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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques
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Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques

机译：基于协作标记技术增强具有个性化推荐的电子学习系统

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摘要

Personalization of the e-learning systems according to the learner's needs and knowledge level presents the key element in a learning process. E-learning systems with personalized recommendations should adapt the learning experience according to the goals of the individual learner. Aiming to facilitate personalization of a learning content, various kinds of techniques can be applied. Collaborative and social tagging techniques could be useful for enhancing recommendation of learning resources. In this paper, we analyze the suitability of different techniques for applying tag-based recommendations in e-learning environments. The most appropriate model ranking, based on tensor factorization technique, has been modified to gain the most efficient recommendation results. We propose reducing tag space with clustering technique based on learning style model, in order to improve execution time and decrease memory requirements, while preserving the quality of the recommendations. Such reduced model for providing tag-based recommendations has been used and evaluated in a programming tutoring system.
机译：根据学习者的需求和知识水平的个性化e-learnal systems在学习过程中呈现关键元素。具有个性化建议的电子学习系统应根据个人学习者的目标调整学习体验。旨在促进学习内容的个性化，可以应用各种技术。协作和社交标记技术对于加强学习资源的建议有用。在本文中，我们分析了在电子学习环境中应用基于标签的建议的不同技术的适用性。基于张量分解技术，最合适的模型排名已被修改以获得最有效的推荐结果。我们提出了基于学习风格模型的聚类技术还原标签空间，以提高执行时间并降低内存要求，同时保留建议的质量。用于提供基于标签的建议的这种降低的模型已经在编程辅导系统中进行了评估。

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