This study will investigate whether pair programming, a collaborative form of programming in which two students work side-by-side on one computer, impacts the performance and retention of students with learning disabilities (LD) in computing courses. Growing post-secondary enrollment of students with LD has created a commensurate need for research on accessibility and success for students with LD in higher education. Students with LD who set out to pursue careers in computing often struggle with computing courses and abandon career pathways that would provide rewarding job opportunities. Pair programming has been used successfully with students without disabilities in computing courses. It has increased learning, decreased frustration, and reduced the confidence gap between male and female students while improving the confidence of all students. However, little research has examined the pair interaction involving students with LD.
This project serves to develop and diversify the nation's computing workforce. To study pair interaction, participants will be engaged in collaborative problem solving. Students enrolled in introductory information technology and programming classes will be randomly assigned to course sections that serve as control or experimental groups. Students in the experimental group will be trained in effective pair programming as well as how to fill the role of driver, the one who writes or types in the code, and navigator or observer, the one who reviews each line of code as it is entered and considers the strategic direction of the work. Partners will complete ten programming activities with two different partners, switching their roles (driver/navigator) every ten minutes. Researchers will observe and record participants' interactions. Data collection will include pre- mid- and post-test questionnaires to measure participants' programming experience, programming self-efficacy, attitude and motivation toward programming. Student learning outcomes will be measured through programming exercises, assignments, and midterm/final exam grades. A focus group will be held during the last class meeting in which participants will be encouraged to share their experiences. Open-ended answers to survey questions, focus group interview responses, and observation notes will be analyzed using qualitative analysis methods. Quantitative data will be analyzed using multivariate general linear models to compare groups. Research results have the potential to be applied in teaching high-school and middle-school students and used in K-12 settings to support students with LD or those who approach learning differently, increasing the likelihood that they will follow the path to computing careers.
