There are many approaches to improve what we do in education, with a sometimes overwhelming number of internal and external factors that bring opportunities and challenges. Learning Engineering (an engineering-inspired approach to educational design, as opposed to the study of engineering) provides a method to tackle these opportunities and challenges in a collaborative, data-informed and iterative way. At the Online Learning Summit 2025 (OLS25) at the University of Leeds, I was delighted to chair a panel on Learning Engineering following a brief introduction by Jim Goodell, editor of the Learning Engineering Toolkit. This post summarises the panel discussion and I’m grateful for the contributions of Julie Lindsay and Maria Soledad Ramirez-Montoya, who provided excellent examples of educational practice and collaboration in action.
Why is Learning Engineering a useful approach?
The strategic and existential imperative for higher education was outlined by Prof. Martin Bean at the start of OLS25, setting an agenda that needs to be skills-based, responsive and relevant to shifting external contexts. There is a need to translate that overall strategy into practical steps within the context of learners and a particular institution, and this is where Learning Engineering can come in.
“Learning engineering is a process and practice that applies the learning sciences, using human-centred engineering design methodologies and data-informed decision-making, to support learners and their development.” Kessler, et al. (2023).
As Goodell explained, the process of Learning Engineering is driven by challenges situated in context. I find the language of ‘engineering’ appealing as it implies applying science within a situation, adjusting parameters, but ultimately being responsive to new information and adapting. Although there is a balance needed in the use of language that appears too mechanistic or detached from the innate human experience of learning. When considering the educational system, including how that system has to evolve and adapt, it is not possible to have a complete view without the inclusion of the human elements of learners and educators.
“We can look at all the attributes of individual learners… the learning context and modalities… and the physical, social and human-tech environment whether they help or hinder learning… and institutional context of faculty workload, organisational processes, organisational leadership…” Jim Goodell (OLS25).
Learning Engineering works with human complexity with both the learner-centered focus of challenges, set within the complexity of multi-layered context, and recognition of the collaboration of people required to work together to ‘investigate, create and implement’ around a central challenge. Data and evidence informs choices and iterations on a solution and these come from both the learning sciences and from learners in context. Learning Engineering is therefore also encouraging educators to develop an understanding of learning science to inform the design of learning and embrace the use of learning analytics (and, I would argue, other forms of data, including qualitative judgements) to respond to learners and adapt the learning process accordingly.
Education as iteration
Indeed, one of the appeals of Learning Engineering is the core idea of iteration. At the 2023 Online Learning Summit, Aaron Kessler showed the way the learning design team at MIT had adopted Learning Engineering to address the shortcomings of established publishing models of courses (see OLS23 write up). Education is a highly iterative process itself, requiring educators and learners to be responsive to progress and performance in learning. The Learning Engineering model (illustrated below – text description via ICICLE) emphasises that iteration, but is wholly dependent on a key challenge around which the iteration occurs. Kessler emphasised the need to define and explore the challenge as part of the process, rather than start with a solution, as this gives room for collaborative input, data-informed decisions and creativity.
The visual shows the system with a feedback loop as Goodell explained, but also the idea that a challenge may have multiple parts to it that each iterate and have layers of context around them. There is therefore not an abstraction of complexity, but a way to work within a complex context by focusing on a specific challenge and understanding how it is situated within a context. This contextualised challenge I think needs to be seen as some form of two-way symbiosis (shown by two headed arrow representation throughout the diagram), in that addressing the challenge is not just dependent on the context, but can also shape it. Moving from applying Learning Engineering to a teaching and learning activity, up to bigger picture institutional systems still requires the same principles of evidence, collaboration and iteration. With that mindset, there are opportunities to radically shift institutional constraints and bridge both internal and external contexts, leading to the transformational outcomes alluded to by Bean.
The importance of collaboration
In the session, participants were asked to start to identify the challenges with establishing a fully competency-driven, microcredential-based educational system. The panel summarised the discussion they heard with the following challenges highlighted:
- How to integrate with existing institutional structures, curriculum frameworks and systems?
