8,789 research outputs found

    Towards an Intelligent Tutor for Mathematical Proofs

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    Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453

    MOSAIC: A Model for Technologically Enhanced Educational Linguistics

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    Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

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    “This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe

    Learner Modelled Environments

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    Learner modelled environments (LMEs) are digital environments that are capable of automatically detecting learner’s behaviours in relation to a specific knowledge domain, to reason about those behaviours in order to asses learner’s performance, skills, socio-emotional and cognitive needs, and to act accordingly in a pedagogically appropriate manner. Digital environments that possess such capabilities are typically referred to as Intelligent Learning Environments, or more traditionally – as Intelligent Tutoring Systems (ITSs)

    New Trends in Second Language Learning and Teaching through the lens of ICT, Networked Learning, and Artificial Intelligence

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    In the last few decades, Information and Communications Technology (ICT) applications have been shaping the field of Computer Assisted Language Learning (CALL). Mobile Assisted Language Learning (MALL) paved the way for ubiquitous learning. The advent of new technologies in the early 21st century also added a social dimension to ICT that allowed for Networked Learning (NL). Given that language learning is fundamentally a socio-cultural experience, networked learning capabilities have provided the potential for language learning in community settings. This has revitalized the earlier frameworks provided by CALL. NL has empowered language learners today to connect globally, to access Open Educational Resources, and to self-regulate their learning processes beyond the scope of traditional curricula. In parallel, the rising pervasiveness of Artificial Intelligence (AI) applications and their relevance to language learning has led CALL to branch out into Intelligent CALL (ICALL). The first section of this article provides a brief historical overview of CALL, examines it through the lens of ICT, networked learning, and open access. The second section focuses on the implications of AI for creating new trends in second language education, the challenge for providing customization at scale, and raises important issues related to transparency and privacy for future research
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