Last year, the reflections in my columns for ETENjournal were centred around themes like belonging in education, neurodiversity in the classroom, and the empowerment of children and young people. All these topics share a common concern: How can we create educational environments in which all learners really have the opportunity to participate, develop and flourish?

This week, I started a new professional chapter. I joined the research group ‘Learning Technology & Analytics’ at the university where I already work, The Hague University of Applied Sciences. This start feels both exiting and slightly unfamiliar. My background lies primarily in pedagogy, inclusive education, and performing arts. Learning analytics, machine learning, and educational data science belong to a different – and rapidly evolving – domain of educational research and practice.

The more I explore this field, the more I begin to see connections with questions that have occupied colleagues and myself for decades. In the inaugural lecture of this research group’s leader, Prof. Theo Bakker, the central issue is captured in a powerful phrase: ‘No fairness without awareness.’ Learning analytics aims to analyse educational data in order to understand patterns in student participation, study progress, and outcomes. When used in a responsible way, such analysis can help to identify inequalities that may otherwise remain invisible. Data can reveal for example which groups of students and pupils experience higher dropout rates, which learning pathways are most successful, and where structural barriers may exist in educational systems.

For those of us who are committed to inclusive education, this opens new and interesting perspectives. Traditionally, discussions on inclusion have heavily relied on qualitative data: experiences in classrooms, stories of teachers, or case studies of individual pupils. These perspectives remain essential. However, data driven approaches may complement them by revealing patterns in a systematic way. Learning analytics can give insights in ways in which institutional structures support (or hinder) equal participation.

At the same time, these technological developments raise important questions. Algorithms and data are not neutral. Educational data sets often reflect historical inequalities, and predictive models may unintentionally reproduce existing biases. Researchers working with learning analytics therefore emphasise the need to analyse fairness within educational data and machine-learning models to ensure that technologies do not reinforce the very inequalities they aim to address.

Artificial intelligence and adaptive technologies are already being explored as tools to support inclusive learning environments. Recent studies suggest that AI-driven systems can personalise learning pathways, support students with disabilities via assertive technologies, and give real-time feedback that is tailored to individual needs. In theory, such technologies could help educators respond more effectively to the diversity of learners in their classrooms.

However, theory alone is not enough. As teacher educators, we must make sure that technological innovation remains grounded in pedagogical values. Inclusive education ultimately about human relationships, trust, and participation. Technology should support these goals rather than replace them. Learning analytics can help us understand learning processes more clearly, but it cannot replace the professional judgement and ethical responsibility of educators.

For me personally, entering the world of learning technology and analytics therefore feels less like leaving my previous work and more like an expansion of it. Questions about belonging, empowerment, and inclusion remain central. What changes are the tools we use to explore these questions.

Maybe, the most important challenge for the coming years will be building bridges between fields that have often developed apart from each other: pedagogy, data science, educational technology, and teacher education. If we succeed in doing so, learning analytics may help us to develop a richer understanding of how inclusive education can be realised more effectively. And who knows …. the next step in creating classrooms of belonging may not only begin with listening to students’ voices, but also with learning how to read the stories hidden within educational data.

Michel Hogenes, March 2026

The Hague University of Applied Sciences

Categories: Debate, News