1,773 research outputs found

    A graph-based approach for learner-tailored teaching of Korean grammar constructions

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    NLP-based personal learning assistant for school education

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    Computer-based knowledge and computation systems are becoming major sources of leverage for multiple industry segments. Hence, educational systems and learning processes across the world are on the cusp of a major digital transformation. This paper seeks to explore the concept of an artificial intelligence and natural language processing (NLP) based intelligent tutoring system (ITS) in the context of computer education in primary and secondary schools. One of the components of an ITS is a learning assistant, which can enable students to seek assistance as and when they need, wherever they are. As part of this research, a pilot prototype chatbot was developed, to serve as a learning assistant for the subject Scratch (Scratch is a graphical utility used to teach school children the concepts of programming). By the use of an open source natural language understanding (NLU) or NLP library, and a slackbased UI, student queries were input to the chatbot, to get the sought explanation as the answer. Through a two-stage testing process, the chatbot’s NLP extraction and information retrieval performance were evaluated. The testing results showed that the ontology modelling for such a learning assistant was done relatively accurately, and shows its potential to be pursued as a cloud-based solution in future

    Program Synthesis using Natural Language

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    Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks. We present a general framework for constructing program synthesizers that take natural language (NL) inputs and produce expressions in a target DSL. The framework takes as input a DSL definition and training data consisting of NL/DSL pairs. From these it constructs a synthesizer by learning optimal weights and classifiers (using NLP features) that rank the outputs of a keyword-programming based translation. We applied our framework to three domains: repetitive text editing, an intelligent tutoring system, and flight information queries. On 1200+ English descriptions, the respective synthesizers rank the desired program as the top-1 and top-3 for 80% and 90% descriptions respectively

    A Comparison Between BLEU and METEOR Metrics Used for Assessing Students within an Informatics Discipline Course

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    AbstractThis paper is proposing a very brief review of the Intelligent Tutoring System general structure and the use of Natural Language Processing techniques within such system. Also, this paper is proposing a review of the comparison performed by the author between the use of the Natural language Processing metrics, BLEU and METEOR algorithms, for students knowledge assessment module, within an Intelligent Tutoring System developed by the author for teaching, learning and assessing students with applicability to the course of Programming of Computers and C language

    The development and analysis of extended architecture model for intelligent tutoring systems

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    Intelligent Tutoring Systems (ITS) are computer programs that use leamers" knowledge level to providing indĂ­vidualized education. ITS research has successfully delivered systems efficiently supporting one-to-one tutoring. Most of these systems are actively used in real-worid settings and have even contributed to changing traditional education curricula. Instructional activities, learning examples, exploring interactive simulations and playing educational games can benefit from individualized computer-based assistance. To enhance ongoing research related to the improvement of tutoring, we present an extended knowledge mode! including besides the standard modules a common shared database and knowledge-based background, too. The external databases can improve the guality of the behavior models both in tutor and student models. The Python programming language and OWL are efficient tools to combine the ontology management and machine leaming functions to develop ITS systems. In this Paper, we survey ITS technologies andpresent a novel extended architecture model for Intelligent e-Tutoring Systems
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