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

Amos, Jennifer R.; Zhang, Zhilin; Angrave, Lawrence; Liu, Hongye; Shen, Yiyin

doi:10.18260/1-2--36627

Cited by 3 publications

(6 citation statements)

References 2 publications

(3 reference statements)

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“…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]. ClassTranscribe was created at the University of Illinois Urbana-Champaign and is used as one of the instruction tools in engineering courses across multiple disciplines.…”

Section: Classtranscribementioning

confidence: 99%

“…Specifically, area (1) presents a filter for students to locate a specific course from which they want to access the dictionary. Other than viewing all the glossary terms, students can directly search for specific terms through the search bar provided in area (2). In area (2), students can also view or change their current page.…”

Section: Online Gallery Pagementioning

confidence: 99%

“…Other than viewing all the glossary terms, students can directly search for specific terms through the search bar provided in area (2). In area (2), students can also view or change their current page. Area (3) displays all the glossary terms with explanations, external links, and original sources, which supports sorting in alphabetical order.…”

Section: Online Gallery Pagementioning

confidence: 99%

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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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Section: Classtranscribementioning

confidence: 99%

Section: Online Gallery Pagementioning

confidence: 99%

Section: Online Gallery Pagementioning

confidence: 99%

See 1 more Smart Citation

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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“…Digital note-taking has been increasingly implemented and studied in higher education for its potential to further develop universal design for learning (UDL) techniques to benefit all students [3]. Accessibility for SWD with such technology is recognized as a best practice [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Engineering education researchers have previously reported that SWD preferred searchable lecture video with transcriptions for the content delivery compared with students without disabilities (SWOD) [4]. Furthermore, reported student interviews in the past showed all students in a course benefited from multiple modalities of content delivery in online learning [3][4][5]. However, lecture videos with transcripts and/or a course textbook are not always available, even when students need this content [4].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Low-Stakes Assessment Performance: Student-Perceived Accessibility, Belongingness, and Self-Efficacy in Connection to the Use of Digital Notes in Engineering and Computing Courses

Ding,

Sun,

Xiao

et al.

2023 ASEE Annual Conference &Amp; Exposition Proceedings

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Course content plays a critical role in student success. Among all college students, students with disabilities (SWD) face numerous additional challenges when digital content is inaccessible or difficult to use. Digital note-taking has been increasingly implemented and studied in higher education for its potential to further develop universal design for learning (UDL) techniques to benefit all students, especially SWD. This study combines low-stakes assessment results with surveys about accessibility, belongingness, self-efficacy and perceived learning in connection to the use of digital notes. The multi-discipline, multi-site, and multi-timepoint research design includes two junior-level undergraduate probability and statistics courses in the disciplines of Computer Science and Industrial Engineering at the University of Illinois Urbana Champaign (UIUC). The study investigated the effect of digital notes introduced at week 10, produced using ClassTranscribe developed at UIUC. The digital notes allow for transcripts, screenshots, and mathematical equations taken directly from video recordings. The digital notes were provided to the students of the two classes in both .pdf and .epub formats with included links to the video. Additionally, we considered Students With Accessibility Needs (SWAN) if they reported conditions that prevented them from attending class at some point while not having an officially recognized disability by the university.The students in the classes were surveyed three times: a baseline survey followed by two posttest surveys. The baseline survey was conducted early in the Fall 2022 semester by measuring the students' perceived accessibility of the course using the Web Content Accessibility Guidelines (WCAG)-based POUR model in the context of mathematically-rich engineering courses. The POUR model (perceivable, operable, understandable, and robust subscales) was found previously to be a significant predictor of perceived learning, while disability type was not a significant predictor of perceived learning. After the digital notes were introduced, two posttest surveys were conducted in the 12 th and 16 th week, respectively, to record changes in students' responses for the POUR scales, perceived learning, belongingness, self-efficacy, and their association with low-stakes assessment including homework assignments and quizzes.The results from the responses of 285 students (including 22 SWD) for the first survey found that students generally reported positive scores for the courses' accessibility in all four POUR subscales (scores average > 4.40 out of 5). SWAN and students without disabilities (SWOD) differed in perceivable subscales (p < 0.006), with SWAN finding course material less perceivable. As the course content's difficulty level increased, SWD differed from SWOD significantly regarding perceivable, understandable and robustness subscales at the second survey with lower scores for SWD (FDR p < 0.02). Even though the perceived accessibility scales for the classes decreased in the second s...

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Exploring collaborative caption editing to augment video-based learning

Bhavya

Chen

Zhang

et al. 2022

Education Tech Research Dev

Self Cite

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

Cited by 3 publications

References 2 publications

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

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

Evaluating the Low-Stakes Assessment Performance: Student-Perceived Accessibility, Belongingness, and Self-Efficacy in Connection to the Use of Digital Notes in Engineering and Computing Courses

Exploring collaborative caption editing to augment video-based learning

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