Symposium Smart Learning Environments

Abstract

The relationship between technology and supporting learning is complex. Technology offers various possibilities to support learning, such as presenting information, practising knowledge and skills, personalising learning environments, fostering collaboration, organising education, and supporting teachers. Based on recent studies conducted by our research group, I will explore the factors that influence the effectiveness of technology for learning, with a particular focus on the educational design of technological tools, their implementation by teachers, and their use by students. In conclusion, several key considerations will be discussed that contribute to a thoughtful and sustainable implementation of technology in education.

Bio

Fien Depaepe is a full professor at KU Leuven and campus dean of the Faculty of Psychology and Educational Sciences at Kulak. She is affiliated with the Center for Instructional Psychology and Technology (CIP&T) and with itec, an imec research group at KU Leuven. Fien Depaepe’s research focuses on the effectiveness of technology-enhanced learning environments in various educational contexts. Her research program places particular emphasis on the instructional design, implementation, and evaluation of digitally personalized learning environments.

Abstract

As learning analytics dashboards become more prevalent in education, teachers need data literacy skills to effectively interpret and apply the data to improve
student learning. In this presentation, Manel van Kessel and Stefanie Vanbecelaere will discuss a new data literacy tool they developed to support teachers in navigating and utilising dashboard data. Their research addresses a critical gap in existing data literacy assessments by focusing on the skills necessary for interpreting and applying complex data in educational settings. The session will highlight how this tool aims to empower teachers and inform the development of future data literacy instruments.

Bio's

Manel van Kessel is an educational scientist and advisor at Kennisnet, as well as an external PhD candidate at Leiden University’s ICLON Graduate School of Teaching. With a Master’s degree in Educational Sciences, she focuses on the intersection of technology and education, particularly the effective use of teacher dashboards and adaptive learning technologies. Her research explores how educators can leverage data-informed tools to support student learning, emphasizing the necessary skills, knowledge, and contextual conditions for successful implementation. At Kennisnet, she contributes to national discussions on digitalization in education, including topics like smartphone policies and data literacy. Manel actively collaborates with educators, researchers, and policymakers to promote responsible and effective integration of technology in classrooms.

Stefanie Vanbecelaere is a postdoctoral researcher at itec, a research group of KU Leuven and imec at the Kulak campus in Kortrijk. Her research focuses on the effectiveness of digital technologies, with particular attention to adaptive and personalized learning. She has co-developed and validated the Teacher Data Literacy Instrument (TDLI). Stefanie’s work mainly targets primary education and examines how technologies such as dashboards and educational games can contribute to the development of fundamental skills such as early numerical and reading skills. Currently, she is an FWO researcher studying teacher’s professional competence to use dashboards for the promotion of personalized learning.

Abstract

This talk explores how digital learning environments can serve as powerful tools for understanding children's mathematical and cognitive development. Focusing on Rekentuin, a game-based platform designed to support early math learning, we investigate how children's performance across different games reveals patterns in skill acquisition and developmental trajectories. A central aim is to identify reliable ways to measure progress within playful, low-stakes learning settings. By analysing in-platform data, we uncover how different types of tasks may relate to one another and what this can tell us about broader cognitive abilities. This research highlights how technology can be used not only to deliver educational content but also to generate meaningful insights into how children learn, providing valuable information that can inform both research and practice. The work demonstrates the value of interdisciplinary approaches that bring together
developmental psychology, data science, and educational design.

Bio

Abe Hofman conducts research on the development of mathematical and cognitive skills in children. A central aspect of this work involves investigating how these abilities can be reliably measured, providing a foundation for addressing broader questions about developmental trajectories. For instance, current research explores whether there is a relationship between children's progress across different games within the digital learning platform Rekentuin.

Brenda Jansen is an associate professor in the Developmental Psychology group at the University of Amsterdam. Her research focuses on the motivational and affective factors of learning, especially in learning mathematics. Her primary research interests include math anxiety, academic motivation, and curiosity. She is one of the PIs in the NRO research project on teacher math anxiety and a member of the advisory board of Edaptiv, an interuniversity alliance of edtech researchers.

