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Video-based learners' observed attention estimates for lecture learning gain evaluation

机译：基于视频的学习者的观察注意力估计，用于评估课堂学习效果

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摘要

A significant problem in the field of higher education is maintaining learners' attention during lectures, which is known to significantly affect their learning outcomes. Attention management is commonly associated with the individual ability of a lecturer to track and respond to the common behaviour of an auditorium; it lacks a detailed estimation of the intra-variability of individual learners' attention during the course of the lecture. This paper suggests an objective and non-intrusive evaluation of learners' attention against learning outcomes by introducing an observed attention estimate (OAE). The procedure uses human annotations based on visual cues with a supporting video recording/playback system and a web-based annotation system. This proposed procedure enables us to estimate the attention level of individual learners as observed by human annotators for given time intervals associated with specific concepts covered by the lecture. As part of the procedure, we use an inventory-based lecture gain evaluation and representation based on a novel learning gain matrix. This procedure allows for a detailed analysis of the lecture time flow with regard to learners' attention. We have verified the applicability of the procedure on a small-scale case study.
机译：高等教育领域的一个重要问题是在演讲过程中保持学习者的注意力，众所周知这会极大地影响他们的学习成果。注意管理通常与讲师跟踪和响应礼堂的普通行为的个人能力相关。它缺少对讲课过程中单个学习者注意的内在变异性的详细估计。本文通过引入观察注意力估计（OAE），提出了针对学习成果的学习者注意力的客观且非侵入性的评估方法。该过程使用基于视觉提示的人工注释以及支持的视频记录/播放系统和基于Web的注释系统。这个提议的程序使我们能够估计人类注释者在与讲义所涵盖的特定概念相关的给定时间间隔内，由人类注释者观察到的注意力水平。作为该过程的一部分，我们使用基于清单的演讲增益评估和基于新颖学习增益矩阵的表示。此过程可以针对学生的注意力对讲课时间流进行详细分析。我们已经在小规模案例研究中验证了该程序的适用性。

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