Mónica Trella

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Student assessment is a very important issue in educational settings. The goal of this work is to develop a web-based tool to assist teachers and instructors in the assessment process. Our system is called SIETTE, and its theoretical bases are Computer Adaptive Testing and Item Response Theory. With SIETTE, teachers worldwide can define their tests, and(More)
In this paper we present an extension of a previously developed generic student model based on Bayesian Networks. A new layer has been added to the model to include prerequisite relationships. The need of this new layer is motivated from different points of view: in practice, this kind of relationships are very common in any educational setting, but also(More)
As a consequence of the increasing importance of distance education there is a growing interest in the application of intelligent techniques to existing web-based educational systems. Many researchers are focusing their efforts on reusing high quality educational software and material to take advantage of their sound theoretical foundations and effective(More)
In this paper we propose the use of Bayesian Networks as a theoretical framework for Computerized Adaptive Tests. To this end, we develop the Bayesian Network that supports the Adaptive Testing Algorithm, that is, we define what variables should be taken into account, what kind of relationships should be established among them, and what are the required(More)
This paper introduces a set of resources that provide web learning environments with student modeling services. SAMUEL is a user modeling server for registering, updating and maintaining student knowledge data from different sources that use their own ontologies. In order to make inferences about student knowledge, it becomes necessary to establish(More)