Answering academic questions for education by recommending cyberlearning resources
Identifieur interne : 000633 ( Istex/Corpus ); précédent : 000632; suivant : 000634Answering academic questions for education by recommending cyberlearning resources
Auteurs : Xiaozhong Liu ; Han JiaSource :
- Journal of the American Society for Information Science and Technology [ 1532-2882 ] ; 2013-08.
Abstract
In this study, we design an innovative method for answering students' or scholars' academic questions (for a specific scientific publication) by automatically recommending e‐learning resources in a cyber‐infrastructure‐enabled learning environment to enhance the learning experiences of students and scholars. By using information retrieval and metasearch methodologies, different types of referential metadata (related Wikipedia pages, data sets, source code, video lectures, presentation slides, and online tutorials) for an assortment of publications and scientific topics will be automatically retrieved, associated, and ranked (via the language model and the inference network model) to provide easily understandable cyberlearning resources to answer students' questions. We also designed an experimental system to automatically answer students' questions for a specific academic publication and then evaluated the quality of the answers (the recommended resources) using mean reciprocal rank and normalized discounted cumulative gain. After examining preliminary evaluation results and student feedback, we found that cyberlearning resources can provide high‐quality and straightforward answers for students' and scholars' questions concerning the content of academic publications.
Url:
DOI: 10.1002/asi.22851
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xml:id="asi22851-kwd-0003">education</keyword></keywordGroup><abstractGroup> <abstract type="main"><p>In this study, we design an innovative method for answering students' or scholars' academic questions (for a specific scientific publication) by automatically recommending e‐learning resources in a cyber‐infrastructure‐enabled learning environment to enhance the learning experiences of students and scholars. 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By using information retrieval and metasearch methodologies, different types of referential metadata (related Wikipedia pages, data sets, source code, video lectures, presentation slides, and online tutorials) for an assortment of publications and scientific topics will be automatically retrieved, associated, and ranked (via the language model and the inference network model) to provide easily understandable cyberlearning resources to answer students' questions. We also designed an experimental system to automatically answer students' questions for a specific academic publication and then evaluated the quality of the answers (the recommended resources) using mean reciprocal rank and normalized discounted cumulative gain. 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