• Title/Summary/Keyword: Intelligent tutorial environment

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Tangible Media based on Interactive Technology: A Tutorial

  • Yoon, Joongsun;Yoh, Myeungsook;Lee, Hyewon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.4 no.2
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    • pp.241-248
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    • 2004
  • Recent paradigm in technology shifts from object-based technology to environment-based technology. Issue here is interaction among humans and the world around humans. The world we consider includes natural and artificial "space." Interactive technology, which explore holistic interactions based on "Mom (embodiment)," suggests a good starting point for exploring this issue. Soft engineering, "Mom," holistic interactions, tangible space, ubiquitous computing, science of emotion, and interactive media are key concepts in interactive technology. Interactive tangible media "iT_Media" is proposed to explore and synthesize these ideas. Interactive technology initiative (ITI) is an interdisciplinary research group to search for the proper technology and the proper way of implementing technology: "interactive technology" or "soft engineering." Some experimental activities conducted by ITI are presented in this paper.tal activities conducted by ITI are presented in this paper.

A Mathematics Tutoring Model That Supports Interactive Learning of Problem Solving Based on Domain Principles (공식원리에 기반한 대화식 문제해결 학습을 지원하는 수학교수 모형)

  • Kook, Hyung-Joon
    • The KIPS Transactions:PartB
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    • v.8B no.5
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    • pp.429-440
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    • 2001
  • To achieve a computer tutor framework with high learning effects as well as practicality, the goal of this research has been set to developing an intelligent tutor for problem-solving in mathematics domain. The maine feature of the CyberTutor, a computer tutor developed in this research, is the facilitation of a learning environment interacting in accordance with the learners differing inferential capabilities and needs. The pedagogical information, the driving force of such an interactive learning, comprises of tutoring strategies used commonly in various domains such as phvsics and mathematics, in which the main contents of learning is the comprehension and the application of principles. These tutoring strategies are those of testing learners hypotheses test, providing hints, and generating explanations. We illustrate the feasibility and the behavior of our propose framework with a sample problem-solving learning in geometry. The proposed tutorial framework is an advancement from previous works in several aspects. Firstly, it is more practical since it supports handing of a wide range of problem types, including not only proof types but also finding-unkown tpes. Secondly, it is aimed at facilitating a personal tutor environment by adapting to learners of varying capabilities. Finally, learning effects are maximized by its tutorial dialogues which are derived from real-time problem-solving inference instead of from built-in procedures.

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A Tutorial: Information and Communications-based Intelligent Building Energy Monitoring and Efficient Systems

  • Seo, Si-O;Baek, Seung-Yong;Keum, Doyeop;Ryu, Seungwan;Cho, Choong-Ho
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.7 no.11
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    • pp.2676-2689
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    • 2013
  • Due to increased consumption of energy in the building environment, the building energy management systems (BEMS) solution has been developed to achieve energy saving and efficiency. However, because of the shortage of building energy management specialists and incompatibility among the energy management systems of different vendors, the BEMS solution can only be applied to limited buildings individually. To solve these problems, we propose a building cluster based remote energy monitoring and management (EMM) system and its functionalities and roles of each sub-system to simultaneously manage the energy problems of several buildings. We also introduce a novel energy demand forecasting algorithm by using past energy consumption data. Extensive performance evaluation study shows that the proposed regression based energy demand forecasting model is well fitted to the actual energy consumption model, and it also outperforms the artificial neural network (ANN) based forecasting model.