Publications
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Learning how to behave: an analysis of the behavioural changes induced by public support for R&D. KU Leuven
This research seeks to shed light on the behavioural changes both firms and individuals go through when faced with a complex R&D policy environment. In a first instance, I show that complexity leads companies to mimic their peers’ decisions to adopt newly introduced tax exemptions. Implementing an innovative approach to identify this effect, I rely on economic homophyly and geographical proximity to identify networks of firms, and show that ...
Why do learning goals (not) work: a reexamination of the hypothesized effectiveness of learning goals based on students' behaviour and cognitive processes Institute of Tropical Medicine
The effect of cues for calibration on learners' self-regulated learning through changes in learners’ learning behaviour and outcomes KU Leuven
Literature on blended learning emphasizes the importance of self-regulation for learning in blended learning environments and the role of learners’ calibration. Although literature on calibration is clear on its importance for self-regulated learning, it provides inconclusive insight in the effect of support for calibration on learners’ self-regulated learning. One under-investigated avenue might be learners’ ability to enact on the cues ...
Why do learning goals (not) work: a reexamination of the hypothesized effectiveness of learning goals based on students’ behaviour and cognitive processes KU Leuven
Explicitly informing students about learning goals has been argued to foster intentional learning. This study tested three theoretical hypotheses regarding the facilitating effects of learning goals by investigating 72 students’ actual reading behaviour and cognitive processes (i.e., the use of learning goals). Participants studied a text presented on 15 separate computer screens with/without learning goals. In line with the three hypotheses, ...
Cognitive feedback and behavioral feedforward automation perspectives for modeling and validation in a learning context KU Leuven
State-of-the-art technologies have made it possible to provide a learner with immediate computer-assisted feedback by delivering a feedback targeting cognitive aspects of learning, (e.g. reflecting on a result, explaining a concept, i.e. improving understanding). Fast advancement of technology has recently generated increased interest for previously non-feasible approaches for providing feedback based on learning behavior observations by ...
Modelling Context-Sensitive Dynamic Activity-Travel Behavior Under Conditions of Uncertainty Incorporating Reinforcement Learning, Habit Formation, And Behavioral and Cognitive Adaptation Strategies Hasselt University
This paper develops a framework for modelling the dynamic formation of location choicesets. The proposed framework integrates three key concepts, namely aspiration, activation and expected utility. Aspirations are defined at the level of attributes of choice alternatives and represent an individual’s beliefs about performance levels that potentially can be achieved. Activation levels are defined at the level of choice alternatives and represent ...
Learning and clustering of fuzzy cognitive maps for travel behaviour analysis Hasselt University
In modern society, more and more attention is given to the increase in public transportation or bike use. In this regard, one of the most important issues is to find and analyse the factors influencing car dependency and the attitudes of people in terms of preferred transport mode. Although the individuals’ transport behavioural modelling is a complex task, it has a notable social and economic impact. Thus, in this paper, fuzzy cognitive maps ...
Machine Learning Insights on Driving Behaviour Dynamics among Germany, Belgium, and UK Drivers Hasselt University
The i-DREAMS project has a core objective: to establish a comprehensive framework that defines, develops, and validates a context-aware 'Safety Tolerance Zone' (STZ). This zone is crucial for maintaining drivers within safe operational boundaries. The primary focus of this research is to conduct a detailed comparison between two machine learning approaches: long short-term memory networks and shallow neural networks. The goal is to evaluate the ...