Titlebook: Artificial Intelligence in Education; 21st International C Ig Ibert Bittencourt,Mutlu Cukurova,Eva Millán Conference proceedings 2020 Sprin

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Using Motion Sensors to Understand Collaborative Interactions in Digital Fabrication Labsn to work on their projects, facilitators have to strike a difficult balance between micromanaging them and letting the community support itself. In this paper, we explore how Kinect sensors can continuously monitor students’ collaborative interactions so that instructors can gain a more comprehensi

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Student Dropout Predictioninstitutions, and being able to predict it could help to alleviate its social and economic costs. To address this problem we developed a tool that, by exploiting machine learning techniques, allows to predict the dropout of a first-year undergraduate student. The proposed tool allows to estimate the

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Impact of Methodological Choices on the Evaluation of Student Modelssetting. Such issues may seem like technical details, and they do not get much attention in published research. Nevertheless, their impact on experiments can be significant. We report experiments with six models for predicting problem-solving times in four introductory programming exercises. Our foc

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Using Neural Tensor Networks for Open Ended Short Answer Assessment systems. The approach was evaluated on data collected using a dialogue based intelligent tutoring system (ITS). Particularly, we have experimented with different assessment models that were trained using features generated from knowledge graph embeddings derived with NTN. Our experiments showed tha

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To Tailor or Not to Tailor Gamification? An Analysis of the Impact of Tailored Game Elements on Learenerally accepted that in order to be effective, gamification should be tailored to users. Currently, most systems adapt by assigning different game elements based on a single learner profile (e.g. dominant player type, personality or gender). However, there is no study yet that analyse the effect o