Baltimore , Maryland
June 25, 2023
June 25, 2023
June 28, 2023
New Engineering Educators Division (NEE)
Diversity
24
10.18260/1-2--44464
https://peer.asee.org/44464
474
Jiaxi Li is a 5-year BS-MS in Computer Science student at University of Illinois at Urbana Champaign, advised by Professor Lawrence Angrave and Professor Klara Nahrstedt. He has research interests in the intersection of Machine Learning and Systems. He has previous experience in video analytics and text mining.
Yijun Lin is a Master in Computer Science student at University of Illinois who is interested in Software Engineering and Machine Learning.
Sujit Varadhan is a Junior at the University of Illinois at Urbana-Champaign majoring in Computer Science. He is an undergraduate research assistant as well as a frontend developer on ClassTranscribe.
Rob Kooper is a lead research programmer at the software directorate at the National Center for Super computing Applications. He is interested in enabling scientists to do research work using software developed with the help of NCSA as well as teaching go
Dr. Lawrence Angrave is an award-winning computer science Teaching Professor at the University of Illinois Urbana-Champaign. He creates and researches new opportunities for accessible and inclusive equitable education.
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 moti- vated 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 on- going 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, en- gagement and interactivity. Scholastic research into Active Learning suggests student- initiated 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 defini- tion, and an example usage. Students can view both English and ASL explanations. By embedding the Glossary into ANONYMIZED, 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. For English entries, 241 (80.3%) glossary items had a corresponding English explanation available. For ASL entries, 31 (10.3%) glossary items had a corresponding ASL definition available, and 17 (5.7%) items had ASL sign, example and definition available. Preliminary results suggest this is a promising educational technology that has the potential to help all students thrive in their engineering disciplines.
Li, J., & Lualdi, C. P., & Lin, Y., & Bhatia, A., & Cai, J., & Varadhan, S., & Kooper, R., & Angrave, L. (2023, June), The Inclusive Glossary: An Embedded, Interactive Approach to Accessible and Inclusive Learning Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--44464
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