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

Bibliographic Data

ID21360171
AuthorsEmad A Alghamdi (0000-0002-2316-3457, King Abdulaziz University English Language Institute Jeddah 21589 Saudi Arabia, corresponding author), Paul Gruba (0000-0002-6616-9568, The University of Melbourne Department of Linguistics and Applied Linguistics Grattan Street, Parkville Melbourne Victoria 3010 Australia), Eduardo Velloso (0000-0003-4414-2249, The University of Melbourne School of Computing and Information Systems Grattan Street, Parkville Melbourne Victoria 3010 Australia)
Year2022
Volume106
Issue2
Pages393-410
Publication date2022-06-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueModern Language Journal (JOURNAL)
Journal identifiersISSN: 0026-7902 • E-ISSN: 1540-4781
PublisherWiley (PUBLISHER • GB)
DOI10.1111/modl.12773
OpenAlexW4281395017
LanguageEN
Citations received6
References cited94

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

Component (thermodynamics) · Linguistic sequence complexity · Linguistics · Machine learning · Natural language processing · Point (geometry) · Readability · Regression analysis · Set (abstract data type) · Variance (accounting) · Artificial Intelligence · Computer Science · Mathematics · Second Language Acquisition and Learning · Subtitles and Audiovisual Media · Text Readability and Simplification

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