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Enhancing student learning through innovative scholarship

by Jenny Burnham, Matt Mears, Kennedy Offor, Jane Woodin and Tim Herrick

This blog is a report on the Enhancing Student Learning Through Innovative Scholarship (ESLTIS) conference held at the University of Sheffield in July 2026. The conference involved participants from 24 different universities and nearly 50 sessions.

When higher education faces real pressure, coming together to reimagine how we teach and learn is more than just professional development – it is a vital act of community. In July 2026, 97 educators, researchers, and student partners gathered at the University of Sheffield for two energising days at the Enhancing Student Learning Through Innovative Scholarship (ESLTIS) conference.

Across two inspiring keynotes, 39 talks and oral bites, and seven hands-on workshops, the atmosphere was filled with practical optimism, bold experimentation, and genuine connection. At a time when financial constraints weigh heavily across the sector, ESLTIS 2026 proved that an exceptional, high-impact academic gathering does not have to cost the earth. By keeping delegate fees as low as possible without compromising on quality, the conference demonstrated that transformative scholarship can remain accessible, inclusive, and deeply rewarding for everyone involved.

To capture the spirit of those two days, this blog brings together the key ideas, personal reflections, and practical insights that defined the event. We explore the overarching themes shaping the sector, followed by firsthand perspectives from our keynote speaker, insights from a session chair navigating AI in the curriculum, and the invaluable contributions of our student partners.

Key Conference Themes

With the tone of ‘Dealing with Disruption’ set by Professor Sam Nolan, the conference explored how learning and teaching in higher education is responding to and being shaped by the outside influences bringing both adversity and opportunity into the sector. A recurrent theme of innovations drawing on AI technology encompassed support for diverse student needs, its use in assessment, and as a teaching aid. The conference mirrored the acknowledgement of the evolving higher education demographic, with discussions covering inclusivity, decolonisation, engagement, identity, and self-worth, with structured gap-filling support, and breaking down of participation barriers, busting out of disciplinary silos to support students’ belonging and wellbeing. Wide-ranging talks covered: curriculum both hidden and carefully designed, strategies to augment students’ development of their skills and their readiness for their post-graduation world, and experiences of collaboration with and between students, staff, and professional services teams

Underpinning it all was the continuing ethos of the ESLTIS community that recognises and celebrates the education-focus of academia, exploring this with talks and workshops on academic identity – how it grows and develops, the things that shape it, and how it can be described and evidenced. The conference was not one thing for all delegates, it had something for everyone; areas with which we are familiar and aspects provoking thought and reflection, from which we could learn.  Yet again, I have attended a conference and come away with more than I expected. 

Reflections from a keynote speaker 

Dr Jenny Burnham is a Senior University Teacher in Chemistry at the University of Sheffield. Here she writes about her career and how she shared her experience at the conference.

I am interested in everything and my career highlights have been similarly eclectic. Rather than focus on a single speciality, I used myself as the common thread to weave a story that would be useful, relevant, or inspirational for a diverse audience of enthusiastic and accomplished educators. Taking inspiration from the conference name and conference themes (academic identity, community in learning and teaching, SoTL, supporting student success, and learning and teaching possibilities), my keynote made a virtue of the meanders of my academic career so far.  

My academic journey started in chemistry, shaping my relationship with information (evidence is key) and placing the primacy of experimental investigation at the centre of my disciplinary awareness. My early scholarship interests grew from this, exploring the development of inquiry skills through laboratory education. Spending a lot of time teaching students in the lab and enjoying their individuality shaped a desire to see them all succeed. This developed into an education philosophy that students are clever and capable with enormous potential, which permeates my work. An MEd drawing on what can be learned from sharing practice and insight (Rowland, 1999) transformed my approach to education. The benefits I felt in sharing my work with people who were interested motivated me to extend my networks. These have been a source of inspiration, opportunity and support, and allowed me to define my leadership identity; supporting, facilitating, promoting, and enabling collegial interaction and growth within my community.

My keynote aimed to provide a little of something for everyone, and evidence that I succeeded in this is captured in three different post-keynote interactions. I was thanked for owning my nerves: “it helps” she said. I had lunch with a PhD chemist considering an academic career; and I received an enquiry about a Learning Landscapes initiatives I worked on. There was something for me too: a final, unexpected result of my talk was that pulling it together reminded me of the cool stuff I have done.  Celebrating successes is definitely something I intend to do more of.  

Reflections from a session chair 

Dr Kennedy Offor was a member of the local organising committee and reflects on his experiences at ESLTIS in the role of session chair.

Chairing the ‘AI in the Curriculum’ session gave me with a unique perspective on how colleagues from different disciplines are responding to the rapid emergence of generative AI in higher education. Although the eight presentations ranged from institutional policy to engineering, mathematics, business, humanities, and language learning, a common thread emerged throughout the session – how learning, assessment, and curriculum design should evolve to ensure that students continue to develop critical judgement, disciplinary understanding, and professional responsibility. The discussion reflected a noticeable shift from reacting to AI as a disruptive technology towards designing learning environments that make purposeful and educationally meaningful use of it.

Laurie Wilson opened the session by introducing the University of Sheffield’s developing ‘Common Approach to AI in the Curriculum’, emphasising “harmonisation rather than homogenisation” and arguing that AI should support good disciplinary teaching rather than drive it. Joanne Irving-Walton then offered a thought-provoking perspective by examining the “gaps” where judgement develops, suggesting that AI increasingly participates in the uncertainty, rehearsal and reflection through which students and educators learn. 

Panos Kloukinas, Nick McCullen and Will Roberts built on this by arguing that mathematics and computation education should increasingly prioritise application, interpretation and problem formulation over procedural execution, while Dawn Whitton demonstrated how students could critically evaluate AI itself through a staged business assessment that required them not only to use AI, but also to critique its assumptions, biases and wider societal implications.