Abstract

When educational AI tools enter the classroom, teachers' pedagogical and didactic actions begin to shift. For instance, when planning lessons or differentiating instruction with an adaptive learning technology (ALT), some actions teachers once carried out themselves may be replaced, complemented or augmented by the AI. Studying these evolving forms of teacher-AI collaboration provides insights into how AI functionalities and teachers’ practices co-evolve over time. What remains missing is a shared conceptual language to describe these changes in teachers’ practice. In this session, we will first introduce a conceptualisation of teaching as macro-pedagogical-didactic actions (e.g., lesson preparation or lesson orchestration) that can be broken down into micro subtasks (e.g., grouping students or interpreting AI recommendations) using hierarchical task analysis (Annett, 2003; Stanton, 2006). At this more granular level, it becomes possible to describe whether subtasks disappear, remain the same, change, or are entirely new in the presence of educational AI tools. We will then illustrate how this framework can be applied with findings from a study on how teachers use Snappet, an adaptive learning technology designed for curriculum adaptivity (De Mooij et al., 2025). We will also challenge participants to apply this shared conceptual language to their own cases, exploring together how AI reconfigures pedagogical practices in the classroom. 

Bio's

Anne-Wil Kramer is a postdoctoral researcher at the Adaptive Learning Lab of the BSI and NOLAI (National Education Lab AI) with a background in pedagogical science and cognitive neuropsychology. At NOLAI, she works on TAICo (Teacher-AI-Complementarity), an EU-funded project on teacher-AI collaboration. Her research examines how AI is transforming teachers’ work by analysing which tasks and skills disappear, remain, shift, or newly emerge. Using task- and skill-analysis methods, she identifies where workload changes occur and where new skill gaps emerge, providing a basis for targeted professional development and educational AI design.

Susanne de Mooij is a postdoctoral researcher at the Adaptive Learning Lab (ALL) within the Behavioural Science Institute (BSI) at Radboud University, and serves as the data science lead at the National Education Lab AI (NOLAI). Her research focuses on improving intelligent learning technologies to enhance students’ learning experiences, with a particular interest in the role of self-regulated learning. This is also part of her work as one of the coordinators of the Centre for Learning and Living with AI (CELLA). At NOLAI, she explores how human-AI collaboration can be effectively integrated into adaptive learning systems, using large (and occasionally messy) longitudinal datasets.

Abstract

Adaptive learning technologies increasingly rely on AI to tailor instruction to individual learners. To support teachers in this process, Learning Analytics Dashboards— also known as LADs —offer insights into students’ learning progress. However, the design of these dashboards varies widely and often lacks a solid theoretical foundation in learning and instruction. This session presents findings from an ongoing study within a joint PhD project (KU Leuven & Radboud University) focused on developing theory-informed and actionable LADs. We introduce a classification scheme that integrates insights from both computer science and educational research to analyse the current landscape of teacher-facing dashboards in Dutch-speaking primary education. Over 30 tools from 20 EdTech providers in the Netherlands and Flanders were mapped using this scheme, based on interviews with developers and teachers. Participants will gain insight into current dashboard design and implementation practices and explore how future LADs can better support teacher decision-making in everyday classroom practice. The session concludes by highlighting how a focus on actionable design recommendations can guide the development of more effective hybrid human-AI systems in education.

Bio

Ann Fastré is a PhD researcher at KU Leuven (CIP&T and itec, an imec research group) and Radboud University (Adaptive Learning Lab and NOLAI). She combines several professional experiences: she started out teaching Dutch as a second language in adult education, trained preservice teachers in second/foreign language acquisition didactics at a university college and worked at KU Leuven's central educational development unit. Ann also played a key role in the Flemish i-Learn project for facilitating digital personalised learning in compulsory education throughout Flanders. Her current research focuses on theory-informed actionable learning analytics dashboards to support data-informed teaching in adaptive learning environments.

Bio

Inge Molenaar is the director of the National Education Lab AI (NOLAI) and professor of Education and Artificial Intelligence at the Behavioural Science Institute at Radboud University in the Netherlands. She has over 20 years of experience in technology-enhanced learning, taking multiple roles from entrepreneur to academic.

Her research focuses on technology-empowered innovations to optimize human learning and teaching. The application of data, Learning Analytics and Artificial
Intelligence in understanding how learning unfolds over time is central to her work. Artificial Intelligence offers a powerful way to measure, understand and design innovative learning scenarios. Dr Molenaar envisions Hybrid Human-AI Learning Technologies that augment human intelligence with artificial intelligence to empower learners and teachers in their quest to make education more efficient, effective and responsive.

In the National Education Lab AI (NOLAI), a collaboration between the Dutch government, schools, research and industry, we believe that a multistakeholder,
interdisciplinary approach is essential to develop the next generation educational technologies and a shared understanding of responsible use of AI in education.

Dr Molenaar has received multiple grants among which an ERC Starting Grant to develop the first Hybrid Human-AI Regulation system to train young learners’
Self-regulated learning skills with AI and she is a Jacobs Foundation Fellow. she holds Master’s degrees in Cognitive Psychology and International Business studies and a PhD in Educational Sciences (University of Amsterdam).