Titlebook: Responsive and Sustainable Educational Futures; 18th European Confer Olga Viberg,Ioana Jivet,Tina Papathoma Conference proceedings 2023 The

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山崩

,Broader and Deeper: A Multi-Features with Latent Relations BERT Knowledge Tracing Model,al technology. Traditional knowledge tracing algorithms generally use one or a few features to predict students’ behaviour and do not consider the latent relations between these features, which could be limiting and disregarding important information in the features. In this paper, we propose MLFBK:

吹牛大王

,Why You Should Give Your Students Automatic Process Feedback on Their Collaboration: Evidence from dividual students. As in other forms of learning, students need feedback on the quality of their work. Still, writing high-quality informative feedback requires time on the part of educators. This makes it less feasible to provide quality feedback at scale. Given the importance of group dynamics in

动作谜

,Interactive Web-Based Learning Materials Vs. Tutorial Chatbot: Differences in User Experience, tutors that support learning through dialogues. In this work, we explore how user experience differs between learning via web-based interactive content and learning within a dialogue led by a computational tutorial agent. To this purpose, we conducted a study with 31 master students of inclusive ed

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亚麻制品

Early Prediction of Learners At-Risk of Failure in Online Professional Training Using a Weighted Voincluding classroom instruction, practical experience, and online training. While online training has the potential to reach a wider audience, a lack of interaction and support may lead to low engagement and high risk of failure. In this paper, we propose a solution to predict, as early as possible,

征兵

,How to Characterize and Analyze the Computational Thinking Skills of a Learning Game?,velop the SPY game. In this paper we conduct a didactic analysis of the game and we propose a tool to describe the competencies targeted by the game based on game mechanics. The proposed tool has been applied to describe 21 of the 26 PIAF skills (a skills base on computational thinking). We show how

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evince

,Exploring Indicators for Collaboration Quality and Its Dimensions in Classroom Settings Using Multiation quality and its aforementioned dimensions. Also, our results from clustering provide indications of the potential of video data for identifying different levels of collaboration quality (e.g., high, low, medium). The findings have implications for building collaboration quality monitoring and guiding systems for authentic classroom settings.