The engineering perspective was represented through our own presentation, which used longitudinal Graduate Teaching Assistant reflections to show how observations of student uncertainty, teamwork and AI use informed iterative redesign of an MSc engineering design module. Mireilla Bikanga Ada’s graduate study provided an interesting counterpoint by showing that, despite widespread AI adoption, teamwork remained the strongest predictor of graduates’ perceptions of degree relevance. 

Rachel Johnson illustrated how a simple, low-stakes classroom activity could encourage students critically to examine AI-generated information and its biases, while Franziska Collier demonstrated how AI avatars could provide authentic, low-risk opportunities for students to practise high-stakes language assessments.

The discussions reinforced my own view that the key educational question is no longer whether students should use AI, but how curricula can help them develop the professional judgement needed to use it responsibly. Rather than diminishing the role of educators, the session demonstrated that AI makes thoughtful curriculum design, assessment, and disciplinary expertise more important than ever, and highlighted an encouraging direction of travel for the Scholarship of Teaching and Learning community.

Students as Partners 

Dr. Jane Woodin was a member of the local organising committee and offers her thoughts on working with students as volunteer partners 

The conference ethos of inclusive dialogue was also evident in the organisation of student volunteers who were invited to participate in whatever they wished, from visiting and advising on the venue beforehand, supporting registration and general enquiries, and co-chairing sessions together with a staff volunteer. Responses from the eight volunteers ranged from tentative interest in helping out to requests to ‘have a go at everything’, and others who did the minimum in terms of active volunteering because they found the topics of the talk so interesting and important.

It was heartening to hear one volunteer offer to sit at the front of the large auditorium to oversee a presentation session, handling the Q &A masterfully, including interventions from the co-chair. Another volunteer started out shyly offering to help at the registration desk, but seeing fellow students taking the chairing role, threw herself into it enthusiastically. 

These experiences remind us how the processes surrounding education are as important as the education itself – or perhaps are or should be inseparable, as the ‘way that you do it’ melds seamlessly into ‘what you do’; offering an example of SoTL in action at its best.  

Final Reflections

Dr Matt Mears is a Senior University Teacher in Physics at the University of Sheffield, and was chair of the Local Organising Committee for ESLTIS2026

As Conference Chair, reflecting on the energy across those two days fills me with immense pride and a distinct sense of gratitude. While hosting an event of this scale undoubtedly takes work, the dividend it pays back to the host team and the wider community is extraordinary. Watching colleagues reconnect, draw genuine inspiration from such a rich variety of presenters, and spark new collaborations in real time reminded me why these gatherings are so vital for our sector.

If you have ever toyed with the idea of organising a conference, or perhaps bringing ESLTIS to your own institution, I cannot encourage you enough to take the leap. The effort involved is far outweighed by the connection, camaraderie, and fresh momentum it generates. Whether as an attender, a presenter, or a future host, I look forward to seeing where our community goes next.


Dr Jenny Burnham is a chemistry university teaching specialist with 20 years’ experience teaching and learning about education at the University of Sheffield. 

Dr Matt Mears is a Senior University Teacher in Physics in the School of Mathematical and Physical Sciences at the University of Sheffield. His research focuses on inclusive education practices and laboratory-based teaching.

Dr Kennedy Offor is an engineering educator and researcher with experience teaching undergraduate and postgraduate engineering at the University of Sheffield’s School of Electrical and Electronic Engineering and, more recently, apprenticeship and technical engineering education at the AMRC Training Centre.  

Dr. Jane Woodin is a Senior University Teacher in the School of Languages, Arts and Societies at the University of Sheffield. She has taught and researched in intercultural communication, focusing on peer interaction and dialogue approaches.

Dr Tim Herrick is a Senior University Teacher in the School of Education, and a National Teaching Fellow. He was part of the ESLTIS conference committee and helped co-ordinate this blogpost.

Reference Rowland, S (1999) ‘The role of theory in a pedagogical model for lecturers in higher education’ Studies in Higher Education, 24(3), 303–314. https://doi.org/10.1080/03075079912331379915


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Learning spaces, educational technology and the future of the physical learning environment

by James Rutherford

To look into the near future, it would be sensible to start by avoiding this typical question: ‘What technology do we need to install in these rooms?’ We would be wise to ask instead: ‘What do we want our education to look like?’ Future learning spaces and associated educational technologies require an approach that supports teaching and student collaboration, with accessibility features and environmental adaptations embedded within the design of learning spaces rather than delivered through standalone audio-visual equipment.

Research on learning spaces highlights the importance of aligning technology decisions with pedagogy and intended learning activities, rather than allowing the space and in-class technology to dictate educational practice. (Radcliffe et al, 2008) This principle resonates strongly with my experience working in HE learning environments, where the most successful spaces are those that enable effective communication and participation for all learners. The technical and physical space itself must therefore support diverse forms of engagement, ensuring that staff can teach effectively and that students can participate fully through listening, discussion, collaboration, practice and observation. (Jamieson, 2003)

Audiovisual technology

At City St George’s, University of London, the educational technology allows staff to enter any learning space, connect a laptop, teach, collaborate, record or stream sessions with minimal effort. This has largely been achieved through the learning spaces project (Designing Active Learning Initiative (DALI), 2026) and subsequent upgrade work, with room controls and user experiences standardised across the institution to reduce complexity and support requirements.

Design for learning and teaching

However, this can mean that technical decisions are made before the educational intent has been planned and shared with all stakeholders. It is advisable that educational technology supports the intended teaching activities rather than determines them, with the educational strategy and signature pedagogy of the university driving the learning environment design (Rudd, 2006; Hasper, 2024). Spaces ought to be designed around the learning and teaching needs of an institution, making it straightforward for staff to teach effectively and for students to engage successfully.

Environmental prerequisites

In many learning environments, audio quality often receives less attention than display technology, but it is obvious that students need to hear clearly, be able to participate in discussions and access lecture capture recordings. It is increasingly recognised as a key contributor to students’ learning outcomes, and there is evidence that speech intelligibility and the control of background noise are fundamental requirements for effective learning. This is because the design of each space directly influences effective communication. (Acoustic Design for Schools, DFE, 2015) This includes unwanted noise from corridors, neighbouring rooms, ventilation and external sources, as extraneous noise can be a real hindrance to learning and teaching capabilities.

