2023 ASEE Annual Conference & Exposition

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.