Model Based Systems Development is a promising research area for the production of systems dedicated to Human Learning. Our research focuses on developing tools and techniques for producing models of Learning situations called cooperative Problem-Based Learning (PBL). Our works led us to propose a UML profile dedicated to such learning situations. The profile is built on the CPM (Cooperative Pr...

construction industry consists of several phases in which a variety of stakeholders are involved. as construction projects are becoming larger, more complex and more diverse, the design phase has been more important factor for the success of projects than ever before. however, it is considered that most of design work occurred in actual design process is intangible. such recognition makes the d...

introduction: problem-based learning (pbl) as a learning style has gained a special position amongst different levels of education systems, and many different approaches, such as tutor education, proper scenario presentation, etc., are used to increase its efficiency. however, the role of homogeneous groups to facilitate team working has never been studied. the purpose of this study is to exami...

Nowadays, technology-enhanced learning systems must have the ability to reuse learning resources from distributed repositories, to take into account the context and to allow dynamic adaptation to different learners (individuals, dyad or team) based on substantial advances in pedagogical theories and knowledge models. We focus on learning systems using a problem-based learning approach represent...

Nowadays, technology-enhanced learning systems must have the ability to reuse .learning resources from distributed repositories, to take into account the context and to allow dynamic adaptation to different learners based on substantial advances in pedagogical theories and knowledge models. We focus on learning systems using a problem-based learning approach represented by scenarios. In our fra...

Naturalistic driving data (NDD) are valuable for testing autonomous systems under various conditions. Automatically identifying scenes from high-dimensional and unlabeled NDD remains a challenging task. This paper presents novel approach automatically test scenarios through deep unsupervised learning. Firstly, US DAS2 leveraged, the selection of variables representing vehicle state surrounding ...

We investigate the connections between compression learning and scenario based optimization. We first show how to strengthen, or relax the consistency assumption at the basis of compression learning and study the learning and generalization properties of the algorithm involved. We then consider different constrained optimization problems affected by uncertainty represented by means of scenarios...