文章摘要
面向学习情境的个性化学习资源推荐研究
Context-aware Personalized Learning Resource Recommendation
投稿时间:2020-10-12  修订日期:2020-10-12
DOI:
中文关键词: 个性化学习  情境化推荐  协同过滤  用户画像
英文关键词: Personalized Learning  Contextualized Recommendation  Collaborative Filtering  User Portrait
基金项目:1.广东省教育厅高校重点平台与科研项目“基于旅客情绪感知的航班延误治理研究”(编号:2018GWTSCX053),项目主持人:张颖敏;2.广东省高职教育教学改革研究与实践项目“基于CDIO理念的民航电子商务类课程项目化教学改革研究与实践”(编号:GDJG2019306),项目主持人:张颖敏。
作者单位E-mail
张颖敏 广州民航职业技术学院 149090135@qq.com 
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中文摘要:
      摘 要 移动互联网下过载的信息使得学习者很难从海量的碎片化资源中快速找到自己所需。精准的个性化学习资源推荐成为“互联网+教育”亟待解决的热点问题。传统的协同过滤算法在海量情境数据环境下的信息推荐效果并不理想,考虑学习情境对学习者知识体系建构的影响,本文将学习者的学习情境加入到学习资源推荐系统中,构建学习者-学习情境-学习资源的推荐模型,通过学习者个性化学习行为的捕捉,勾勒学习者画像,通过学习情境与学习者行为的相似度计算,找到与学习者学习情境相匹配的学习资源并进行推荐。实验结果表明,基于用户画像的情境化推荐方法比传统的协同过滤方法具有更高的精度,能更好适用于海量学习资源的个性化推荐。
英文摘要:
      Abstract Massive learning resources are readily available under the rapid development of mobile Internet. However, overloaded information makes it difficult for learners to quickly find the learning resources they need from a large number of fragmented resources. Accurate recommendation of learning resources has become a hot issue to be solved urgently for “Internet + education”. The traditional collaborative filtering (CF) recommending algorithm has low accuracy in the massive context data environment. In view of the impact of learning situations, this paper adds learning context factors to learning resources preference modeling, constructs a context-aware learning resources recommendation model. Outlines the learner’s portrait through the capture of their personalized learning behaviors. Calculates the similarity of learning context and behaviors and recommend the matching resources under similar learning situation. The final experiment result shows that the context-aware recommendation method is higher accurate in personalized learning resources recommending.
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