short-paper

Comparing MOOC Learners Engagement with Japanese Videos and Text to Speech Generated English Videos

Authors:

Sasipa Boonyubol,

Shahriar Kabir,

Jeffrey Scott CrossAuthors Info & Claims

L@S '22: Proceedings of the Ninth ACM Conference on Learning @ Scale

Pages 317 - 320

https://doi.org/10.1145/3491140.3528302

Published: 01 June 2022 Publication History

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Abstract

In recent years, massive open online courses (MOOCs) have been rapidly growing with learners from both English and non-English speaking countries. Nowadays, many MOOC developers provide transcripts in multiple languages to attract a broader range of learners. However, providing transcripts is ineffective for visually impaired learners and tends to divert learners' attention away from the overall content of the video lecture. Thus, aside from offering English transcripts, Japanese lecture videos in the Tokyo TechIntroduction to Electrical and Electronic Engineering MOOC were automatically dubbed into English with a computer-generated voice. To understand the effect of English-automatically dubbed videos on learners, the learners' interactions, video completion rate, and course completions were compared to the original course in Japanese. The results show that English automatically dubbed videos have an overall high learner engagement when compared to Japanese videos with English transcripts. Survey results on learners' satisfaction towards the English dubbed videos also showed higher positive responses. These results show the effectiveness of dubbing Japanese lectures into English using text-to-speech technology which has signification for MOOCs offered in various languages.

Supplementary Material

MP4 File (L-at-S22-lswp127.mp4)

In this presentation, the learners? engagement in two edX Massive Open Online Courses (MOOCs) entitled, Introduction to Electrical and Electronic Engineering (EE), released by the Tokyo Institute of Technology was examined. The analysis was performed on Japanese lecture videos with English transcripts released in 2017 (EE2017) and the English automatically dubbed videos with a computer-generated voice using text-to-speech technology in 2021 (EE2021). In order to understand the effect of English automatically dubbed videos on learners, the learners' interactions towards videos, video completion, and course completion, were analyzed and compared to the Japanese videos with English transcripts. The results show that English automatically dubbed videos have an overall higher learners' engagement when compared to Japanese videos with English transcripts. Moreover, the survey results on learners? satisfaction towards the English automatically dubbed videos also showed highly positive responses.

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References

[1]

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Cited By

View all

Cai KLiu CChan D(2024)Anim-400K: A Large-Scale Dataset for Automated End to End Dubbing of VideoICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447911(11796-11800)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447911
Doshi VKumar A(2023)Design and Development of Time and Memory-Efficient Video Translation Tool2023 4th International Conference for Emerging Technology (INCET)10.1109/INCET57972.2023.10170354(1-4)Online publication date: 26-May-2023
https://doi.org/10.1109/INCET57972.2023.10170354
Carlon MBoonyubol SKeerativoranan NCross J(2023)Educational Data Science Approach for an End-to-End Quality Assurance Process for Building Creditworthy Online CoursesEducational Data Science: Essentials, Approaches, and Tendencies10.1007/978-981-99-0026-8_4(151-191)Online publication date: 30-Apr-2023
https://doi.org/10.1007/978-981-99-0026-8_4

Index Terms

Comparing MOOC Learners Engagement with Japanese Videos and Text to Speech Generated English Videos
1. Applied computing
  1. Education
    1. E-learning

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Published In

L@S '22: Proceedings of the Ninth ACM Conference on Learning @ Scale

June 2022

491 pages

ISBN:9781450391580

DOI:10.1145/3491140

General Chair:
René F. Kizilcec
Cornell University, USA
,
Program Chairs:
Katie Davis
University of Washington, USA
,
Xavier Ochoa
New York University, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 2022

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L@S '22

L@S '22: Ninth (2022) ACM Conference on Learning @ Scale

June 1 - 3, 2022

NY, New York City, USA

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Cai KLiu CChan D(2024)Anim-400K: A Large-Scale Dataset for Automated End to End Dubbing of VideoICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447911(11796-11800)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447911
Doshi VKumar A(2023)Design and Development of Time and Memory-Efficient Video Translation Tool2023 4th International Conference for Emerging Technology (INCET)10.1109/INCET57972.2023.10170354(1-4)Online publication date: 26-May-2023
https://doi.org/10.1109/INCET57972.2023.10170354
Carlon MBoonyubol SKeerativoranan NCross J(2023)Educational Data Science Approach for an End-to-End Quality Assurance Process for Building Creditworthy Online CoursesEducational Data Science: Essentials, Approaches, and Tendencies10.1007/978-981-99-0026-8_4(151-191)Online publication date: 30-Apr-2023
https://doi.org/10.1007/978-981-99-0026-8_4

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Recommendations

The Effect of Peer Assessment Rubrics on Learners' Satisfaction and Performance Within a Blended MOOC Environment

Perception of MOOC Pedagogical Tools and Learners' Learning Styles in MOOC Blended Teaching: a Case Study

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