The Relative Contribution of Language Complexity to Second Language Video Lectures Difficulty Assessment,The Modern Language Journal

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The Relative Contribution of Language Complexity to Second Language Video Lectures Difficulty Assessment
The Modern Language Journal ( IF 4.7 ) Pub Date : 2022-05-23 , DOI: 10.1111/modl.12773
EMAD A. ALGHAMDI ₁ , PAUL GRUBA ₂ , EDUARDO VELLOSO ₃

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Although core in the teaching of academic language skills, little research to date has investigated what makes video-recorded lectures difficult for language learners. As part of a larger program to develop automated videotext complexity measures, this study reports on selected dimensions of linguistic complexity to understand how they contribute to overall videotext difficulty. Based on the ratings of English language learners of 320 video lectures, we built regression models to predict subjective estimates of video lecture difficulty. The results of our analysis demonstrate that a 4-component partial least square regression model explains 52% of the variance in video difficulty and significantly outperformed a baseline model in predicting the difficulty of videos in an out-of-sample testing set. The results of our study point to the use of linguistic complexity features for predicting overall videotext difficulty and raise the possibility of developing automated systems for measuring video difficulty, akin to those already available for estimating the readability of written materials.

中文翻译：

语言复杂度对第二语言视频讲座难度评估的相对贡献

尽管是学术语言技能教学的核心，但迄今为止很少有研究调查是什么让语言学习者难以接受视频录制的讲座。作为开发自动化视频文本复杂性测量的更大计划的一部分，本研究报告了语言复杂性的选定维度，以了解它们如何影响整体视频文本难度。基于对 320 个视频讲座的英语学习者的评分，我们建立了回归模型来预测视频讲座难度的主观估计。我们的分析结果表明，4 分量偏最小二乘回归模型解释了 52% 的视频难度方差，并且在预测样本外测试集中视频的难度方面明显优于基线模型。

更新日期：2022-05-23

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