a staff appraisal procedure in Australia since the late eighties and early nineties, when universities were experiencing pressure to introduce procedures for annual staff appraisal. The emphasis at that time was evaluative – a performance management approach seeking efficiency and linking appraisal to external rewards and sanctions. Various researchers and commentators c.1988-1993, including Lonsdale, Abbott, and Cannon, sought an alternative approach which emphasised collegial professional development. At that time action research involving POT was prevalent in the school system using the Action Research Planner of Kemmis and McTaggert. Around this time Jarzabkowski and Bone from The University of Sydney developed a detailed guide for Peer Appraisal of Teaching. They defined the term ‘peer appraisal’ as a method of evaluation, that could both provide feedback on teaching for personal development as well as providing information for institutional or personnel purposes. ‘Observer expertise in the field of teaching and learning’ was a requirement.

In American universities various peer-review-through-observation projects had emerged in the early nineties. A scholarly discussion of peer review of teaching was taking place under the auspices of the American Association for Higher Education Peer Review of Teaching project and the national conference, ‘Making Learning Visible: Peer-review and the Scholarship of Teaching’ (2000), brought together over 200 participants. The work of both Centra and Hutchings in the 90s, and Bernstein and others in the 2000s advocated the use of peer review for teaching evaluation.

In 2002 I was commissioned by what was then the Generic Centre (UK) to report on POT in Australian universities. At that time several universities provided guidelines or checklists for voluntary peer observation, while a number of Australian universities were accepting peer review reports of teaching observations for promotion and appointment. Soon after that I worked on a government funded Peer Review of Teaching project led by the University of Melbourne, again reviewing POT in Australian universities. One of the conclusions of the report was that POT was not a common professional activity. Many universities however listed peer review of teaching as a possible source of evidence for inclusion in staff appraisal and confirmation and promotion applications.

My last serious foray into POT was an intensive departmental program developed with Paul Cooper, then Head of one of our schools in the Engineering Faculty. Along with my earlier work, the outcomes of this program, published in IJAD (2013), confirmed my view that a carefully designed and implemented collegial program could overcome problems such as those reported back in 1998 by Martin in Innovations in Education and Teaching International, 35(2). Meanwhile my own head of department asked me to design a POT program that would provide ‘formal’ peer observation reports to the promotions and tenure committee. I acquiesced, although I was concerned that once POT became formalised for evaluation purposes in this way, the developmental program would be undermined.

Around 2008 my university implemented the formal POT strategy with trained, accredited peer observers and reporting templates. POT is now accepted in the mix of evidence for promotions and is compulsory for tenure applications. In the past year I’ve been involved in a project to review existing peer observation of teaching activities across the institution, which has found little evidence of the use of developmental POT.

The Lonsdale report (see above) proposed a set of principles for peer review of teaching and for the type of evidence that should be used in decisions about promotion and tenure: Fairness such that decisions are objective; openness such that criteria and process are explicit and transparent; and consistency between standards and criteria applied in different parts of the institution and from year to year. It always seemed to me that the question of criteria and standards would prove both difficult and contentious. How does a promotions committee decipher or interpret a POT report? What about validity and reliability? What if the POT reports don’t align with student evaluation data? And what does it mean for the dynamics of promotion when one of your peer’s observations might influence your appraisal?

In 2010 Chamberlain et al reported on a study exploring the relationship between annual peer appraisal of teaching practice and professional development. This quote from a participant in the study stays with me, “… the main weakness as far as I’m concerned is that it doesn’t know what it is. Well, what is its purpose?”

POT for professional development is an activity that is collegial, subjective, and reflective. My view is that POT for professional development can only co-exist with a version of POT for evaluation that is re-named, re-framed and standardised. And let’s call it what it really is – Peer Evaluation of Teaching (PET).

Dr Maureen Bell is Editor of HERDSA NEWS, Higher Education Research and Development Society of Australasia; HERDSA Fellow; Senior Fellow, University of Wollongong Australia.