人工智能在远程教育中的最新应用:一个系统的地图研究

Gulnora Jamalova, Farida Aymatova, Sayidolim Ikromov
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引用次数: 0

摘要

在这个以知识为基础的社会中,不断增长的获取知识的需求迫使大学在其教育计划中有效地适应远程学习技术,以满足大量喜欢远程学习模式的学生。人工智能技术的进步也促进了远程学习项目的发展,作为教学和学习过程中的支持工具,缩小了学生和教师之间的教育和组织差距,例如执行时间密集型任务,提供持续的反馈,减少远程学习课程的不合格和退学。在这篇文章中,本文通过系统的映射方法介绍了人工智能在远程教育中的最新应用,旨在识别、分析和分类关于不同人工智能技术在远程教育环境中的应用的相关文献。基于研究问题,搜索和选择策略,从四个学术知识库中确定并检索了60个候选研究。之后,通过筛选标准进一步筛选,讨论达成共识,最终排除23份文件。由此产生的37项研究构成了最终的关键出版物库,这些出版物将被分类和分析,以建立内容分类法,并提供人工智能支持的远程教育的最新技术。本文还确定了研究兴趣集群,重点是学生文本和行为模式的术语分析,面部识别和情感识别。本研究总结了本研究的启示、研究的局限性和未来的研究议程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The state-of-the-art applications of artificial intelligence in distance education: a systematic mapping study
In this knowledge-based society, the increasing need of gaining knowledge has forced universities into the effective adaptation of distance learning technologies in their educational program so that they can satisfy a huge number of students who prefer to attend in distance learning mode. Advances in artificial intelligence technologies have also contributed to the development of distance learning programs as support tools in teaching and learning processes, closing the educational and organizational gap between students and teachers such as performing time-intensive tasks, providing constant feedback, and reducing disqualifications and dropouts in distance learning courses. In this contribution, this paper presents the state-of-the-art applications of artificial intelligence in distance education by using a systematic mapping approach that aims to identify, analyze, and classify the relevant literature about different artificial intelligence technologies applied in distance education environments. Based on research questions, and search and selection strategies, 60 candidate studies are identified and retrieved from four academic repositories. Afterward, they are further filtered through selection criteria and discussed to reach a consensus, leading to exclusion of 23 documents. The resulting 37 studies constitute the final pool of key publications that are classified and analyzed to build content taxonomy and provide the state of the art in artificial intelligence-supported remote education. This paper also identified research interest clusters with highlights for the term analysis of the textual & behavioral patterns of students, face recognition, and emotion recognition. The study concludes the paper with implications, limitations of the study, and future research agenda.
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