Claudia Mazziotti

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Machine Learning methods for Performance Prediction in Intelligent Tutoring Systems (ITS) have proven their efficacy; specific methods, e.g. Matrix Factorization (MF), however suffer from the lack of available information about new tasks or new students. In this paper we show how this problem could be solved by applying Transfer Learning (TL), i.e.(More)
Affect plays a significant role in students’ learning behaviour. Positive affective states can enhance learning, while negative ones can inhibit it. This paper describes how we provide intelligent support in a learning platform based on affect states. We discuss two components: an affective state detector to perceive affective states in speech during(More)
Robust knowledge consists of both conceptual and procedural knowledge. In order to address both types of knowledge, offering students opportunities to explore target concepts in an exploratory learning environment (ELE) is insufficient. Instead, we need to combine exploratory learning environments, to support students acquisition of conceptual knowledge,(More)
Productive Failure (PF) – comprising initial problem solving and delayed instruction – has been proven effective for learning when compared to Direct Instruction (DI) in multiple studies with high school and university students. Although the problem-solving phase is usually implemented in a collaborative setting, the role of collaboration for the(More)
We describe the design of the Invention Coach, an intelligent, exploratory learning environment (ELE) to support Invention, an exploratory learning activity. Our design is based on a two-pronged approach. Our own study of naturalistic teacher guidance for paper-based Invention uncovered phases in the Invention process. Prior research on the mechanisms of(More)
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