Creating Tools for Learning Statistics: An exploratory project to benefit deaf and hard of hearing students
Statistics is central to research in almost every STEM discipline. Thus, introductory statistics is a gateway course for the development of students into research scientists. Deaf and hard of hearing (DHH) learners often receive less attention from mainstream instructors who believe that, with sign language interpreters and other support services, DHH students have equal access to learning in their classrooms. Yet access often falls short of 'equal' due to variations of instructional skill, interpreter knowledge of the discipline, and the lack of alternative representations of content. DHH learners have historically received less attention from researchers due to a perception that results would benefit only this small group. As a result, improvements in access, success and retention in STEM majors for this population continue to be a concern.
Project Thinking CAP: Communication, Access, & Persistence Among Deaf And Hard Of Hearing Students In Foundational Statistics Courses aims to investigate the potential of Supplemental Online Learning Tools (SOLTs) that integrate visual representations of complex concepts with signed explanations to enhance the academic success of deaf and hard of hearing (DHH) students in foundational statistics courses at Rochester Institute of Technology. Core objectives include 1) develop a pilot collection of SOLTs for learning complex concepts in mainstream postsecondary settings and 2) test the efficacy of these videos in experimental and natural class settings.
The design of each SOLT incorporates a series of micro-videos that breaks a topic into parts, and explains the terms and concepts needed to understand the topic. In the first year of the project, the team identified the first topic, broke it into pieces, developed components, and created pilot versions of first SOLT micro-video. They also crafted a process for choosing a course topic to address that includes operational guidelines, data collection and triangulation of multiple data sources.
Two versions of the first SOLT micro-video were created: One using ﾓdirect instructionﾔ ﾖ a deaf instructor signing for himself, the other using instruction via a sign language interpreter. During the second year, the project team is further investigating best practices for SOLTﾒs, which includes exploring how the SOLTﾒs could be placed into an educational gaming framework. In addition, evaluation of the first micro-video and development of further materials is ongoing.
This project has the potential to increase learning for DHH students in statistics, increase the number of DHH students who continue to pursue statistics or other STEM disciplines, and contribute to diversity within STEM workforce careers. Other learners may also benefit from visual representation of complex concepts. It is estimated that ?65% of the population are visual learners, as are half of all students in special education programs. The potential for the broader application of SOLTs in other STEM subjects for DHH or other students may increase knowledge of how diverse groups of visual learners access complex concepts. Dissemination thus far has included faculty presentations on campus and at a national conference, and a student researcher presentation at a local conference.
Labor policies prevented the RIT staff sign language interpreter specified in the project proposal from working on the project. Finding and hiring a free lance sign language interpreting expert proved to be difficult, due to the needs of the project (mathematics/statistics classroom interpreting experience) and the terms of employment. Ultimately, a recently hired faculty member was added as SP. See is a mathematics instructor at NTID and a former classroom interpreter at RIT, with a B.S. and M.S. in mathematics.