Adaptive Web-Based Instruction for Enhancing Learning Ability

Abstract : Web technology in an instructional environment is primarily dedicated to distributing course materials to supplement traditional classroom learning. It also uses designed intelligence to adapt to learners' specific needs. The main purposes of this study were to construct and determine the efficiency of adaptive web-based instruction for LIS students. The web-based content was designed to adapt to three levels of learning ability: high, moderate and low. The system automatically collects data concerning each learner's behavior, determines the level of learning ability and provides suitable content for each learner. In addition, this web-based instruction evaluated using 80/80 standard efficiency criteria and compared the learning achievement of the students who learned through the adaptive WBI and those who learned through traditional methods. Finally, a study was conducted to evaluate students' level of satisfaction with adaptive web-based instruction. The sample consisted of 60 undergraduate students from Srinakharinwirot University, majoring in Library and Information Science. Research instruments included adaptive web-based instruction, achievement test, and satisfaction evaluation form. The findings revealed that the adaptive web-based instruction met the efficiency criteria at 80.78/81.17. The learning achievement of students who learned through adaptive web-based instruction was significantly higher than those who learned through traditional methods at 0.01 level, and they were satisfied with the web-based instruction at a good level.
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Zhongzhi Shi; Sunil Vadera; Agnar Aamodt; David Leake. 6th IFIP TC 12 International Conference on Intelligent Information Processing (IIP), Oct 2010, Manchester, United Kingdom. Springer, IFIP Advances in Information and Communication Technology, AICT-340, pp.145-153, 2010, Intelligent Information Processing V. 〈10.1007/978-3-642-16327-2_20〉
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Wawta Techataweewan. Adaptive Web-Based Instruction for Enhancing Learning Ability. Zhongzhi Shi; Sunil Vadera; Agnar Aamodt; David Leake. 6th IFIP TC 12 International Conference on Intelligent Information Processing (IIP), Oct 2010, Manchester, United Kingdom. Springer, IFIP Advances in Information and Communication Technology, AICT-340, pp.145-153, 2010, Intelligent Information Processing V. 〈10.1007/978-3-642-16327-2_20〉. 〈hal-01055062〉

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