Therefore, acoustic treatment needs to be considered as fundamental, now and in the future, not least with accessibility at the forefront of our minds. Universities can often improve existing spaces through acoustic treatment and interventions designed to reduce reverberation, although the feasibility and cost will vary by building.

Controllable lighting, appropriately designed and ergonomic furniture, thermal comfort and excellent sightlines all contribute to an effective learning experience. Classroom furniture is now expected to be comfortable, robust and appropriate for the type of teaching taking place. Spaces need to accommodate different body sizes, the requirements of student movement, with diversity and accessibility alongside different timetabled patterns of use and perhaps involving longer teaching sessions.

Accessibility is fundamental

Future learning spaces will need to be designed with diverse learners in mind, consistent with the principles of Universal Design for Learning (Universal Design for Learning｜CAST, 2026). Spaces are required to include captioning, recordings, remote participation and flexible methods of seating and student engagement. Across HE, accessibility is increasingly being incorporated into space planning from the outset through features such as captions, accessible controls, suitable furniture for students with access needs, clear audio and barrier-free access. Real inclusion is developing into a measure of quality rather than an afterthought.

Design for hybrid delivery

Hybrid delivery could be seamlessly integrated into learning space design while ensuring that the on-campus experience remains the priority. Spaces need to support clear sound and appropriately intelligent camera angles. Video conferencing tools permit content sharing, recording and online participation as standard, but hybrid teaching must not feel like an add-on; rather, it should be a flexible and inclusive addition to the learning approach (Beatty, 2007; Melcher et al, 2025). In hybrid spaces, students need to hear the lecturer, hear questions from the room, follow discussions and revisit recordings afterwards.Consequently, excellent microphones, clear loudspeakers, robust voice support, as well as captions and assistive listening are expected to be core requirements.

AI tools

AI is likely to support areas such as transcription, captioning, camera tracking controls, fault detection and remote monitoring for the service teams. Its role would be to improve accessibility, as well as provide data analysis of how the space is performing environmentally and reduce workload rather than add complexity. Such developments must, however, consider how AI tools relate to privacy, ethics, and data governance, which will remain important factors in their implementation.

Support and maintenance

Learning spaces will become easier to monitor, maintain and update through centralised management tools that do exist, but with emerging AI capabilities that could enhance these processes through predictive maintenance, intelligent system monitoring, automated troubleshooting, and data-driven insights. Support teams would then be able to identify and resolve many issues remotely, improving reliability and assisting with planning of new environments.

The transformation of accessibility and inclusion

Accessibility is no longer regarded purely as a matter of compliance, but as an essential component of the learning environment. Future learning spaces ought to be planned and designed on the basis that:

  • some students will need captions
  • some students will rely heavily on lecture capture recordings
  • some students will be neurodivergent
  • some students will join remotely
  • some students will be working in noisy or shared environments
  • some students will need language support

This is key because it changes accessibility from a ‘special provision’ into part of the normal learning experience, in particular the essential role that lecture recordings play for neurodivergent and disabled students (Horlin, Hronska and Nordmann, 2024).

The AI-supported room

AI technology is likely to be adopted within learning spaces with support for:

  • automatic camera framing
  • lecturer tracking
  • capture of student questions
  • reduction of background noise in lecture capture
  • live transcription
  • searchable lecture capture recordings
  • automatic chapters in those recordings
  • summaries of sessions
  • early warning of equipment faults
  • better support information for technical teams
  • information about how rooms are being used

There are obviously numerous risks with adopting AI: too much monitoring; weak governance; data protection and privacy concerns; intellectual property questions; and technology that looks clever but does not help teaching. However, as two recent literature reviews illustrate, the opportunity that AI tools can bring is undoubtedly significant, with the potential for less pressure on academics to operate the technology in a classroom and by offering better access for students (Crompton and Burke, 2023; Adamakis and Rachiotis, 2025).

Conclusion

If the last twenty years were defined by the digitisation of teaching, the next decade could be defined by the redesign of learning itself. Will higher education across the UK embrace this opportunity? Can it afford not to in the current climate of financial uncertainty and geopolitical vulnerability? Accessibility, hybrid participation, intelligent systems and engaging learning environments can no longer be regarded as enhancements, as they will surely become the standard to meet the expectations of students and staff. The institutions that thrive will not be those with the most technology, but those that use it most purposefully. The future of learning spaces is not about what technology we install; it is about what we enable.

James Rutherford is a Senior Educational Technologist at City St George’s, University of London, specialising in learning spaces, educational technology, and hybrid delivery. With over 35 years’ experience in higher education, he has worked as a learning space designer, AV project manager, and video producer, leading initiatives that support innovative and inclusive learning and teaching. James holds a Master’s in Advanced Educational Practice from the UCL Institute of Education and is a Fellow of Advance HE.


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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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Sidestepping: my experience as a female Black tutor

by Olajumoke Orebamjo

I have been teaching for over 16 years (with the last 10 years in the tertiary sector) and I have had the pleasure and sometimes, unfortunately, the displeasure of interacting with individuals from diverse backgrounds, races and perspectives. Lately, my role, amongst others, entails one-to-one supervision sessions. This is a role like many other university roles which is repetitive and sometimes mundane in nature. Nevertheless, I particularly enjoy working with mature adult learners as I find my interactions with them intellectually stimulating. The sessions often deviate from the topic of focus to other issues that are not necessarily relevant, but what is gained from these interactions is not just a fulfilment of the aims of the meeting but also a general sense of wellbeing that is cerebral in nature.

