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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.
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Perspectives on Computational Models of Learning and Forgetting
Technological developments have spawned a range of educational software that strives to enhance learning through personalized adaptation. The success of these systems depends on how accurate the knowledge state of individual learners is modeled over time. Computer scientists have been at the forefront of development for these kinds of distributed learning systems and have primarily relied on data-driven algorithms to trace knowledge acquisition in noisy and complex learning domains. Meanwhile, research psychologists have primarily relied on data collected in controlled laboratory settings to develop and validate theory-driven computational models, but have not devoted much exploration to learning in naturalistic environments. The two fields have largely operated in parallel despite considerable overlap in goals. We argue that mutual benefits would result from identifying and implementing more accurate methods to model the temporal dynamics of learning and forgetting for individual learners. Here we discuss recent efforts in developing adaptive learning technologies to highlight the strengths and weaknesses inherent in the typical approaches of both fields. We argue that a closer collaboration between the educational machine learning/data mining and cognitive psychology communities would be a productive and exciting direction for adaptive learning system application to move in.
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- Award ID(s):
- 1631428
- PAR ID:
- 10113804
- Date Published:
- Journal Name:
- International Conference on Cognitive Modeling
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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