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FaSEF provides theoretical and practical training to prepare for the CAPAES for teachers currently employed at universities, adult education institutions offering higher-level programs, at higher arts schools, or any member of the academic or scientific staff responsible for teaching at a university, even if they do not previously hold a teaching credential.

As part of the interdisciplinary course “AI: Challenges and Opportunities,” students at UNamur presented a series of posters focusing on the uses of artificial intelligence in their fields of study. This highlight showcased the diversity of AI applications, as well as the University of Namur’s commitment to training students to use it critically, fairly, and thoughtfully.

Information technology plays a significant role in our daily lives. Life without a computer or cell phone seems unimaginable to us. But information technology serves many other fields, such as medicine, management, the environment, agriculture, space, biology… and its role in new sectors is constantly growing. Get ready to shape the future of our society in a young, dynamic, and rapidly expanding field  

During the academic year, the Cellule interfacultaire d'appui pédagogique (CIAP) team offers 3 types of group support to develop effective working method strategies.

Join a community of innovators, explore cutting-edge technologies, and get ready to shape the digital world of tomorrow! Ready to code your success? Welcome to the Faculty of Computer Science at UNamur, a pioneer in university-level computer science education in Europe. 

A Collaboration Between UNamur, Hénallux, and IFEC Image Image Image Since September 2022, Hénallux and UNamur have been collaborating with IFEC (Institut de Formation de l’Enseignement Catholique) with the goal of raising awareness and training secondary education professionals in differentiated instruction, as advocated in the Pact for Educational Excellence. To this end, various approaches have been designed and implemented: professional development days in schools, follow-up visits to schools that request them, training programs for teachers serving as differentiation liaisons, and training programs for CSA (support and guidance counselors).The agreement is coordinated by Sandrine Biémar (UNamur) and Alain Bultot (Hénallux), and the research team consists of Anne-Catherine Dessy (Hénallux), Anne Libert (UNamur), Virginie Meyer (UNamur), and Sylvie Van der Linden (Hénallux).  Background This collaborative research is part of a broader project focused on differentiated instruction, which encompasses several tracks with different objectives and target audiences. These include, in particular, professional development programs in schools; training for lead teachers designed to equip them to facilitate the development of differentiated instruction practices in their schools; training for all IFEC CSA staff, as well as regular meetings with IFEC’s in-house trainers interested in this topic. Collaborative research is part of this ongoing effort, in which each track feeds into the others. It aims to anchor the discussion of differentiation in classroom practices through a mutual enrichment of theory and practice. In practice School Tours: Teacher training and follow-up sessions lasting from one to several days CSA Program: A 4-day training session for all CSAs and the IFEC Recommended itineraries: 4-day training program to form pairs of school liaisons, plus 1 day Trainer Training Programs: community of practice with in-house trainers; differentiation at IFEC Collaborative Research Track: Supporting teachers over the course of a school year in implementing differentiated instruction The Objectives Empower teachers by mobilizing and interpreting objective data collected in the field.Collaborative research is built through negotiation between participants and researchers. Therefore, each stage of the research is developed jointly to align as closely as possible with the concerns and challenges related to differentiated instruction that arise in each participant’s specific context.For example, the research question and sub-questions are co-formulated to best address the concerns and challenges in the field regarding the impact of a differentiated teaching approach on students’ motivation and autonomy. Methodology This research is inspired by the protocol developed by Schildkamp (2018, 2019) within the “Data TEAMS.” It aims to develop and promote teachers’ agency through decision-making based on data collection and the evaluation of classroom practices.The data generated by this research project will inform reference and training programs. In addition, data and outputs from this collaborative research will also be shared with colleagues and organizations representing various stakeholders (schools, CSA). Want to participate? We are looking for pairs of teachers from the same school, which will help facilitate the implementation of the process within each participating school. The TopicDeveloping and adjusting differentiation strategies based on classroom data.ObjectivesUnderstanding and Taking ActionUnderstand, through the analysis of available classroom data and the exchange of practices among professionals, the effects of a differentiated teaching approach on students’ motivation and autonomy.To objectify one’s intuition in order to act effectively.Format7 sessions throughout the year at the Salle des Pros (7 Rue Godefroid—in downtown Namur)Why participate?To enrich research by drawing on real-world classroom practices.To enhance your teaching practices with the guidance of researcher-trainers.To meet other teachers who share your concerns. Contacts For more information, please feel free to contact the research teamAnne-Catherine Dessy: anne-catherine.dessy@henallux.be Virginie Meyer: virginie.meyer@unamur.beSylvie Van der Linden: sylvie.vanderlinden@henallux.be Project Coordinator Sandrine Biémar: sandrine.biemar@unamur.be 

