The effects of video-annotated learning and reviewing system with vocabulary learning mechanism on English listening comprehension and technology acceptance

Chen, Chih‐Ming; Li, Ming-Chaun; Lin, Mei-Feng

doi:10.1080/09588221.2020.1825093

Cited by 21 publications

(12 citation statements)

References 59 publications

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“…In addition, the findings of this study also support the notion that students who learn a language by repeatedly (but not excessively) watching the same film with subtitles can boost their ability to understand new vocabulary and further recognize and learn new expressions in the language being learned (Chen et al, 2022;Koolstra & Beentjes, 1999;Tsai, 2022;Etemadi, 2012;). Films also support language learning by presenting conversations that provide contextual vocabulary (Lin & Chen, 2007).…”

Section: Discussionsupporting

confidence: 75%

Language Education: Conceptual and Practical Explorations (LECPE 2022)

2022

JSSH

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Implementing the online learning process requires qualified EFL teachers as agents of change to get hold of effective learning resources to help students achieve learning goals.This pilot study explores EFL students' perceptions from two private Islamic senior high schools in urban areas in Aceh Province, Indonesia, towards using video clips with subtitles (bimodal of English-Indonesian) while learning English in the classroom. The study applied the mixed-methods design by disseminating a questionnaire to 78 participants and conducting a semi-structured interview with ten selected participants. Findings revealed that most students positively perceived using subtitled video clips in learning English vocabulary. However, some encountered setbacks in learning English words due to the poor use of the elements in the video clip subtitles. Therefore, the subtitling procedures still need revisiting to assist students better in digesting subtitled movies in a more wellorganized manner.

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Section: Discussionsupporting

confidence: 75%

Language Education: Conceptual and Practical Explorations (LECPE 2022)

2022

JSSH

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“…The structure of the encoder includes the number of hidden layers, the number of nodes contained in each hidden layer, and the node type of each hidden layer [18]. After many experiments, the deep autoencoder used in the study consists of seven layers, including an input layer, an output layer, and five hidden layers, and the number of nodes in each layer is 490 × 720 × 720 × 50 × 720 × 720 × 490 [19].…”

Section: Encoder Categorymentioning

confidence: 99%

Research on English Vocabulary and Speech Corpus Recognition Based on Deep Learning

Zhen¹

2022

Wireless Communications and Mobile Computing

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In order to investigate how to recognize English words and speech corpus, an English vocabulary and English speech recognition model based on deep learning algorithm was proposed. Through recommending key technical problems and solutions based on deep learning algorithm, how to realize the recognition of English vocabulary and speech corpus was investigated. In the research, the accuracy of the method on the English vocabulary and speech corpus recognition based on the deep learning algorithm increased 79% over the previous methods. Combined with the principle of the deep automatic encoder and deep learning algorithm, the research emphasis was on the effects of speech recognition framework for speech corpus. The speech recognition research based on the theory of deep learning not only had a theoretical guidance meaning but also had the use value in the practical application.

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“…With VALRM, users can review marked sections with bilingual captions immediately or at a later stage to aid them in listening comprehension (see Chen & Chen, 2018). Meanwhile, VALRS-VLM allows users to highlight unfamiliar words in subtitles and learn these words with a built-in online dictionary (see Chen et al, 2020). With password-protected video server, only the video owners and invited individuals can get access to the videos in VFR (see Hulsman & Vloodt, 2015).…”

Section: Video Annotation As a Teaching And Learning Toolmentioning

confidence: 99%

Enriching Student Learning Through Video-annotated Peer Feedback Activity: A Guide

Lam¹,

Habil²

2021

IJARPED

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Despite the growing research base on the use of video annotation in education, little is known on how to coherently structure a video-annotated peer feedback activity to engage students. Eventhough some studies have outlined the guidelines for conducting video-based annotation practice, little has been published with the focus on video-annotated peer feedback. To explore this under-investigated realm, the current review was conducted. In order to identify studies to be included in this review, a strategy for literature search was initially developed. This was followed by the selection and categorization of relevant studies. This paper thus provides a succinct overview of the essential steps involved in conducting a video-annotated peer feedback learning activity. Most importantly, it has resulted in a guide that outlines those essential stages and steps, thus providing insights into adaptation for use in education.

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The effects of video-annotated learning and reviewing system with vocabulary learning mechanism on English listening comprehension and technology acceptance

Cited by 21 publications

References 59 publications

Language Education: Conceptual and Practical Explorations (LECPE 2022)

Language Education: Conceptual and Practical Explorations (LECPE 2022)

Research on English Vocabulary and Speech Corpus Recognition Based on Deep Learning

Enriching Student Learning Through Video-annotated Peer Feedback Activity: A Guide

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