{"title":"基于多智能分析的移动电子学习预测系统设计","authors":"T. Kaewkiriya","doi":"10.7903/IJECS.1413","DOIUrl":null,"url":null,"abstract":"This research aimed to design a mobile e-Learning forecasting system based on case studies using Multiple Intelligence analysis. The framework is divided into six modules. The first module describes a Patterns-based module. The second module describes the student Mapping module. The third module presents the Forecasting module. The fourth module presents the Learning portal module. The fifth module explains the Adaptive module. The last module presents the learning content based on the case study modules. In addition this paper introduces an example of an operational framework. The evaluation is comprised of two sections. The first section evaluates student achievement. The second section evaluates student prediction precision. The results of the first evaluation showed that students who studied via the mobile e-Learning forecasting system are more successful than students who studied via the normal e-Learning system with significance at 0.05 (Group 1, t = 7.577, p < 0.05; Group 2, t = 3.684, p < 0.05; Group 3, t = 15.190, p < 0.05). For the second section, the results considered the prediction precision percentage. We compared three algorithms: 1) J48 algorithm, 2) ID3 algorithm, and the 3) NaA¯ve Bayes algorithm. The J48 algorithm had the highest value at 88.286%. To cite this document: Thongchai Kaewkiriya, \"Design of a mobile e-learning forecasting system based on a case study using multiple intelligence analysis\", International Journal of Electronic Commerce Studies, Vol.7, No.2, pp.189-200, 2016. Permanent link to this document: http://dx.doi.org/10.7903/ijecs.1413 Â","PeriodicalId":38305,"journal":{"name":"International Journal of Electronic Commerce Studies","volume":"7 1","pages":"189-200"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"DESIGN OF A MOBILE E-LEARNING FORECASTING SYSTEM BASED ON A CASE STUDY USING MULTIPLE INTELLIGENCE ANALYSIS\",\"authors\":\"T. Kaewkiriya\",\"doi\":\"10.7903/IJECS.1413\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research aimed to design a mobile e-Learning forecasting system based on case studies using Multiple Intelligence analysis. The framework is divided into six modules. The first module describes a Patterns-based module. The second module describes the student Mapping module. The third module presents the Forecasting module. The fourth module presents the Learning portal module. The fifth module explains the Adaptive module. The last module presents the learning content based on the case study modules. In addition this paper introduces an example of an operational framework. The evaluation is comprised of two sections. The first section evaluates student achievement. The second section evaluates student prediction precision. The results of the first evaluation showed that students who studied via the mobile e-Learning forecasting system are more successful than students who studied via the normal e-Learning system with significance at 0.05 (Group 1, t = 7.577, p < 0.05; Group 2, t = 3.684, p < 0.05; Group 3, t = 15.190, p < 0.05). For the second section, the results considered the prediction precision percentage. We compared three algorithms: 1) J48 algorithm, 2) ID3 algorithm, and the 3) NaA¯ve Bayes algorithm. The J48 algorithm had the highest value at 88.286%. To cite this document: Thongchai Kaewkiriya, \\\"Design of a mobile e-learning forecasting system based on a case study using multiple intelligence analysis\\\", International Journal of Electronic Commerce Studies, Vol.7, No.2, pp.189-200, 2016. 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引用次数: 2
摘要
本研究旨在利用多元智能分析,设计一个基于案例研究的移动电子学习预测系统。该框架分为六个模块。第一个模块描述了一个基于模式的模块。第二个模块描述了student Mapping模块。第三个模块是预测模块。第四个模块介绍了学习门户模块。第五部分介绍了Adaptive模块。最后一个模块是基于案例研究模块的学习内容。此外,本文还介绍了一个操作框架的示例。评估由两个部分组成。第一部分评估学生的成绩。第二部分评估学生的预测精度。第一次评估结果显示,通过移动e-Learning预测系统学习的学生比通过正常e-Learning系统学习的学生更成功,差异有统计学意义(p < 0.05)(第一组,t = 7.577, p < 0.05;第二组,t = 3.684, p < 0.05;第三组,t = 15.190, p < 0.05)。对于第二部分,结果考虑了预测精度百分比。我们比较了三种算法:1)J48算法,2)ID3算法和3)朴素贝叶斯算法。J48算法最高,为88.286%。引用本文:Thongchai Kaewkiriya,“基于多元智能分析的移动电子学习预测系统设计”,《国际电子商务研究》,Vol.7, No.2, pp.189- 200,2016。本文档的永久链接:http://dx.doi.org/10.7903/ijecs.1413 Â
DESIGN OF A MOBILE E-LEARNING FORECASTING SYSTEM BASED ON A CASE STUDY USING MULTIPLE INTELLIGENCE ANALYSIS
This research aimed to design a mobile e-Learning forecasting system based on case studies using Multiple Intelligence analysis. The framework is divided into six modules. The first module describes a Patterns-based module. The second module describes the student Mapping module. The third module presents the Forecasting module. The fourth module presents the Learning portal module. The fifth module explains the Adaptive module. The last module presents the learning content based on the case study modules. In addition this paper introduces an example of an operational framework. The evaluation is comprised of two sections. The first section evaluates student achievement. The second section evaluates student prediction precision. The results of the first evaluation showed that students who studied via the mobile e-Learning forecasting system are more successful than students who studied via the normal e-Learning system with significance at 0.05 (Group 1, t = 7.577, p < 0.05; Group 2, t = 3.684, p < 0.05; Group 3, t = 15.190, p < 0.05). For the second section, the results considered the prediction precision percentage. We compared three algorithms: 1) J48 algorithm, 2) ID3 algorithm, and the 3) NaA¯ve Bayes algorithm. The J48 algorithm had the highest value at 88.286%. To cite this document: Thongchai Kaewkiriya, "Design of a mobile e-learning forecasting system based on a case study using multiple intelligence analysis", International Journal of Electronic Commerce Studies, Vol.7, No.2, pp.189-200, 2016. Permanent link to this document: http://dx.doi.org/10.7903/ijecs.1413 Â
期刊介绍:
The IJECS is a double-blind referred academic journal for all fields of Electronic Commerce. To serve as an international platform, the IJECS encourages manuscript submissions from authors all around the world. As a multi-discipline journal, The IJECS welcome both technology oriented and business oriented electronic commerce research articles. The purpose of the International Journal of Electronic Commerce Studies is to promote electronic commerce research and provide worldwide scholars a place to publish their innovative work in electronic commerce. To be published in the journal, the manuscript must make strong empirical, theoretical, or practical contributions and highlight the significance of the contributions to the electronic commerce field. Thus, preference is given to submissions that test, extend, or build strong theoretical frameworks for electronic commerce theory, electronic commerce system development, and electronic commerce practice. The journal is not tied to any particular national context; the geographic distribution of authors publishing in the journal came from countries around the world. Articles introducing cases of innovative applications in electronic commerce around the world are also published in the journal. The journal provides scholars opportunities to realize the electronic commerce research and development around the world. Articles in the International Journal of Electronic Commerce Studies will include, but are not limited to the following areas.