Broadening Participation

Computational algorithmic thinking (CAT) is the ability to design, implement, and assess the implementation of algorithms to solve a range of problems. It involves identifying and understanding a problem, articulating an algorithm or set of algorithms in the form of a solution to the problem, implementing that solution in such a way that it solves the problem, and evaluating the solution based on some set of criteria. CAT has roots in Mathematics, through problem solving and algorithmic thinking. CAT lies at the heart of Computer Science, which is defined as the study of algorithms.

The purpose of this study was to develop and validate a survey of opportunities to participate (OtP) in science that will allow educators and researchers to closely approximate the types of learning opportunities students have in science classrooms. Additionally, we examined whether and how opportunity gaps in science learning may exist across schools with different socioeconomic levels. The OtP in science survey consists of four dimensions that include acquiring foundational knowledge, planning an investigation, conducting an investigation, and using evidence to communicate findings.

Making sense of fractions can be challenging for students with learning disabilities. Dr. Jessica Hunt of North Carolina State University studies how these children think and learn and is developing novel teaching methods that facilitate mathematics learning for this underserved population.

Economically disadvantaged and underrepresented high school students in many urban, rural, and small suburban communities don’t have access to Advanced Placement® (AP®) courses either because of a lack of trained teachers, limited or no AP program, or a school history of low participation. Physics is often a “gate keeper” course to entry into physical science, technology, engineering and mathematics (STEM) careers and academic programs.

Case studies from the FAACT project.

Understand students’ fraction concepts through interview tasks.  Includes tasks and guide to record student thinking.

Touchscreen devices, such as smartphones and tablets, represent a modern solution for providing graphical access to people with blindness and visual impairment (BVI). However, a significant problem with these solutions is their limited screen real estate, which necessitates panning or zooming operations for accessing large-format graphical materials such as maps.

Touchscreen-based smart devices, such as smartphones and tablets, offer great promise for providing blind and visually-impaired (BVI) users with a means for accessing graphics non-visually. However, they also offer novel challenges as they were primarily developed for use as a visual interface. This paper studies key usability parameters governing accurate rendering of haptically-perceivable graphical materials.

Touchscreen-based smart devices, such as smartphones and tablets, offer great promise for providing blind and visually-impaired (BVI) users with a means for accessing graphics non-visually. However, they also offer novel challenges as they were primarily developed for use as a visual interface. This paper studies key usability parameters governing accurate rendering of haptically-perceivable graphical materials.

Significance: Touchscreen-based, multimodal graphics represent an area of increasing research in digital access for individuals with blindness or visual impairments; yet, little empirical research on the effects of screen size on graphical exploration exists. This work probes if and whenmore screen area is necessary in supporting a patternmatching task.