- How to design microcredentials within established programmes?
- How to guide students from developing a range of skills and competences into specific careers and employment?
- What are the ‘right’ skills and competences to focus on when developing the portfolio?
There were many more challenges, but just taking the above areas of discussion as examples, it is clear that none of these can be answered by a single area of expertise. Each requires, at the very broad level, a combination of strategic, educational and operational perspectives. Yet, as Julie Lindsay observed, many of the challenges expressed by participants did not yet focus on the practical aspects of teaching or the learners’ experience of this new educational paradigm. To me, this requires more conscious facilitation of discussions where the perspectives not in the room need to be brought in, such as with students present in design discussions. Conscious facilitation is also where the role of learning designers comes in, where they are able to bring in student experience perspectives and the educational design of learning journeys, and bring in different colleagues’ expertise.
“Roles in learning engineering teams:Learning sciences; assessment, measurement, evaluation expertise; subject-matter expertise; data science; software engineering; learning experience design; learning environment engineering; education and training professionals.” Goodell and Kolodner (2023).
I think learning designers also have a role to play in bringing a shared understanding and a focus on a design challenge. As Goodell indicated, “teams need a shared understanding. In some of these domains, the same words mean different things.” It’s therefore crucial to avoid assumptions that meanings are shared and even that everyone has an agreed understanding of the design objectives.
Brining together expertise
The importance of collaboration was summarised by Marisol Ramirez-Montoya, in that collaboration has to be the model for the future of education and it is how we can work with complexity. Drawing on an award-winning, large-scale, international collaboration, she explained how having a broad range of disciplines and countries involved provide a rich experience for learners. Furthermore, the data collected from tens of implementations across the globe enabled a range of narratives and reports tailored to different stakeholders. This is clear recognition that educational systems are situated in a much larger context, including the need to work within funding, policy and social requirements. What sustained the collaboration across different countries and more than 10,000 students, was sharing a similar interest and motivation for engagement: to look for a new solution for society.
But what happens when an institution doesn’t have a full range of expertise? This was a question posed by the session audience.
To answer this, Goodell pulled the focus back to the defined challenge being addressed and what specific expertise is required of that challenge. If that expertise isn’t within the organisation, then it requires more flexibility in financial models and HR policies to be able to look beyond, towards local and international colleagues, consultants and evidence. As a complement to in-person expertise, Ramirez-Montoya recommended the use of open educational resources (OERs). This is a highly scalable way to share and bring in knowledge for both internal capacity building and the process of teaching itself. Lindsay offered a perspective for smaller institutional teams that are dependent on the upskilling of academic colleagues to build institutional capacity in a range of educational practices. This is a model that I have favoured too, particularly through working together with colleagues who have a deep interest in educational theories, adopt a reflective approach to the evaluation of teaching and learning, and ultimately wanting to challenge the way things are done or have their own thinking challenged. Many institutions have learning and teaching cultures and communities that colleagues across both academic and professional services roles jointly develop. In these spaces, the sharing of practice of both successes and perceived failures offer a wealth of learning.
Data-informed education
Reflection on learning and reflexivity in teaching should be informed by data. Goodell reiterated that the feedback in the Learning Engineering cycle is key. He noted that in the past there was mainly post-hoc data, however learning analytics provides real-time data to respond to. In response to my prompt that data-led decisions in education seems too detached from the individual, human experience, Goodell explained that we cannot know what is happening inside our learner’s minds. Instead we have to use learner data, such as where they click, how they respond to quizzes and other interactions that surface their learning externally. This includes not just performance data, but affective states, which can be used proactively to support students and prevent drop-out. I would argue that this live data, particularly for online asynchronous learning, allows for much faster iterations. As Lindsay asserted “asynchronous is the glue that binds online learning together”, as this mode of education enables flexible, individually-paced and social learning that can bridge formal study and workplace learning. There is more time for educators to respond to learners asynchronously than on-the-fly in a lecture room. Therefore there are benefits not just to the learner, but also the educator, where asynchronous learning is designed with a view for responsiveness and data to iterate the experience. Lindsay also suggested the importance of data on the educator as well.
