Improving Student Accessibility, Equity, Course Performance, and Lab Skills: How Introduction of ClassTranscribe is Changing Engineering Education at the University of Illinois

Angrave, Lawrence; Jensen, Karin; Zhang, Zhilin; Mahipal, Chirantan; Mussulman, David; Schmitz, Christopher D.; Baird, Robert; Liu, Hongye; Sui, Ruihua; Wu, Maryalice; Kooper, Rob

doi:10.18260/1-2--34796

Cited by 8 publications

(15 citation statements)

References 12 publications

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“…There exist educational systems, such as ClassTranscribe (Ren et al, 2015) and ICS Videos (Deshpande et al, 2014), that can generate captions for lecture videos in a collaborative fashion with learners. Previous studies (Deshpande et al, 2014;Angrave et al, 2020a;Cross et al, 2014) have shown that such tools are effective and efficient for captioning lecture videos and have considerable value in educational practice (Angrave et al, 2020a(Angrave et al, , 2020bAmos et al, 2021;Zhang etal, 2021;Zhang, 2021). Previous research has also shown that involving learners in fixing captions in foreign language educational videos does not impair learning and also helps reduce errors in the captions (Culbertson et al, 2017).…”

Section: Collaborative Video Captioningmentioning

confidence: 99%

Exploring collaborative caption editing to augment video-based learning

Bhavya

Chen

Zhang

et al. 2022

Education Tech Research Dev

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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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Section: Collaborative Video Captioningmentioning

confidence: 99%

Exploring collaborative caption editing to augment video-based learning

Bhavya

Chen

Zhang

et al. 2022

Education Tech Research Dev

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“…Conversely, the introduction of searchable video lectures in an undergraduate-level system programming course which complemented the equivalent online book content caused an increase in course performance for all students [7]. Similarly in an introduction to electronics class, searchable translated class videos led to improved course scores [8].…”

Section: Universal Design For Learningmentioning

confidence: 99%

“…The components of the MUSIC model have been used for the past decade in multiple contexts to identify factors that motivate students [9,10]. Highlights of peer-reviewed course performance models and student feedback showed that searching as a new modality is correlated with positive perceptions and improved student performance [7,8].…”

Section: Universal Design For Learningmentioning

confidence: 99%

A UDL-Based Large-Scale Study on the Needs of Students with Disabilities in Engineering Courses

Amos

Zhang

Angrave

et al.

2021 ASEE Virtual Annual Conference Content Access Proceedings

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“…ClassTranscribe is a web-based learning system and video lecture platform that has more than 13,000 users. Some of its features have been previously presented at ASEE [4,23,15]. Its adoption has led to measurable and statistically significant improvements in learning outcomes [5] and large scale studies of the importance of UDL for engineering students with and without disabilities, for example, [2,14].…”

Section: Classtranscribementioning

confidence: 99%

The Inclusive Glossary: An Embedded, Interactive Approach to Accessible and Inclusive Learning

Li,

Lualdi,

Lin

et al.

2023 ASEE Annual Conference &Amp; Exposition Proceedings

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To assist students in engineering and related STEM disciplines, we report on the motivation, design, implementation, and evaluation of the Inclusive Glossary, a novel embedded interactive educational tool. The Glossary explains technical terms when the student encounters new terms in video and written content. The Glossary was motivated by two equally-important factors. Firstly, to add American Sign Language (ASL) signing of technical terms as a first-class, inclusive educational outcome, and within the normal learning environment of university students. Secondly, to help mitigate the ongoing readiness-to-learn effects due to the lowered learning outcomes from the 2020-22 COVID-19 pandemic and inequity in students' prior high school education experiences. The Glossary takes a strong inclusive design stance; for all students there are valuable context-specific just-in-time learning opportunities to address "Knowledge-Gaps" that create barriers to learning the current topic of study. It also enables ASL signers to learn the growing and evolving corpus of engineering, physics and computer science signs.The Glossary's design and implementation is introduced from three perspectives: ASL, Universal Design for Learning (UDL), and Active Learning. ASL -a complete natural language with its own unique grammar and terms -is the first and primary language of some students who are Deaf or Hard of Hearing (DHH). The principles of UDL promote a user-configurable design that provides multiple forms of modality, engagement and interactivity. Scholastic research into Active Learning suggests studentinitiated knowledge-seeking actions, when embedded into video-based and text-based learning experiences, improve learning outcomes and reduce the difficulty or perceived difficulty of a course. The Glossary is implemented as a web application that uses an automated workflow to efficiently find, download, and index domain-specific terms, definitions, and explanations in two primary languages, English and ASL, in text and video form. The automated workflow extracts domain terms from both the audio transcription and visual text from video content. Definitions and explanations of the glossary terms in English and ASL are automatically curated from open web-sources with zero or minimal instructor time required. Explanations in different lengths are provided for students with different interest levels, learning needs, and attention spans. ASL video entries are provided in three sign forms; as an isolated sign, a sentence definition, and an example usage. Students can view both English and ASL explanations. By embedding the Glossary into ClassTranscribe, we describe the user interface comprised of i) A glossary appendix inside the course notes, ii) Web page popups in the video player, and iii) An online gallery page to browse, edit, and search for glossary terms of the course. The extraction efficiency, precision and recall of the system were evaluated using a corpus of 300 candidate domain-specific terms automatically extracted from 8 videos....

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Improving Student Accessibility, Equity, Course Performance, and Lab Skills: How Introduction of ClassTranscribe is Changing Engineering Education at the University of Illinois

Cited by 8 publications

References 12 publications

Exploring collaborative caption editing to augment video-based learning

Exploring collaborative caption editing to augment video-based learning

A UDL-Based Large-Scale Study on the Needs of Students with Disabilities in Engineering Courses

The Inclusive Glossary: An Embedded, Interactive Approach to Accessible and Inclusive Learning

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