基于网络学习的资源不足学习者推荐系统

Sangam Kumar Chaturvedi, Aparupa Dasgupta, Barnali Pal, Nabarun Bhattacharyya
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摘要

该研究文章提出了一个基于(DEW)云计算的电子学习框架的学习者友好推荐系统,并以有效和经济的方式为印度东北地区的偏远地区提供教育/学术课程。一般来说,在学习者没有太多机会使用新技术进行有效和高质量学习的地区,这个系统是一个非常有用和方便的工具。本文讨论了基于E-Learning(而不是U-Learning)的推荐系统,用于资源较少的社区,即东北语言社区(例如,Khasi或/和Kokborok)学习英语,其中教学媒介是当地/部落语言或母语,由模块和讲师支持(混合学习方法)。根据学习者的英语能力/水平,向学习者推荐半监督式的英语语言学习课程。推荐系统是基于Django框架,用python编程语言编码的。ELL(英语语言学习)课程是为来自印度东北部各邦的中学学习者设计的,具有数据分析(推荐系统)和描述性语言教学方法等特点。
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E-Learning based Recommendation System for Less Resourced Learners
The research article presents a learner friendly Recommendation System of an E-Learning framework which is based on (DEW) Cloud Computing and providing service in an effective and economic way for delivering educational/academic course to remote places of North-Eastern region of India. Generally, in regions where learners do not get much opportunity to use new technologies for effective and quality learning this system is a very useful and convenient tool. E-Learning (rather U-Learning) based recommendation system is discussed for less resourced community, i.e. North Eastern language community (e.g., Khasi or/and Kokborok) for learning English where the medium of instruction is in local/tribal languages or mother tongue supported by modules and instructor (a blended learning approach). A semi-supervised ELL (i.e., English language learning) course is recommended to the learner on the basis of learners” aptitude/caliber on English language. Recommendation system is based on Django framework encoded in python programming language. The ELL (English Language Learning) courses are designed for secondary section learners from North-Eastern states of India with features like, data analytics (for recommendation system), and descriptive language teaching methodology.
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