The ‘dance’

Because most students of colour have had little or no interaction with a successful individual from a minority ethnic group, what often ensues is what I like to call ‘side-stepping’, as we initially engage in a mental dance around each other, trying to determine each other’s thoughts, and oscillating between ‘prey’ and ‘predator’. This is a natural reaction of defended subjects; ever vigilant and ready to ward off potential threats. We spend some time on this preamble before one of us goes on the attack, which would usually be the student, who would ask the question I’ve heard countless times: ‘how did you get this job?’. There is the assumption that I could only have attained this position by questionable means. The perception of the student is that I’m ‘culturally suspect’ (Orebamjo, 2024) and a possible stumbling block to their academic success. I have even been ascribed the moniker ‘oreo’ – black on the outside but white on the inside – by students who felt the need to express their disappointment that I was not Black enough for their liking or that I ‘act white’ (Orebamjo, 2024). 

The students’ negative reactions never come as a surprise as I have become accustomed to this form of ‘friendly fire’ (Philip, Rocha and Olivares-Pasillas, 2017). It was a recurring phenomenon I endured while delivering the top up degree programme in health and social care in a London-based university. My attempt to mitigate the academic challenges of the mature students, who were all from minority ethnic groups, was met with fierce opposition from the students. In their view, my actions, as a Black tutor, not only exposed their inadequacies, it simulated the unrealistic, unfair and discriminatory practices of a hegemonic system (Orebamjo, 2024).  The students’ thinking was that my being Black meant I would have a better understanding of their lived experiences.

It is therefore no wonder that any encounter with students of colour automatically triggers the ‘caution’, ‘get ready to attack’ and ‘attack!’ or ‘stand down’ (in that order) signal within me. I spontaneously assume a defensive persona, with a corresponding reaction in the student.  Each encounter is the same, commencing with psychological dance; the student undulating between delight (of sharing the commonality of ‘minority’), suspicion (that judgement is looming) and disappointment (that no hoodwinking can take place). I’m also mentally prancing; assured of the semblance of authority I believe I possess, wary of the fact that a ‘deadly’ attack may occur at any moment, while at the same time, trying to convey to the student that ‘you are in a safe place’.

It is what it is

As a Black woman, I am aware of all these defensive tactics from global majority students and my experiences mirror those of colleagues from minority ethnic groups. The reactions of this profile of students are taken for granted and are ‘to be expected’. I do however tread carefully in these interactions because I do not want to fulfil the students’ negative expectations and so spend more time than necessary salving their sense of self-worth in a futile attempt to dispel all negative perceptions they may have about me. It’s like I’m saying, ‘hey I’m one of you so don’t judge me too harshly!’. Eventually though, I resign myself to thinking, ‘it is what it is’.

The racial tension between Blacks and Whites is a common occurrence that is often presumed. Hence it is difficult, if not impossible, to explain these experiences to my White colleagues as these actions and reactions are born of the simple reality of an ’other’ interacting with another ‘other’ within a highly hierarchical higher education arena. Each one is engaged in a constant mental negotiation with the dominant values that pose a threat to their individuality and self-worth, whilst attempting to justify their membership of a seemingly hostile establishment that has no appreciation of their individuality (Tormey, 2021).

Constant reflection, together with extensive engagement with literature on mature learners from minority ethnic groups in higher education, has given me in-depth knowledge and understanding of the educational challenges of this erstwhile marginalised group of students and so I am well equipped to manage the students’ attitudes and emotional baggage. Of greatest value is my engagement with intersectionality (Crenshaw, 1989), which has given me an awareness of how social identities such as race, class, social economic status and gender intersect and overlap to result in complex experiences of disadvantage or privilege. Many students of colour would have experienced multifaceted oppression resulting in defensive attitudes, which they end up bringing into their learning environment (Orebamjo, 2024). To therefore come face to face with a Black individual with some level of authority – especially in a university that has a demographic footprint of almost 100% White – is reason enough for the student to call in the ‘defence calvary’.

And so, the dance continues!

Dr Olajumoke (Jumie) Orebamjo is a lecturer in Practice Development: Health and Social Care and Paramedic Practice at University of Cumbria where she oversees undergraduate and graduate research projects. She’s also a Senior Fellow of the Higher Education Academy and a committed academic with over 12 years of experience teaching and supporting students to overcome academic challenges by developing agency. Proven record of designing and effecting teaching and learning methods that develop students’ skills particularly in metacognition.


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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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In defence of SoTL: anchoring educational evaluation and educational research

by Liz Austen

By the end of 2025 I had attended three HE related conferences: Euro SoTL, the Wonkhe Festival of HE and the SRHE Annual Conference. I presented on similar topics at all three events; what evidence do we generate to help us understand and act to enhance student experiences and outcomes in higher education? During the Wonkhe panel and my SRHE session, I defined two approaches at the disposal of HE practitioners:

Higher Education Evaluation: an approach which helps to understand and explore what works and doesn’t work in a given context and is of value to stakeholders. The aim of evaluation is to generate actionable evidence-informed learning, which encourages, informs and supports continuous improvement of process and impact (Evaluation Collective 2025)

Higher Education Research: to extend knowledge and understanding in all areas of educational activity and from a wide range of perspectives, including those of learners, educators, policymakers and the public (adapted from BERA, 2024)

At the Wonkhe panel, Clare Loughlin-Chow (CEO of SRHE) helpfully outlined the higher education research topics that were most prevalent in the SRHE journals. Omar Khan (CEO of TASO) then outlined the scope and priories of TASO, an affiliate member of the government’s What Works Network which focuses on higher education evaluation. My conceptual discussion of evidence generation brought the two together.

At EuroSoTL earlier in the year, my colleagues and I outlined our new institutional approach to the Scholarship of Teaching and Learning:

Scholarship of Teaching and Learning (SoTL): to improve student learning through engagement in the existing knowledge of teaching and learning, developing contextual ideas and innovation in practice, reflecting on practice, applying methodological rigour, working in partnership with students, and sharing of scholarship publicly (adapted from Felton (2013))

When I attended SRHE in December 2025, SoTL appeared in only one session I attended and some of this discussion focused on the challenges of bringing SoTL into spaces for educational research. My hand in the air comment – that criticism of SoTL by educational researchers was an example of ‘academic snobbery’ – certainly raised a few eyebrows. This blog post considers the relationship between these three approaches and whether, for the good of our students, it’s time for some reconciliation.