Abstract Predictive maintenance (PdM) is increasingly being used to improve system reliability, reduce downtime, and optimize operating costs in complex industrial environments through anomaly detection. As industrial systems become more complex, with components interacting with one another and monitored by ever-expanding sensor networks, anomaly detection faces new methodological challenges. In particular, there is a gap in the literature regarding anomaly detection using traditional machine learning (ML) methods based on a Single Label Classification (SLC) approach, which is unsuitable when multiple failures can occur simultaneously.Traditional anomaly detection approaches based on ML methods using an SLC formulation assume that each observation belongs to a single anomaly category. This assumption does not hold when multiple failures occur simultaneously on the same equipment, resulting in a loss of information. We begin by illustrating this limitation through a concrete example showing that using an SLC approach instead of a multi-label formulation degrades the classification of simultaneous anomalies. To address this issue, anomaly detection is reformulated as a Multi-Label Classification (MLC) problem, enabling the detection of multiple failures within a single time step. Following this reformulation, the issue of selecting multi-label classifiers suited to the context of Predictive Maintenance (PdM) must be examined. The general conclusion from the literature is that the performance of classifiers depends heavily on the context and that while there are many detection methods, none is universally dominant. Although in-depth comparative studies exist in the field of MLC, datasets from complex systems are absent from these comparisons, even though they exhibit specific characteristics such as multivariate time-series structures, significant label imbalance, and complex interactions between components.To address this gap, a structured and reproducible evaluation protocol is proposed to evaluate eight state-of-the-art ML methods in an MLC framework across three public industrial datasets. The results confirm that the performance of classifiers depends heavily on the characteristics of the dataset and that no single method consistently outperforms others across all scenarios. However, conducting this type of comparison for each new industrial context is costly in terms of time, computational resources, and data requirements, which limits its feasibility for real-world industrial deployment. The thesis also addresses this issue through dimensionality reduction and variable selection methods. PM systems generate large multivariate datasets in which only a subset of the variables is actually relevant for anomaly detection. A self-adaptive evolutionary strategy is proposed to perform wrapper-style variable selection and obtain a subset containing only the most informative variables. Experimental results show that reducing the variable space improves computational efficiency and, in most cases, anomaly prediction performance. A comparison of the proposed method with state-of-the-art metaheuristic approaches on three PdM datasets and five anomaly detection methods confirms its competitiveness in terms of predictive performance and the optimal size of the selected feature subset. Finally, feature space reduction not only optimizes the resources required for detection but also reduces the number of signals that technicians must analyze during root cause investigations. This simplification aims to strengthen confidence in the system. Finally, the thesis addresses the practical aspects of deploying detection methods in complex systems. Among these, there is a lack of trust and transparency among maintenance technicians and decision-makers. An analysis of the interpretability and robustness of anomaly detection methods is provided to directly contribute to their deployment in real-world conditions. First, the proposed variable selection strategy is evaluated on a public PdM dataset using SHAP-based explanations to verify the consistency between the selected variables and those identified as the most important by tools dedicated to interpretability. The results show that the selected variables are consistent with those identified as the most important by these tools. Furthermore, visual analysis of the most influential variables reveals that anomalies are associated with a limited and well-defined subset of sensor signals, which helps improve transparency and strengthen confidence in automatic detection systems. At the same time, it is necessary to conduct a study of the robustness of multi-label classifiers in controlled sensor degradation scenarios, including different types of degradation and various levels of severity. The results show that the performance of classifiers can deteriorate significantly, particularly in the presence of gradual drifts. To mitigate this effect, introducing sensor degradation during training appears to be a relevant strategy. Finally, this thesis proposes a structured framework for multi-label anomaly detection in complex systems, covering classification methods, feature space optimization, interpretability, and robustness. For each of these aspects, the thesis adopts an industrial perspective and takes into account the differences between controlled laboratory experimental conditions and the additional constraints of real-world complex environments. The jury Prof. Katrien Beuls - University of Namur, BelgiumProf. Elio Tuci - University of Namur, BelgiumProf. Patrick Heymans - University of Namur, BelgiumProf. Jenni Raitoharju - University of Jyväskylä, FinlandMr. Fabio Pinna - Telespazio, BelgiumMr. Vito Trianni - ISTC-CNR, Italy Sign me up

Défense de thèse - Antoine Hubermont 04/09/2026 à 15h à l'auditoire PA02. Name First name E-mail address Will attend the reception following the defense Yes ( optional ) No ( optional ) Need a parking sticker Yes ( optional ) No ( optional ) Would like a certificate for defense assistance Yes ( optional ) No ( optional ) In order to process your request, you must complete all fields marked "optional". When you submit this form, the completed data will be transmitted to UNamur and used to process your request. Learn more about your data protection and your rights This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Are you a professor, teaching assistant, or education professional looking to change the way you teach, modify your assessment methods, find new ways to motivate your students, revitalize your teaching practice, or receive support for an inspiring project? The PUNCh team is here to support and advise you on making pedagogical changes to your teaching and assessment practices. The goal? To inspire and support teachers in their innovative teaching approaches. 

Faced with the ecological crisis and soaring energy prices, the energy transition has become an undeniable emergency. Every day, at UNamur, researchers from a wide range of fields - geology, chemistry, physics, computer science - are thinking about innovative ways of dealing with this perilous future.

On January 23 and 24, 2025, UNamur experts were present at the SETT (School Education Transformation Technology) trade show for its sixth edition. A must-attend event for digital education in the Wallonia-Brussels Federation, dedicated to principals, teachers and technical-pedagogical advisors.

Jamie Wright, a researcher at the Namur Digital Institute at UNamur, participated in a study conducted by Pompeu Fabra University (UPF) in Barcelona and published in the prestigious journal *Nature Human Behaviour*.  The study shows that languages around the world tend towards lexical economy, reusing words to denote different concepts when doing so does not lead to communicative confusion.