Michel C. Desmarais

Learn More
The application of user-expertise modeling for adaptive interfaces is confronted with a number of difficult challenges, namely, efficiency and reliability, the cost-benefit ratio, and the practical usability of user modeling techniques. We argue that many of these obstacles can be overcome by standard, automatic means of performing knowledge assessment.(More)
C onsider how attractive and valuable a support system would be if it allowed any individual to enter a new job without prior training, and if it would gradually bring this individual to higher performance levels than those achieved by traditional initial training sessions. Not only would such a system offer savings in training costs, but it would also(More)
Bayesian networks are commonly used in cognitive student modeling and assessment. They typically represent the item-concepts relationships, where items are observable responses to questions or exercises and concepts represent latent traits and skills. Bayesian networks can also represent concepts-concepts and concepts-misconceptions relationships. We(More)
Identifying the skills that determine the success or failure to exercises and question items is a difficult task. Multiple skills may be involved at various degree of importance. Skills may overlap and correlate. Slip and guess factors affect item outcome and depend on the profile of the student's skill mastery and on item characteristics. In an effort(More)
People-to-people recommendation differ from item recommendations in a number of ways, one of which is that individuals add information to their profile which is often critical in determining a good match. The most critical information can be in the form of free text or personal tags. We explore text-mining techniques to improve classical collaborative(More)
In recent years, learner models have emerged from the research laboratory and research classrooms into the wider world. Learner models are now embedded in real world applications which can claim to have thousands, or even hundreds of thousands, of users. Probabilistic models for skill assessment are playing a key role in these advanced learning(More)
This paper describes an algorithmic means for inducing implication networks from empirical data samples. The induced network enables eecient inferences about the values of network nodes if certain observations are made. This implication induction method is approximate in nature as probablistic network requirements are relaxed in the construction of(More)
The process of deciding which skills are involved in a given task is tedious and challenging. Means to automate it are highly desirable , even if only partial automation that provides supportive tools can be achieved. A recent technique based on Non-negative Matrix Factorization (NMF) was shown to offer valuable results, especially due to the fact that the(More)
Despite their many successes, Intelligent Tutoring Systems (ITS) are not yet practical enough to be employed in the real world educational/training environments. We argue that this undesirable scenario can be changed by focusing on developing an ITS development methodology that transforms current ITS research to consider practical issues that are part of(More)
Probabilistic and learned approaches to student modeling are attractive because of the uncertainty surrounding the student skills assessment and because of the need to automatize the process. Item to item structures readily lend themselves to probabilistic and fully learned models because they are solely composed of observable nodes, like answers to test(More)