Educational evaluation, SoTL and educational research

Educational research in higher education has developed over the last 60 years. Interestingly, research into teaching and learning is cited as the most theorised by this type of research (Tight, 2012). Higher education evaluation, sometimes considered as applied research, was recently propelled by the Office for Students’ agenda to ‘evaluate, evaluate, evaluate (Office for Students, 2022). SoTL has developed alongside HE research and evaluation, emerging from Boyer’s work in 1990.

The aims of each endeavour are distinct, tied by the notion of ‘enquiry’. Research seeks to build new knowledge, and evaluation seeks to provide judgment on a contextual problem. SoTL has a narrower focus on teaching and learning than the broader scope of research and evaluation but incorporates prior knowledge and contextual problem solving through focused enquiry (Gray, 2025). SoTL builds on the foundation of social sciences methodology and can integrate disciplinary methodology into practitioner’s teaching and learning enquiry (Riddell, 2026). Educational evaluation often asks questions of the effectiveness of interventions, but in some teaching and learning spaces, the evaluative language of ‘intervention’ isn’t appropriate (Austen (2025) in Austen and McCaig (2025)). Exploring what works through SoTL enquiry aligns better. Often the bridging term ‘pedagogic research’ is used as integral to SoTL (close to practice) but distinct from educational research (broader anticipated impact). Our chosen SoTL definition uses neither research nor evaluation terminology, but has component parts – knowledge, innovation, method, dissemination – that are central to all.

The essential agents in educational research, evaluation and SoTL are the same – individual students (as partners, as participants and as voice givers), individual staff (academic and professional services), institutional groups or clusters, collaborating HEIs, and third space organisations. Reasons for enquiry are also similar and include sector expectations and shared learning, the desire for institutional enhancement and impact, personal development and career progression. Or as Ashwin & Trigwell (in Evans et al, 2021) note:  to inform a wider audience; to inform a group within context; to inform oneself. All research, evaluation and SoTL agents must navigate the practical and ethical considerations of ‘insider’ enquiry if they are exploring their own practices or within their institutional contexts (BERA, 2018; Barnett & Camfield, 2016).

Output pathways are also interconnected. The SoTL staircase (Beckingham, 2023) recognised the variety of outputs encouraged by SoTL and includes those traditionally aligned with research and evaluation (reports and journal articles). Research outputs may be guided by REF criteria, and evaluation outputs by readership. The conclusions in research articles frequently state that more research needed, and evaluation reports often sit unread in metaphorical desk draws. In comparison, SoTL practitioners benefit from publications which are close to practice, quicker to publish, and more likely to influence change.

Both educational evaluation and educational research are inherently theoretical, grounded by educational or pedagogic theory or a theory of change. SoTL is more action focused, less theoretical than research yet can be more exploratory than evaluation. In 2011, Kanuka questioned SoTL’s credibility due to the lack of theoretical underpinning or reference to existing scholarship. At times, I suggest that educational research can be positioned too far in the opposite direction. The presentations at SRHE were heavily theoretical and sometimes I was left thinking ‘so how would this work actually improve the learning experiences of students’? In contrast, the breadth of SoTL includes both theory and action, albeit in more pragmatic ways.

There are values and specific skill sets of educational researchers and evaluators (and often epistemological disagreements occur between the two). This commitment to identity can be excluding and may help to understand why SoTL has been challenged. Canning & Masika (2022) caution us on the ‘threat to serious scholarship’ posed by SoTL, which they believe risks devaluing research into higher education learning and teaching. Their criticism of ‘anything goes’ I would frame as an important approach to inclusion. Their criticism of the ‘watered-down version of teaching and learning research’ I frame as SoTL’s recognition of the developmental, particularly in building staff confidence. Where confusion over definitions and scope still occur, I question whether institutional SoTL has been well grounded or well led.

Conclusion

There is clearly a divide between higher education research and SoTL. There are few recent SRHE blog posts which reference SoTL at all and one that does advises against flag-in-the-sand nomenclature (Sheridan, 2019). Having spent a lot of time in these circles, I believe higher education evaluators are more agnostic, but I include them in this discussion as they bring a new dynamic to this debate.

In this blog I have identified the ways in which research, evaluation and SoTL have their own agendas and yet have much in common. I argue that SoTL emerges as a grounding anchor between higher education research and higher education evaluation. SoTL borrows from both. SoTL feeds into both. SoTL is more than both (Potter, 2025). SoTL’s inherent value is the ability to build a community which improves student experiences and outcomes in an enquiry led and timely way.

For more details on the approach to SoTL at Sheffield Hallam University see: https://lta.shu.ac.uk/scholarship

Reference

Riddell, J (2026) ‘Hope circuits in practice: how the scholarship of teaching and learning fuels pedagogical courage and systemic change’ Guest Lecture, Sheffield Hallam University

Liz Austen is Professor of Higher Education Evaluation and Associate Dean Learning, Teaching and Student Success at Sheffield Hallam University. She has worked as an independent Evaluation Consultant on HE sector contracts and is a regular keynote speaker on all things evaluation in HE. Her focus is on evidence informed practice across the student lifecycle. Liz also leads a cross sector HE network called the Evaluation Collective.


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Can folk pedagogies help us understand the limited impact of research on higher education?

by Alex Buckley

The SRHE conference is a great place to see our field in all its glory. From the sessions I attended in December 2025, one thing that was abundantly clear was the desire of so many HE researchers to change the world. A distinctive feature of contemporary HE research – reflecting the social sciences more broadly – is the focus on political and ethical issues, with avowedly political and ethical intentions. The improvement of society is often the explicit end, rather than the more humble improvement of our own part of the education system.

Despite this desire to make a difference, higher education research has for many years been held up as an area where the impact of those working in the field is not what it could be. As George Keller said in 1985, “hardly anyone in higher education pays attention to the research and scholarship about higher education”,

Asking the right questions?

There hasn’t been a lot of work on the gap between research and practice in HE – though there is a fair amount in the schools sector from which we can extrapolate, to a greater or lesser extent – but one issue that has received some attention is the fundamental one: are researchers actually asking the right questions?

