Paper from the 2016 Advancing Social Justice from Classroom to Community Conference.
Computational algorithmic thinking (CAT) is the ability to design, implement, and assess the implementation of algorithms to solve a range of problems. Supporting Computational Algorithmic Thinking (SCAT) is a longitudinal project that explores the development of CAT capabilities by guiding African American middle-school girls through the iterative game design cycle, resulting in a set of complex games around broad themes.
This paper explores African American middle-school girls' perspectives of their experience with the Supporting Computational Algorithmic Thinking (SCAT) project and perceptions of themselves as game designers.
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 the solution solves the problem, and evaluating the solution based on some set of criteria.
This article explores middle school girls' reflections about the difficulties they faced while using computational algorithmic thinking capabilities as they engaged in collaborative game design for social change. Authors focus on how these difficulties changed over the course of three years as well as new difficulties that emerged from year to year as girls become more expert game designers and computational algorithmic thinkers.
Much of the research in science education that explores the influence of a racial and gendered identity on science, technology, engineering, and mathematics (STEM) engagement for Black women situate their identities primarily as responses to the oppression and struggles they face in STEM. In this study, we use Phenomenological Variant Ecological Systems Theory as a strengths‐based approach to investigate 10 undergraduate Black women’s perceptions of race and gender on their STEM identity development and engagement.
In this study, authors use Phenomenological Variant Ecological Systems Theory as a strengths‐based approach to investigate 10 undergraduate Black women’s perceptions of race and gender on their STEM identity development and engagement.
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.
This article introduces CAT as explored through the Supporting Computational Algorithmic Thinking (SCAT) project, an ongoing longitudinal between-subjects research project and enrichment program that guides African-American middle school girls (SCAT Scholars) through the iterative game design cycle resulting in a set of complex games around broad themes.
High quality early childhood education and science, technology, engineering, and mathematics (STEM) learning have gained recognition as key levers in the progress toward high quality education for all students. STEM activities can be an effective platform for providing rich learning experiences that are accessible to dual language learners and students from all backgrounds. To do this well, teachers need professional development on how to integrate STEM into preschool curricula, and how to design experiences that support the dual language learners in the classroom.
In this article, the authors outline the main components and the iterative design process we undertook to ensure that the professional supports are relevant and effective for teachers and children.
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.
This article describes the development of four novel non-visual panning methods designed from the onset with consideration of these perceptual and cognitive constraints.
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.
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.
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.
The current study investigates two questions: (1) Do screen size and grid density impact a user's accuracy on pattern-matching tasks? (2) Do screen size and grid density impact a user's time on task?
