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.

"Advance in Online Education Recommender Systems During and After Covid-19 a Survey"

“…Indeed, the study highlights the potential of chatbots as a valuable tool in online education, offering opportunities to enhance student engagement, foster goal-oriented learning, and cultivate a sense of social presence among learners. Article [3] investigates the use of collaborative caption editing to enhance video-based learning experiences. The study aims to explore the potential benefits and challenges of incorporating collaborative caption editing tools in educational settings.…”

“…It explores topics such as data privacy and security, ethical considerations, the impact on teacher-student relationships, potential biases in AI algorithms, and the necessity of teacher training and professional development to effectively integrate AI tools into pedagogical practices. Additionally, the article [3] presents key findings and trends from reviewed research studies. It discusses various AI applications, such as intelligent tutoring systems, automated grading, adaptive learning platforms, and educational chatbots, and evaluates their effectiveness.…”

“…Additionally, evaluation and feedback mechanisms are incorporated to assess the effectiveness of recommendations, allowing for continuous improvement. Furthermore, in the article [3] the focus is on designing and implementing online education recommender systems that utilize collaborative caption editing to enhance video-based learning. The recommender system considers various factors to optimize the learning experience.…”

Untitled

“…Indeed, the study highlights the potential of chatbots as a valuable tool in online education, offering opportunities to enhance student engagement, foster goal-oriented learning, and cultivate a sense of social presence among learners. Article investigates the use of collaborative caption editing as a means to enhance video-based learning experiences [3].…”

“…It explores topics such as data privacy and security, ethical considerations, the impact on teacher-student relationships, potential biases in AI algorithms, and the necessity of teacher training and professional development to effectively integrate AI tools into pedagogical practices. Additionally, the article presents key findings and trends from reviewed research studies [3]. It discusses various AI applications, such as intelligent tutoring systems, automated grading, adaptive learning platforms, and educational chatbots, and evaluates their effectiveness.…”