Negi Shivsevak, Mitra Ritayan
Indian Institute of Technology Bombay, Mumbai, India.
J Eye Mov Res. 2020 Aug 16;13(6). doi: 10.16910/jemr.13.6.1.
Learning is a complex phenomenon and education researchers are increasingly focussing on processes that go into it. Eye tracking has become an important tool in such research. In this paper, we focus on one of the most commonly used metrics in eye tracking, namely, fixation duration. Fixation duration has been used to study cognition and attention. However, fixation duration distributions are characteristically non-normal and heavily skewed to the right. Therefore, the use of a single average value, such as the mean fixation duration, to predict cognition and/or attention could be problematic. This is especially true in studies of complex constructs, such as learning, which are governed by both cognitive and affective processes. We collected eye tracking data from 51 students watching a 12 min long educational video with and without subtitles. The learning gain after watching the video was calculated with pre- and post-test scores. Several multiple linear regression models revealed a) fixation duration can explain a substantial fraction of variation in the pre-post data, which indicates its usefulness in the study of learning processes; b) the arithmetic mean of fixation durations, which is the most commonly reported eye tracking metric, may not be the optimal choice; and c) a phenomenological model of fixation durations where the number of fixations over different temporal ranges are used as inputs seemed to perform the best. The results and their implications for learning process research are discussed.
学习是一种复杂的现象,教育研究人员越来越关注学习过程。眼动追踪已成为此类研究中的一项重要工具。在本文中,我们聚焦于眼动追踪中最常用的指标之一,即注视持续时间。注视持续时间已被用于研究认知和注意力。然而,注视持续时间的分布通常呈非正态且严重右偏。因此,使用单个平均值,如平均注视持续时间,来预测认知和/或注意力可能会有问题。在诸如学习等由认知和情感过程共同支配的复杂结构的研究中尤其如此。我们收集了51名学生在观看有字幕和无字幕的12分钟教育视频时的眼动追踪数据。通过前后测分数计算观看视频后的学习收获。几个多元线性回归模型表明:a) 注视持续时间可以解释前后数据中相当一部分的变化,这表明其在学习过程研究中的有用性;b) 作为最常报告的眼动追踪指标的注视持续时间的算术平均值可能不是最佳选择;c) 一种将不同时间范围内的注视次数用作输入的注视持续时间现象学模型似乎表现最佳。本文讨论了这些结果及其对学习过程研究的启示。