Use of Smart Learning Resource Management Systems for Sustainable Learning

Gan Chanyawudhiwan, Kemmanat Mingsiritham
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引用次数: 1

Abstract

Learning resources are very important to all the stakeholders in the world of education. There is a very large number of learning resources today and they are scattered all over the internet. This study was about the development of a new smart learning resource management system called STOU SmartLearnX, which can locate and pull up digital content from the internet and social media. It has integrated technology to support independent learning via digital media. The system passed quality control evaluation by a panel of 15 experts before being tested on bachelor’s degree students enrolled in an online course emphasizing nanolearning style offered by an open university in Thailand. The 65 sample students testing the system came from Humanities and social science, Science and technology and Health science. They were given a pretest before using STOU SmartLearnX in the course and a posttest after using it, and their mean scores were compared using t-test dependent and one-way ANOVA. Mean posttest scores were higher than pretest scores to a statistically significant degree, but there was no significant difference in posttest scores between the students from the group, indicating that the system can be successfully used for learning in different subject areas. In the future the system may be further developed to make it able to deliver more precise results by utilizing advanced searches and images. Searches should take into account the context and should be responsive.
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使用智能学习资源管理系统促进可持续学习
学习资源对教育界的所有利益相关者都非常重要。今天有大量的学习资源,它们分散在互联网上。这项研究是关于一种名为STOU SmartLearnX的新型智能学习资源管理系统的开发,该系统可以从互联网和社交媒体中定位和提取数字内容。它集成了通过数字媒体支持自主学习的技术。该系统通过了一个由15名专家组成的小组的质量控制评估,然后在参加泰国一所开放大学提供的强调纳米学习风格的在线课程的学士学位学生身上进行了测试。测试该系统的65名样本学生来自人文社会科学、科学技术和健康科学。他们在课程中使用STOU SmartLearnX之前进行了前测,在使用后进行了后测,他们的平均分数使用t检验相关和单向方差分析进行比较。后测平均分高于前测平均分,差异有统计学意义,但组内学生的后测平均分差异无统计学意义,说明该系统可以成功用于不同学科领域的学习。在未来,该系统可能会进一步发展,使其能够通过利用高级搜索和图像提供更精确的结果。搜索应该考虑到上下文,并且应该响应。
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