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Architecture of a context-aware and adaptive learning schedule for learning Java

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Yau, Jane Yin-Kim and Joy, Mike (2007) Architecture of a context-aware and adaptive learning schedule for learning Java. In: 7th IEEE International Conference on Advanced Learning Technologies, Niigata, JAPAN, JUL 18-20, 2007. Published in: 7TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES, PROCEEDINGS pp. 252-256.

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Abstract

Novice programmers require large amounts of time and motivation to learn an object-oriented programming language such as Java. In this paper, the architecture of our Context-aware and Adaptive Learning Schedule (CALS) tool is described. The tool has been designed to focus initially on supporting first Year computer science undergraduate students to become more proficient Java programmers, and makes use of a learning schedule, where the learner inputs their daily activities. Based on this information, the tool is able to automatically determine the contextual features such as the location and available time. The appropriate learning materials are selected for the students according to, firstly, the learner preferences (such as learning styles), and secondly the contextual features (such as the level of concentration).

Item Type: Conference Item (UNSPECIFIED)
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
L Education
L Education > LB Theory and practice of education
Journal or Publication Title: 7TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES, PROCEEDINGS
Publisher: IEEE COMPUTER SOC
ISBN: 978-0-7695-2916-5
Editor: Spector, JM and Sampson, DG and Okamoto, T and Kinshuk, X and Cerri, SA and Ueno, M and Kashihara, A
Date: 2007
Number of Pages: 5
Page Range: pp. 252-256
Identification Number: 10.1109/ICALT.2007.72
Publication Status: Published
Title of Event: 7th IEEE International Conference on Advanced Learning Technologies
Location of Event: Niigata, JAPAN
Date(s) of Event: JUL 18-20, 2007
URI: http://wrap.warwick.ac.uk/id/eprint/31014

Data sourced from Thomson Reuters' Web of Knowledge

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