文章基本信息

标题：A Domain-Based Learning Object Search Engine to Support Self-Regulated Learning
本地全文：下载
作者：Ali Alharbi ; Frans Henskens ; Michael Hannaford 等
期刊名称：International Journal of Computer and Information Technology
印刷版ISSN：2279-0764
出版年度：2012
卷号：1
期号：1
页码：83
出版社：International Journal of Computer and Information Technology
摘要：The number of learning resources available on the web has increased dramatically. However, it is a difficult task for students to locate the learning materials that are appropriate to their requirements and needs. This study proposes a custom search engine to help students find learning objects related to computer science topics. The custom search engine provides a unified interface to search different learning material repositories and filter the result using criteria such as the type of learning material and the topic under which these learning materials are classified. The custom search engine implements a term suggestion function to make it easy for students to choose relevant keywords for their search. The term suggestion function is based on the IEEE/ACM Computing Curriculum guidelines and the ACM Computing Classification System. An empirical evaluation of the proposed custom search engine with computer science students reveals that the system is highly effective in retrieving learning objects related to topics about programming languages. The students' responses to the evaluation questionnaire indicate that they consider the custom search engine easy to use and useful for finding computer science learning objects.
关键词：Learning objects; computer science education; self- ; regulated learning; microdata; rich snippets; custom search ; engine