Vivianne Robinson is a researcher who has laid a substantial amount of blame at the feet of researchers, who “have little to offer by way of alternative solutions, when the problems they have been studying are not those of the practitioner” (Robinson 1993). I have recently used Robinson’s model of Problem-Based Methodology to explore whether research about exams in higher education does engage sufficiently with the challenges that teachers take themselves to face. The results were not encouraging.

One of the more straightforward of Robinson’s criteria for impactful research is that researchers should be addressing teachers’ beliefs, and correcting them where they are erroneous. That’s important, but what if those beliefs are hard to shift? We all have stubborn hunches about how higher education works: good ways of motivating students, how to write feedback that will make students pay attention, how to clearly communicate complex ideas. What if there are teacher beliefs that are deeply embedded, so deeply that we don’t always know we have them, but that aren’t helping us and need to change?

One idea that has been explored in the school sector, but has largely passed us by, is the concept of ‘folk pedagogies’. This idea was developed in the 1990s as an extension of the more famous concept of ‘folk psychologies’: the tacit theories that we all have that allow us to make sense of people’s behaviour. For Jerome Bruner, a natural next step from folk psychologies was the idea that we have intuitive theories about how people learn.

“Watch any mother, any teacher, even any babysitter with a child and you’ll be struck by how much of what they do is steered by notions of ‘what children’s mind are like and how to help them learn,’ even though they may not be able to verbalise their pedagogical principles.” Bruner (1996)

There has been some research in the school sector about the implications of this idea, particularly in terms of how much difference research makes to educational practice. Folk pedagogies have two features that will make them a factor in the impact of education research: they interfere with the uptake of new research-based ideas and approaches, and they are stubborn. On the first point, the idea is that new ideas about higher education will have to replace the old if they are to influence teachers; and on the second, evidence suggests that even where trainee teachers have ostensibly internalised more scientific theories of learning, the folk pedagogies come creeping back.

In the case of higher education, what might these commonsense, intuitive theories look like? They might just be very general ideas about how people learn, applied to the particular context of higher education. Bruner identifies a range of broad folk pedagogical views, such as one which sees ‘children as knowers’, with a focus on the gathering and organising of facts. Perhaps one kind of folk psychology of higher education would be the application of that idea specifically to students in universities rather than other sectors: a focus on the selection, organisation and retention of propositional knowledge within degree programmes. Perhaps there are also specific intuitive theories about higher education that influence teachers’ practices. Perhaps there is a folk intuition that university students should not be spoon-fed – that they must take responsibility for their own learning and seek to develop their own views. Perhaps there is a folk intuition that students should encounter challenging views that encourage them to question their own certainties. In the absence of research, we can only speculate (and introspect).

Respecting the ‘folk’

The idea that teachers have deep intuitions about how students learn, that those intuitions can prevent them from acting on more evidence-based beliefs, and that those intuitions are hard to shake; none of those ideas are particularly earth-shattering. They are probably common sense among those researching and enhancing higher education. The value of the idea of ‘folk pedagogies’ lies instead in the way that it encourages us to take those intuitions seriously, both as an object of study and a powerful barrier to change.

Rather than dismissing intuitions about higher education – as ignorant beliefs and hide-bound traditions – we can study them. What are they? Where do they come from? How do they change? The idea of folk pedagogies is not pejorative. There’s no shame in having intuitions about how learning works. As with folk psychological theories, they are necessary parts of how we navigate the world, and something we can’t do without. There is also deep wisdom to be found in those intuitions, even if they are sometimes misleading. Research goes wrong by departing from common sense, at least as much as the other way around.

Acknowledging the existence of folk theories of higher education can help improve the impact of our research in all sorts of ways. We can research them, to understand why teachers and students (and others) do what they do, and the conditions in which deep intuitions can change. It can help us understand where – and why – research has departed so far from common sense as to be of little practical relevance.

It can also help us understand the scale of the challenge. In much of what we do, we’re seeking to modify what university teachers do, which very often means changing how they think. The reality is that we aren’t usually changing superficial, specific beliefs, at least not where the improvements we’re seeking are substantive. We’re changing deep beliefs picked up over a lifetime. Our model of improvement may then need to fit the old adage: if you’re not making progress at a snail’s pace, you’re not making progress. That’s a bit different from annual quality enhancement cycles or short-term strategic initiatives. We can change the world, but it will take time.

References

Bruner, J. (1996). The culture of education. Harvard

Robinson, V. M. J. (1993). Problem-Based Methodology: Research for the Improvement of Practice. Pergamon Press

Dr Alex Buckley is an Associate Professor in the Learning & Teaching Academy at Heriot-Watt University, Scotland. His research is focused on conceptual aspects of research and practice in assessment and feedback.


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Understanding complex ambiguous problems through the lens of Soft Systems Methodology

by Joy Garfield and Amrik Singh

As the future leaders of a society that is increasingly complex and challenging higher education students need a good grasp of social, political, economic and environmental issues and need to feel equipped to propose reasonable recommendations. This can seem a daunting prospect for anyone, let alone higher education students who may have little or no prior experience of working in these areas. Students need to understand the world view of the stakeholders and the what, how and whys of the situation being explored. What is the problem situation, how will we understand it, and why are we trying to understand it? Here we describe an approach successfully used in our postgraduate teaching at Aston Business School, UK.

Soft Systems Methodology (SSM) (Checkland, 1986) has been successfully used in many different contexts for complex problem-solving. With its seven-stage structure it provides a framework for structuring/framing wicked problems by initially thinking about what is happening in the real world from the point of view of different stakeholders. An idealised world without any constraints is then explored from different stakeholder perspectives so that different wants/needs for a new system can be considered. Students are encouraged to use empathetic discourse to understand the multiple perspectives of the stakeholders in the problem situation. The comparison between the real world and idealised worlds allows for an eventual accommodation of future ways forward.

