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Intelligent and adaptive tutoring through a social network for higher education
New Review of Hypermedia and Multimedia ( IF 1.2 ) Pub Date : 2021-04-10 , DOI: 10.1080/13614568.2021.1908436
Christos Troussas 1 , Akrivi Krouska 1 , Efthimios Alepis 1 , Maria Virvou 1
Affiliation  

ABSTRACT

Social Media have become a tool for communication among people, the majority of whom belong to the younger generation. Hence, exploiting them in the educational field can promote communication and collaboration among students and instructors. Moreover, expanding them with more pedagogical tools, such as assessments and adaptivity, can improve further the learning outcomes. In view of the above, this paper presents i-LearnC#, an intelligent tutoring application over Facebook, for learning programming. This Facebook app extends the capabilities of Facebook platform to be used as an integrated tutoring system. In particular, i-LearnC# embodies an intelligent Virtual Coach offering individualised advice, covering students’ knowledge gaps and solving queries on unknown learning concepts. Regarding students’ assessment, i-LearnC# applies the Revised Bloom Taxonomy and delivers personalised assistance to them through the Virtual Coach. Finally, i-LearnC# employs cluster analysis for recommending the optimal groups for collaboration to students. i-LearnC# was evaluated using a well-known framework, the statistical hypothesis test and system's log files. The results are very promising, showing that i-LearnC# has been educationally beneficial to students in higher education promoting effective learning and teaching of computing education and achieving high acceptance rate as an adaptive intelligent social learning environment.



中文翻译:

通过社交网络为高等教育提供智能和适应性辅导

摘要

社交媒体已成为人们之间交流的工具,其中大多数属于年轻一代。因此,在教育领域利用它们可以促进学生和教师之间的交流与合作。此外,使用更多的教学工具(例如评估和适应性)来扩展它们,可以进一步提高学习成果。有鉴于此,本文介绍了i-LearnC#,一个Facebook 上的智能辅导应用程序,用于学习编程。此 Facebook 应用程序扩展了 Facebook 平台的功能,可用作集成辅导系统。特别是,i-LearnC#体现了智能虚拟教练,提供个性化建议,覆盖学生的知识差距并解决对未知学习概念的疑问。关于学生的评价,i-LearnC# 应用修订后的 Bloom 分类法,并通过虚拟教练为他们提供个性化的帮助。最后,i-LearnC# 使用聚类分析向学生推荐最佳协作组。i-LearnC# 使用众所周知的框架、统计假设检验和系统日志文件进行评估。结果非常有希望,表明 i-LearnC# 作为一种自适应智能社交学习环境,在教育上有利于高等教育学生促进计算教育的有效学习和教学,并获得高接受率。i-LearnC# 使用众所周知的框架、统计假设检验和系统日志文件进行评估。结果非常有希望,表明 i-LearnC# 作为一种自适应智能社交学习环境,在教育上有利于高等教育学生促进计算教育的有效学习和教学,并获得高接受率。i-LearnC# 使用众所周知的框架、统计假设检验和系统日志文件进行评估。结果非常有希望,表明 i-LearnC# 作为一种自适应智能社交学习环境,在教育上有利于高等教育学生促进计算教育的有效学习和教学,并获得高接受率。

更新日期:2021-04-10
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