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

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Die Nachfolge im Familienunternehmene have developed a high fidelity prototype of FIRST using 10 years of student data in our College. As part of our iterative design process, we performed a focus group study with six advisors following a demonstration of the prototype. Our focus group evaluation highlights the sensemaking value in th

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Traumatic-Grief

Die Familie als Gegenstand der Ethnologiewe trained off-task dialogue models for text messages from a group chat feature integrated into C. I.: E.J., a collaborative game-based learning environment for middle school ecosystem science. We evaluate the effectiveness of the off-task dialogue models, which use different word embeddings (i.e.,

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Zur Geschichte der Familienwissenschafts of students’ natural language responses to reflection prompts—GloVe, ELMo, tf-idf, unigrams—across several machine learning-based regression techniques (i.e., random forest, support vector machine, multi-layer perceptron) to assess the depth of student reflection responses. Results demonstrate tha

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Familienzentren in Nordrhein-Westfalents were significantly better when our ontology was used to support the gamification. The learning outcomes were significantly better in scripted CL sessions gamified through our approach, with positive correlations to the intrinsic motivation and perceived choice. Based on these results, we can stat

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Kapitel 10: Die deutsche Rechtslagesuperior error correction, but also much faster training and prediction. On a benchmark dataset of 4K buggy programs collected from actual student submissions, . outperforms existing methods by 20% at suggesting fixes for popular errors while being competitive or better at other errors. . offers a t

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Kapitel 9: Das Recht der Europ?ischen Unionn that successful projects in makerspaces necessitate both individual and group efforts. Furthermore, such aggregation and display of information can aid instructors in uncovering the state of student learning in makerspaces. Identifying the instances and diversity of collaborative interactions affo

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https://doi.org/10.1007/978-3-642-82629-0time with the exhibit in a science museum. We demonstrate that the proposed Bayesian hierarchical modeling approach outperforms competitive baseline techniques. These findings point toward significant opportunities for enriching our understanding of visitor engagement in science museums with multimo

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https://doi.org/10.1007/978-3-642-82629-0arn mathematics can improve intrinsic motivation. Finally, we show that tailoring to both player type and motivation profiles can improve intrinsic motivation, and decrease amotivation, compared to a single adaptation only based on learner motivation. We discuss the implications of our findings rega