Soft Systems Methodology is currently used to teach complex problem solving to postgraduate students at Aston. The module team have developed a group-based approach that has been found to produce a deeper understanding of concepts and yield better overall results, particularly given that students are mostly international postgraduate students. For most of the students their first language is not English, and they are new to complex problem-solving.

Teaching sessions are structured around the different stages of the Soft Systems Methodology. Group work is used so that students support one another in their learning of the concepts and then apply these individually to their chosen assessment topic. The UK criminal justice system is taken as an in-class example and students are asked to think about a particular complex area to focus on, eg overcrowding in prisons in a particular city. Terminology can be particularly complex and hard to grasp if your first language is not native English, so the language used to explain concepts is kept simple and a number of areas of scaffolding are used to help to support the learning.

The first task related to SSM involves students identifying the stakeholders and their power/interest in the complex situation. Students are then taught the concepts of a rich picture and they draw a rich picture as a group for their chosen problem situation using white board paper (example below). The rich picture itself enables students to understand the real world, stakeholder issues, conflicts, and relationships together with who interacts with the problem from outside of its boundary.  Students present their rich pictures to the wider group for formative feedback.

This helps with constructive feedback and a deeper understanding of the complex issue. The rich pictures may seem simple, but simplifying a complex problem is complex in itself! This helps students to understand and tease apart the complexities of the problem situation. The rich picture depicts the problem situation better than just making notes alone.

For the realisation of the idealised world, students put themselves in the shoes of the stakeholder.  This involves empathetic discourse whereby students interview one another about what they would want for a system, without taking into consideration any constraints from different stakeholder perspectives. Students are then able to expand these statements as a group to take into consideration the different aspects. From this, students construct a model which helps depict the transformation activities that the stakeholders wish to conduct to reach their desired output.

By gaining a better understanding of the real world from drawing the rich picture and thinking about an idealised world and possible transformation activities, students can then gain an understanding for the changes going forward.

Topics chosen by students for their assessment have included: housing refugees in the UK; online exams or in person exams at university; homelessness; impact of the pandemic on tourism; child marriages in India; a start up in France to reduce plastic packaging; finding the appropriate route for a railway between two cities in Germany. These are all complex and ambiguous problems that need to be understood before any potential solutions are made.

During the module students develop confidence in the application of SSM and come to a true understanding of the process of accommodating different stakeholder perspectives – especially when consensus is not always possible. What we understand from this journey is that there is no ‘one shoe fits all’ solution when understanding complex ambiguous problems.

Empathy enriches the SSM process by ensuring the human side of systems is as important as the technical side. It helps to create solutions that work not just in theory but in real, messy, human-centric environments. Empathetic discourse is very valuable to understand the voice of the stakeholders. What we have learned from the delivery of the module is that when complex ambiguous problems are human centric, then the solutions are human centric also.

Checkland, P (1986) Systems thinking, systems practice Chichester: Wiley

Dr Joy Garfield is a Senior Teaching Fellow and Director of Learning and Teaching for an academic department at Aston Business School, Aston University, UK.  Her subject discipline area is information systems, particularly systems modelling and complex problem solving. With just over 20 years of experience in academia, she has worked at a number of UK universities. Joy is a Senior Fellow of Advance HE and is currently an external examiner at Sheffield Hallam University and the University of Westminster. 

Dr Amrik Singh is a Senior Teaching Fellow at Aston University, UK. He has over 15 years of academic experience in Higher Education. He is also a Senior Fellow of the Advance HE, SFHEA. His teaching areas includes operations management, effective management consultancy, and business operations excellence. 


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Teaching students to use AI: from digital competence to a learning outcome

by Concepción González García and Nina Pallarés Cerdà

Debates about generative AI in higher education often start from the same assumption: students need a certain level of digital competence before they can use AI productively. Those who already know how to search, filter and evaluate online information are seen as the ones most likely to benefit from tools such as ChatGPT, while others risk being left further behind.

Recent studies reinforce this view. Students with stronger digital skills in areas like problem‑solving and digital ethics tend to use generative AI more frequently (Caner‑Yıldırım, 2025). In parallel, work using frameworks such as DigComp has mostly focused on measuring gaps in students’ digital skills – often showing that perceived “digital natives” are less uniformly proficient than we might think (Lucas et al, 2022). What we know much less about is the reverse relationship: can carefully designed uses of AI actually develop students’ digital competences – and for whom?

In a recent article, we addressed this question empirically by analysing the impact of a generative AI intervention on university students’ digital competences (García & Pallarés, 2026). Students’ skills were assessed using the European DigComp 2.2 framework (Vuorikari et al, 2022).

Moving beyond static measures of digital competence

Research on students’ digital competences in higher education has expanded rapidly over the past decade. Yet much of this work still treats digital competence as a stable attribute that students bring with them into university, rather than as a dynamic and educable capability that can be shaped through instructional design. The consequence is a field dominated by one-off assessments, surveys and diagnostic tools that map students’ existing skills but tell us little about how those skills develop.

This predominant focus on measurement rather than development has produced a conceptual blind spot: we know far more about how digital competences predict students’ use of emerging technologies than about how educational uses of these technologies might enhance those competences in the first place.

Recent studies reinforce this asymmetry. Students with higher levels of digital competence are more likely to engage with generative AI tools and to display positive attitudes towards their use (Moravec et al, 2024; Saklaki & Gardikiotis, 2024). In this ‘competence-first’ model, digital competence appears as a precondition for productive engagement with AI. Yet this framing obscures a crucial pedagogical question: might AI, when intentionally embedded in learning activities, actually support the growth of the very competences it is presumed to require?

A second limitation compounds this problem: the absence of a standardised framework for analysing and comparing the effects of AI-based interventions on digital competence development. Although DigComp is widely used for diagnostic purposes, few studies employ it systematically to evaluate learning gains or to map changes across specific competence areas. As a result, evidence from different interventions remains fragmented, making it difficult to identify which aspects of digital competence are most responsive to AI-mediated learning.

