高等教育中参与教学视频的特点:教师在视频会议中的行为和动作的系统文献综述

IF 3.1 Q1 EDUCATION & EDUCATIONAL RESEARCH
Navdeep Verma, S. Getenet, Christopher Dann, T. Shaik
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引用次数: 0

摘要

在线学习因其便利性、灵活性、成本效率和可访问性等优点而需求量很大。在在线学习中,视频会议是一种有效的协作和提高在线学生参与度的技术。该研究是利用机器学习和深度学习等人工智能(AI)方法开发视频注释工具的大型研究的一部分,该研究采用基于设计的研究(DBR)。系统的文献综述是确定参与式教学视频的特征和指标的基础。本系统文献综述中纳入的研究从7个数据库中收集,并根据系统评价的首选报告项目应用纳入/排除标准进行选择。从选定的研究中,我们根据教师的行为和动作识别、分类并解释了引人入胜的教学视频的特征和指标。在本研究中,我们确定了对提高学生参与度至关重要的11个特征和47个相关指标。教师和高等教育机构可以利用这些特征和指标作为基准,提高教学视频的参与性质量,进而改善教与学。在DBR的最后阶段,确定的指标可以用来训练机器学习工具,这是一种人工智能。该工具可以通过突出教师的行为和动作来提供引人入胜的教学视频报告。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Characteristics of engaging teaching videos in higher education: a systematic literature review of teachers' behaviours and movements in video conferencing
Online learning is in high demand due to benefits such as convenience, flexibility, cost efficiency, and improved accessibility. In online learning, video conferencing is an effective technology for collaboration and increasing online student engagement. This study is part of a larger study conducted using design-based research (DBR) to develop a video annotation tool using artificial intelligence (AI) methodologies such as machine learning and deep learning. This systematic literature review is the foundation of the process which identifies the characteristics and indicators of engaging teaching videos. The studies included in this systematic literature review have been gathered from seven databases and selected by applying inclusion/exclusion criteria in accordance with the Preferred Reporting Items for Systematic Reviews. From the selected studies, we identified, categorised, and explained the characteristics and indicators of engaging teaching videos based on teachers’ behaviours and movements. In this study, we identified 11 characteristics and 47 associated indicators of the characteristics critical in enhancing student engagement. Teachers and higher education institutions can use these characteristics and indicators as a benchmark to improve the quality of engaging teaching videos and later improve teaching and learning. In the final stage of DBR, the identified indicators can be used to train a machine learning tool, a form of AI. This tool can provide a report on engaging teaching videos by highlighting the teachers’ behaviours and movements.
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来源期刊
CiteScore
7.10
自引率
3.10%
发文量
28
审稿时长
13 weeks
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