All eyes on the signal? - Mapping cohesive discourse structures with eye-tracking data of explanation videos

Leandra Thiele, Florian Schmidt-Borcherding, John A. Bateman
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Abstract

In this paper, we consider the issue of how the fine-grained multimodal design of educational explanation videos, such as those widely available on YouTube and other platforms, may be made accessible to empirical studies of reception and effectiveness. This is necessary because previous research has often led to conflicting conclusions concerning the roles of particular design elements. We argue that this may largely be due to insufficient characterizations of multimodal design itself. To achieve tighter control of this potential source of variation, we present a multimodal descriptive annotation framework drawing on multimodal (cohesive) film discourse analysis. This framework is seen as a critical first step toward being able to highlight just those differences in design that have functional consequences. For such consequences to accrue, however, viewers need to attend differently to corresponding design differences. The goal of the current paper, therefore, is to use eye-tracking techniques to explore the extent to which discourse structures revealed by our analytic framework relate to recipients' attention allocation. We hypothesize that any potentially emerging anomalies in regards to discourse organization, such as instances of unsuccessful cohesion signaling, may have correlations in the behavioral data. We report our current state of development for performing this kind of multimodal cohesion analysis and some of the unresolved challenges raised when considering how such analyses may be related to performance data.
所有人的目光都集中在信号上?- 利用解说视频的眼动跟踪数据绘制连贯的话语结构图
在本文中,我们将探讨如何使教育解说视频(如 YouTube 和其他平台上广泛提供的视频)的细粒度多模态设计能够为接受度和有效性的实证研究所用。这一点很有必要,因为以往的研究经常会就特定设计元素的作用得出相互矛盾的结论。我们认为,这在很大程度上可能是由于对多模态设计本身的描述不够充分。为了更严格地控制这种潜在的差异来源,我们借鉴多模态(内聚)电影话语分析,提出了一个多模态描述性注释框架。这一框架被视为关键的第一步,以便能够突出那些具有功能性后果的设计差异。然而,要产生这样的后果,观众需要以不同的方式关注相应的设计差异。因此,本文的目标是使用眼动跟踪技术来探索我们的分析框架所揭示的话语结构与接受者的注意力分配之间的关系。我们假设,任何可能出现的与话语组织有关的异常情况,如不成功的内聚信号,都可能与行为数据相关。我们将报告我们在进行这种多模态内聚力分析方面的发展现状,以及在考虑如何将这种分析与表现数据联系起来时所面临的一些尚未解决的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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