There is, nevertheless, emerging evidence that AI can do more than simply ‘consume’ digital competence. Studies by Dalgıç et al (2024) and Naamati-Schneider & Alt (2024) suggest that integrating tools such as ChatGPT into structured learning tasks can stimulate information search, analytical reasoning and critical evaluation—provided that students are guided to question and verify AI outputs rather than accept them uncritically. Yet these contributions remain exploratory. We still lack experimental or quasi-experimental evidence that links AI-based instructional designs to measurable improvements in specific DigComp areas, and we know little about whether such benefits accrue equally to all students or disproportionately to those who already possess stronger digital skills.

This gap matters. If digital competences are conceived as malleable rather than fixed, then AI is not merely a technology that demands certain skills but a pedagogical tool through which those skills can be cultivated. This reframing shifts the centre of the debate: away from asking whether students are ready for AI, and towards asking whether our teaching practices are ready to use AI in ways that promote competence development and reduce inequalities in learning.

Our study: teaching students to work with AI, not around it

We designed a randomised controlled trial with 169 undergraduate students enrolled in a Microeconomics course. Students were allocated by class group to either a treatment or a control condition. All students followed the same curriculum and completed the same online quizzes through the institutional virtual campus.

The crucial difference lay in how generative AI was integrated:

  • In the treatment condition, students received an initial workshop on using large language models strategically. They practised:
  • contextualising questions
  • breaking problems into steps
  • iteratively refining prompts
  • and checking their own solutions before turning to the AI.
  • Throughout the course, their online self-assessments included adaptive feedback: instead of simply marking answers as right or wrong, the system offered hints, step-by-step prompts and suggestions on how to use AI tools as a thinking partner.
  • In the control condition, students completed the same quizzes with standard right/wrong feedback, and no training or guidance on AI.

Importantly, the intervention did not encourage students to outsource solutions to AI. Rather, it framed AI as an interactive study partner to support self-explanation, comparison of strategies and self-regulation in problem solving.

We administered pre- and post-course questionnaires aligned with DigComp 2.2, focusing on five competences: information and data literacy, communication and collaboration, safety, and two aspects of problem solving (functional use of digital tools and metacognitive self-regulation). Using a difference-in-differences model with individual fixed effects, we estimated how the probability of reporting the highest level of each competence changed over time for the treatment group relative to the control group.

What changed when AI was taught and used in this way?

At the overall sample level, we found statistically significant improvements in three areas:

  • Information and data literacy – students in the AI-training condition were around 15 percentage points more likely to report the highest level of competence in identifying information needs and carrying out effective digital searches.
  • Problem solving – functional dimension – the probability of reporting the top level in using digital tools (including AI) to solve tasks increased by about 24 percentage points.
  • Problem solving – metacognitive dimension – a similar 24-point gain emerged for recognising what aspects of one’s digital competences need to be updated or improved.

In other words, the AI-integrated teaching design was associated not only with better use of digital tools, but also with stronger awareness of digital strengths and weaknesses – a key ingredient of autonomous learning. Communication and safety competences also showed positive but smaller and more uncertain effects. Here, the pattern becomes clearer when we look at who benefited most.

A compensatory effect: AI as a potential leveller, not just an amplifier

When we distinguished students by their initial level of digital competence, a pattern emerged. For those starting below the median, the intervention produced large and significant gains in all five competences, with improvements between 18 and 38 percentage points depending on the area. For students starting above the median, effects were smaller and, in some cases, non-significant.

This suggests a compensatory effect: students who began the course with weaker digital competences benefited the most from the AI-based teaching design. Rather than widening the digital gap, guided use of AI acted as a levelling mechanism, bringing lower-competence students closer to their more digitally confident peers.

Conceptually, this challenges an implicit assumption in much of the literature – namely, that generative AI will primarily enhance the learning of already advantaged students, because they are the ones with the skills and confidence to exploit it. Our findings show that, when AI is embedded within intentional pedagogy, explicit training and structured feedback, the opposite can happen: those who started with fewer resources can gain the most.

From ‘allow or ban’ to ‘how do we teach with AI?’

For higher education policy and practice, the implications are twofold.

First, we need to stop thinking of digital competence purely as a prerequisite for using AI. Under the right design conditions, AI can be a pedagogical resource to build those competences, especially in information literacy, problem solving and metacognitive self-regulation. That means integrating AI into curricula not as an add-on, but as part of how we teach students to plan, monitor and evaluate their learning.

Second, our results suggest that universities concerned with equity and digital inclusion should focus less on whether students have access to AI tools (many already do) and more on who receives support to learn how to use them well. Providing structured opportunities to practise prompting, to critique AI outputs and to reflect on one’s own digital skills may be particularly valuable for students who enter university with lower levels of digital confidence.

This does not resolve all the ethical and practical concerns around generative AI – far from it. But it shifts the conversation. Instead of treating AI as an external threat to academic integrity that must be tightly controlled, we can start to ask:

  • How can we design tasks where the added value lies in asking good questions, justifying decisions and evaluating evidence, rather than in producing a single ‘correct’ answer?
  • How can we support students to see AI not as a shortcut to avoid thinking, but as a tool to think better and know themselves better as learners?
  • Under what conditions does AI genuinely help to close digital competence gaps, and when might it risk opening new ones?

Answering these questions will require further longitudinal and multi-institutional research, including replication studies and objective performance measures alongside self-reports. Yet the evidence we present offers a cautiously optimistic message: teaching students how to use AI can be part of a strategy to strengthen digital competences and reduce inequalities in higher education, rather than merely another driver of stratification.

Concepción González García is Assistant Professor of Economics at the Faculty of Economics and Business, Catholic University of Murcia (UCAM), Spain, and holds a PhD in Economics from the University of Alicante. Her research interests include macroeconomics, particularly fiscal policy, and education.

Nina Pallarés is Assistant Professor of Economics and Academic Coordinator of the Master’s in Management of Sports Entities at the Faculty of Economics and Business, Catholic University of Murcia (UCAM), Spain. Her research focuses on applied econometrics, with particular emphasis on health, labour, education, and family economics.


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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.