1,905 research outputs found

    Adaptive User Interfaces for Intelligent E-Learning: Issues and Trends

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    Adaptive User Interfaces have a long history rooted in the emergence of such eminent technologies as Artificial Intelligence, Soft Computing, Graphical User Interface, JAVA, Internet, and Mobile Services. More specifically, the advent and advancement of the Web and Mobile Learning Services has brought forward adaptivity as an immensely important issue for both efficacy and acceptability of such services. The success of such a learning process depends on the intelligent context-oriented presentation of the domain knowledge and its adaptivity in terms of complexity and granularity consistent to the learner’s cognitive level/progress. Researchers have always deemed adaptive user interfaces as a promising solution in this regard. However, the richness in the human behavior, technological opportunities, and contextual nature of information offers daunting challenges. These require creativity, cross-domain synergy, cross-cultural and cross-demographic understanding, and an adequate representation of mission and conception of the task. This paper provides a review of state-of-the-art in adaptive user interface research in Intelligent Multimedia Educational Systems and related areas with an emphasis on core issues and future directions

    Instruction based on computer simulations

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    Excerpts available at Google Books. For integral text, see publisher's website : http://www.routledge.com/books/details/9780415804615/"Introduction : In the scientific debate on what is the best approach to teaching and learning, a recurring question concerns who should lead the learning process, the teacher or the learner (see e.g., Tobias & Duffy, 2009) ? Poistions takens vary from a preference for direct, expository, teacher-led instruction (Kirschner, Sweller, & Clark, 2006) to fully open student-centered approaches that can be called pure discovery methods (e.g., Papert, 1980), with intermediate positions represented by more or less guided discovery methods (e.g., Mayer, 2004). This discussion also is a recurring theme in this chapter." (http://books.google.fr/books?id=cCD_thHjuxEC&pg=PA446&lpg=PA446&dq=Instruction+based+on+computer+simulations+de+jong&source=bl&ots=tOJ7FdkZow&sig=s8W6OnyU3H7iRLm7wqISfu6CAYE&hl=fr&ei=AZGATviHDMuV0QXewI3KCQ&sa=X&oi=book_result&ct=result&resnum=3&ved=0CDoQ6AEwAg#v=onepage&q=Instruction%20based%20on%20computer%20simulations%20de%20jong&f=false

    Proceedings of the 1993 Conference on Intelligent Computer-Aided Training and Virtual Environment Technology

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    The volume 2 proceedings from the 1993 Conference on Intelligent Computer-Aided Training and Virtual Environment Technology are presented. Topics discussed include intelligent computer assisted training (ICAT) systems architectures, ICAT educational and medical applications, virtual environment (VE) training and assessment, human factors engineering and VE, ICAT theory and natural language processing, ICAT military applications, VE engineering applications, ICAT knowledge acquisition processes and applications, and ICAT aerospace applications

    Learning Support Tools for Developing Spatial Abilities in Engineering Design

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    This paper presents some learning support tools for developing spatial abilities in engineering design students. They are based on the previous authors' experience in teaching traditional Engineering Graphics and uses both on-line and batch computer-aided generation of 3D models from 2D freehand sketches. Although these applications can be used with standards PCs, they are intended to be used on Tablet-PCs to provide an experience similar to sketching on real paper. The objectives of these applications are to develop three important elements for the future engineer: spatial visualization, freehand sketching and normalized view generation. The authors present the results of a pilot study that has been realized in some Spanish universities. Two well-known tests for evaluating spatial abilities: Mental Rotation Test (MRT) and the Differential Aptitude TestÐ Spatial Relations subset (DAT-SR), have been used to validate the pilot study.1) Spanish Ministry of Science and Education and the European Union (Project DPI2004-01373) 2) Fundacio Caixa Castelló-Bancaixa under the Universitat Jaume I program for Research Promotion (Project P1-1B2004-02, titled `Gestural interface for the introduction of variational-parametric sketches and for the definition of assem- bly conditions in the computer-aided design of industrial products')

    Modeling The Influences Of Personality Preferences On The Selection Of Instructional Strategies Inintelligent Tutoring Systems

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    This thesis hypothesizes that a method for selecting instructional strategies (specifically media) based in part on a relationship between learning style preference and personality preference provides more relevant and understandable feedback to students and thereby higher learning effectiveness. This research investigates whether personality preferences are valid predictors of learning style preferences. Since learning style preferences are a key consideration in instructional strategies and instructional strategies are a key consideration in learning effectiveness, this thesis contributes to a greater understanding of the relationship between personality preferences and effective learning in intelligent tutoring systems (ITS). This research attempts to contribute to the goal of a truly adaptive ITS by first examining relationships between personality preferences and learning style preferences; and then by modeling the influences of personality on learning strategies to optimize feedback for each student. This thesis explores the general question what can personality preferences contribute to learning in intelligent tutoring systems? So, why is it important to evaluate the relationship between personality preferences and learning strategies in ITS? While one-on-one human tutoring is still superior to ITS in general, this approach is idiosyncratic and not feasible to deliver to [any large population] in any cost-effective manner. (Loftin, 2004). Given the need for ITS in large, distributed populations (i.e. the United States Army), it is important to explore methods of increasing ITS performance and adaptability. Findings of this research include that the null hypothesis that there is no dependency between personality preference variables and learning style preference variables was partly rejected. Highly significant correlations between the personality preferences, openness and extraversion, were established for both the active-reflective and sensing-intuitive learning style preferences. Discussion of other relationships is provided
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