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Exploring collaborative caption editing to augment video-based learning.

Bhavya Bhavya1, Si Chen2, Zhilin Zhang1

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Summary
This summary is machine-generated.

This study explored how learners edit captions for educational videos, revealing common errors and strategies. Findings support using machine learning to improve caption accuracy and enhance video-based learning accessibility.

Keywords:
Caption transcriptionCollaborative editingLecture video caption editingTechnology-assisted editing

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Area of Science:

  • Educational Technology
  • Human-Computer Interaction
  • Accessibility Studies

Background:

  • Accurate captions are crucial for educational video accessibility and benefit diverse learners.
  • Many educational videos lack captions or contain errors, hindering effective learning.
  • Crowdsourcing offers a cost-effective method for caption generation, but learner editing behaviors are understudied.

Purpose of the Study:

  • To investigate how learners individually and collaboratively edit captions for educational videos.
  • To identify and categorize common errors in automatically generated captions.
  • To explore the potential of machine learning in assisting learners with caption editing.

Main Methods:

  • A user study involving 58 learners editing captions for 89 lecture videos generated by Automatic Speech Recognition (ASR).
  • Two rounds of editing per video, with data collected through editing logs and interviews.
  • Development of a taxonomy of caption errors and analysis of editing strategies.

Main Results:

  • A novel taxonomy of educational video caption errors was established, including discipline-specific and general mistakes.
  • Individual and collaborative editing strategies employed by learners were identified.
  • Machine learning models showed feasibility for assisting learners in the caption editing process.

Conclusions:

  • Understanding learner caption editing is vital for improving educational video accessibility.
  • The identified error types and editing strategies offer practical insights for educational platforms.
  • Machine learning holds promise for enhancing the efficiency and accuracy of educational video captioning.