Despite increasing work investigating the accessibility of research tools, most accessibility research has traditionally focused on popular, mainstream, or web technologies. We investigated barriers and workarounds blind and low vision doctoral students in computing-intensive disciplines experienced and engaged, respectively, when using advanced technical tools for research tasks. We conducted an observation and interview study with eight current and former Ph.D. students, closely analyzing the accessibility of specific tasks. Our findings contextualize how inaccessible tools complicate research tasks, adding time and effort, and exacerbating social entanglements in collaborative relationships. This work contributes empirical data that extricates how in/accessibility of advanced technical tools used in research influences productivity and collegial efforts.

Few students with disabilities transition from undergraduate to graduate programs. Graduate students often receive ineffective and insufficient accommodations, including lack of support specific to graduate students, because disability services policies are shaped by undergraduate experiences. To understand how disability services offices accommodate graduate students: we (1) critically analyzed disability services websites of 18 U.S. institutions, and (2) interviewed 17 disability services staff. Disability services websites publicly present institutional accommodation policies and guidelines, and staff are responsible for identifying, providing, and implementing reasonable accommodations. We found that policies may be interpreted differently depending on specific student circumstances. We discuss our findings in two main themes: (a) Policies and attitudes ascribed to disability, technology, and faculty, and (b) Impacts of policies and perspectives on accommodation decisions for graduate students. The contributions of this work include an empirical investigation of institutional support for disabled graduate students and suggestions for how to improve support from disability services offices to empower students.

In recent years, mobile accessibility has become an important trend with the goal of allowing all users the possibility of using any app without many limitations. User reviews include insights that are useful for app evolution. However, with the increase in the amount of received reviews, manually analyzing them is tedious and time-consuming, especially when searching for accessibility reviews. The goal of this paper is to support the automated identification of accessibility in user reviews, to help technology professionals in prioritizing their handling, and thus, creating more inclusive apps. Particularly, we design a model that takes as input accessibility user reviews, learns their keyword-based features, in order to make a binary decision, for a given review, on whether it is about accessibility or not. The model is evaluated using a total of 5,326 mobile app reviews. The findings show that (1) our model can accurately identify accessibility reviews, outperforming two baselines, namely keyword-based detector and a random classifier; (2) our model achieves an accuracy of 85% with relatively small training dataset; however, the accuracy improves as we increase the size of the training dataset.