“There are techniques, methods and approaches that teachers in an online environment must learn, and students must learn how to learn online. You can have the best designed course, but if it’s not taught in a way that encourages engagement… and if students are not taught how to engage… it’s not going to be as good as it should be.” Julie Lindsay (OLS25).
Often though the challenge is having a learning environment that permits rapid adjustments. With asynchronous learning in particular and highly prescribed learning design aligning activities to assessment (with sound pedagogical rationale), the opportunity to flex and adapt to learners can be a challenge. Course design, structures and development approaches need to consider educator ability to adapt to the cohort. Choice made over tools and content presentation can either enable or hinder an iterative approach. This itself may be the focus of a Learning Engineering project.
Learning to learn
The final theme to take away from the panel discussion is how learners are supported to learn. Following Lindsay’s point (quote above) that students need to know how to learn online, and with reference to Bean’s “enduring human skills”, Goodell suggested that institutions need to be doing a better job of helping students develop those skills. For many learners moving into online education, particularly the emergent demographic of mid-career progression or career switchers, the process of learning remotely, self-motivating and interacting with others for learning is new and different from prior educational experience. For example, in one of my previous articles (Cornock, 2017) I suggested the skill of ‘conversational thinking’ for students to be able to proactively and meaningfully engage in social learning. There are other skills required and, as Lindsay remarked, that applies to educators too. This goes as far as really thinking more creatively about the type of asynchronous interaction in the learning experience.
Videos or articles as content, combined with discussion boards for interaction, are akin to the campus lecture with seminar model. That type of course structure is a higher education trope. It’s predictable and doesn’t work for every discipline or for every learning context.
“The discussion board is old hat.” Julie Lindsay, (OLS25).
New forms of online learning spaces and multimedia based asynchronous communication can bring nuance through more human connection and more authentic interactions. Whatever is chosen really does need to respond to learners and the context of the learning.
Summary
The concept of Learning Engineering and thinking about a core challenge to focus on, bringing expertise together in interdisciplinary and multi-profession teams, and really centering practice on learners in context gives educational teams a practical approach to take new educational paradigms forward. Whilst the learning sciences inform our decisions, the experience of learning and teaching isn’t an exact science, because of the complexity of the humans involved. Each learner brings their own context, including skills for learning, prior learning and personal learning goals. Educators have to be responsive to reflect, adapt and refine. Learning Engineering is a collaborative, iterative and situated professional practice, which, after all, seems to be a pretty good description of effective education.
References
- Bean, M. (2025). Transforming education: seizing opportunities in a disrupted world. Online Learning Summit, 10-11 July 2025, University of Leeds. OLS25.
- Cornock, M. (2017). Conversational thinking for online learning.
- Goodell, J., Cornock, M., Lindsay, J. and Ramirez-Montoya, M.S. (2025). Learning Engineering as Engine of Change. Online Learning Summit, 10-11 July 2025, University of Leeds. OLS25.
- Goodell, J. and Kolodner, J. (eds.) (2023). Learning Engineering Toolkit. Abingdon: Routledge. Open access chapters.
- Kessler, A. (2023). Applying the Learning Engineering Process: Continually and Iteratively Supporting Online Learning. Online Learning Summit, University of Leeds, 10-11 July 2023. OLS23.
- Kessler, A., Craig, S. D., Goodell, J., Kurzweil, D. and Greenwald, S. W. (2023). Learning Engineering as a Process. In Goodell, J. and Kolodner, J. (eds.) (2023). Learning Engineering Toolkit. Abingdon: Routledge.
Acknowledgements
I chaired this panel and attended OLS25 in my capacity as Head of Online Learning, University of Leeds.
Header image: cropped from visual notes of the panel discussion by Buttercrumble. Full image available at OLS25 Resources.
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