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In general, content-based recommender systems use a keyword vector to locate recommendations. However, this method does not consider relations of each keyword and it is also inscrutable to users, who may have a hard time determining which words in their profiles are important and which may be skewing their results to
Lack of overall ecological knowledge structure is a critical reason for learners' failure in keyword-based search. To address this issue, this paper firstly presents the dynamic location-aware and semantic hierarchy (DLASH) designed for the learners to browse images, which aims to identify learners' current
Writing and browsing education blogs has become one of the important methods of e-learning. Learners can search the interesting resources from these education blogs. However, the traditional blog search only provides keyword-based matching, lacking automatic extraction of learner interests and further interest-related
This paper describe the case study of developing and providing the ontology-based educational information search service to overcome the limitations of the existing keyword-based search service. We used the OWL-DL 1) to build the core ontology DB from the existing metadata DB represented by the LOM, 2) to build the
information sorting ability and problem-solving ability. It also implies that students built an information organizing model by using classified Social Bookmarking of network knowledge to assist project-based inquiry learning. The internalized model starts from information needs, clarifying main topic, searching keywords
The Web forum is a key tool in new knowledge building among students in learning management systems. Unfortunately, the huge number of messages makes difficult, for tutors and teachers, to quickly evaluate the progress of their students so, an automated support to the analysis is needed. Our solution relies on simple statistical indices inspired by the work in the text analysis field. The obtained...
This study firstly notices that lack of overall ecologic knowledge structure is one critical reason for learners' failure of keyword search. Therefore in order to identify their current interesting sight, the dynamic location-aware and semantic hierarchy (DLASH) is presented for learners to browse images. This
can avoid using keyword-based search method, thereby greatly decreasing the time spent to look for related articles. The system provides related articles as well as information such as the difficulty of the articles, which would assist English learning, and harbor a more user friendly English learning environment. This
keywords (descriptive terms), then we modify the ontology accordingly by adding the cluster's terms as semantic terms under the “SubSubSubconcept = lecture” to which these documents belong. This research is implemented and evaluated on a real platform HyperManyMedia at Western Kentucky University.
Sharing and collaboration, keywords in current way of using the Web, are strongly present in the e-learning field, but usually not in educational content creation. There is a need that students participate even in lectures design, either by commenting, editing them or sharing notes with each other. This paper
of content parsing. Additionally, through analyzing the logs of learners' learning behaviors, the likely keywords and the association among the learning course contents will be conducted or figured out. And then, we integrate with the metadata of the learning materials distributed in different platforms, and maintain
, which consists of technical phrases and indexes occurring in the title and abstract of the paper. These phrases and indexes are picked up from the ACM computing review classification index, keywords as well as using phrases of Microsoft on-line computer dictionary. The columns of the matrix represent phrases and the rows
with different objectives, contexts, granularities, and educational values. This presents a wide range of options to the user. Due mainly to the amount of options available and the lack of qualification standards, it is difficult use keywords to select learning objects that will maximize the learning process in a
digital content stored in distributed e-learning servers, through the use of ontologies. In order to retrieve useful content to be added to the learning environment while bypassing compatibility problems between different ontological representations of the same domain, we use a mixed approach constituting both keywords and
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