Exploring collaborative caption editing to augment video-based learning

Abstract

Captions play a major role in making educational videos accessible to all and are known to benefit a wide range of learners. However, many educational videos either do not have captions or have inaccurate captions. Prior work has shown the benefits of using crowdsourcing to obtain accurate captions in a cost-efficient way, though there is a lack of understanding of how learners edit captions of educational videos either individually or collaboratively. In this work, we conducted a user study where 58 learners (in a course of 387 learners) participated in the editing of captions in 89 lecture videos that were generated by Automatic Speech Recognition (ASR) technologies. For each video, different learners conducted two rounds of editing. Based on editing logs, we created a taxonomy of errors in educational video captions (e.g., Discipline-Specific, General, Equations). From the interviews, we identified individual and collaborative error editing strategies. We then further demonstrated the feasibility of applying machine learning models to assist learners in editing. Our work provides practical implications for advancing video-based learning and for educational video caption editing.

Keywords: Caption transcription; Collaborative editing; Lecture video caption editing; Technology-assisted editing.

Fig. 1

Interface for editing captions

Fig. 2

Sample captions before and after an edit

Fig. 3

Numbers of different types of edits made by 58 learners. Left: Edits: Discipline-Specific Edits, General Edits, Typo Edits. Right: Discipline-Specific Edits: Symbol Edits ( Abbreviation, Equations, Single Symbol), Non-Symbol Edits. Discipline-Specific Edits on the left side and on the right side is of same value

Fig. 4

Numbers of different types of edits made from 89 Videos

Fig. 5

Variance of a Precision, b Recall and (c) F1 scores with training set size for the Discipline-Specific class on the test set using CI