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Your search keyword '"Barr, Valerie"' showing total 14 results
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14 results on '"Barr, Valerie"'

2. What Must All Post-Secondary Students Learn about Computing? Expanding students’ understanding of computing’s potential.

Author

Barr, Valerie

Subjects
*COMPUTER science education, *POSTSECONDARY education, *REQUIRED courses (Education), *INTERDISCIPLINARY education, *DIVERSITY in education, *HISTORY of technology, *TECHNOLOGY & society, *HIGH technology industries
Abstract

The article presents the author's views on the importance of including computer science as part of the core curriculum in postsecondary education. Topics include the benefits of diversifying the computer science community through interdisciplinary education, a brief history of the high technology industry, and its effect on the current reality. The author offers recommendations for educators to incorporate components of computer science education into their curriculum.

Published
2022
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3. FACILITATING TEAM-BASED DATA SCIENCE: LESSONS LEARNED FROM THE DSC-WAV PROJECT.

Author

LEGACY, CHELSEY, ZIEFFLER, ANDREW, BAUMER, BENJAMIN S., BARR, VALERIE, and HORTON, NICHOLAS J.

Subjects
DATA science, WORK environment, STUDENT engagement, CURRICULUM, EDUCATORS
Abstract

While coursework provides undergraduate data science students with some relevant analytic skills, many are not given the rich experiences with data and computing they need to be successful in the workplace. Additionally, students often have limited exposure to team-based data science and the principles and tools of collaboration that are encountered outside of school. In this paper, we describe the DSC-WAV program, an NSF-funded data science workforce development project in which teams of undergraduate sophomores and juniors work with a local non-profit organization on a data-focused problem. To help students develop a sense of agency and improve confidence in their technical and non-technical data science skills, the project promoted a team-based approach to data science, adopting several processes and tools intended to facilitate this collaboration. Evidence from the project evaluation, including participant survey and interview data, is presented to document the degree to which the project was successful in engaging students in team-based data science, and how the project changed the students' perceptions of their technical and non-technical skills. We also examine opportunities for improvement and offer insight to other data science educators who may want to implement a similar team-based approach to data science projects at their own institutions. [ABSTRACT FROM AUTHOR]

Published
2023
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4. Facilitating team-based data science: lessons learned from the DSC-WAV project

Author

Legacy, Chelsey, Zieffler, Andrew, Baumer, Benjamin S., Barr, Valerie, and Horton, Nicholas J.

Subjects
FOS: Computer and information sciences, 97K80, 97P99, Statistics - Other Statistics, Other Statistics (stat.OT), ComputingMilieux_COMPUTERSANDEDUCATION, Statistics - Computation, Computation (stat.CO)
Abstract

While coursework provides undergraduate data science students with some relevant analytic skills, many are not given the rich experiences with data and computing they need to be successful in the workplace. Additionally, students often have limited exposure to team-based data science and the principles and tools of collaboration that are encountered outside of school. In this paper, we describe the DSC-WAV program, an NSF-funded data science workforce development project in which teams of undergraduate sophomores and juniors work with a local non-profit organization on a data-focused problem. To help students develop a sense of agency and improve confidence in their technical and non-technical data science skills, the project promoted a team-based approach to data science, adopting several processes and tools intended to facilitate this collaboration. Evidence from the project evaluation, including participant survey and interview data, is presented to document the degree to which the project was successful in engaging students in team-based data science, and how the project changed the students' perceptions of their technical and non-technical skills. We also examine opportunities for improvement and offer insight to other data science educators who may want to implement a similar team-based approach to data science projects at their own institutions.

Published
2021

5. Biotic resistance to invasion is ubiquitous across ecosystems of the United States.

Author

Beaury, Evelyn M., Finn, John T., Corbin, Jeffrey D., Barr, Valerie, Bradley, Bethany A., and Rejmanek, Marcel

Subjects
ECOSYSTEMS, INTRODUCED plants, BIODIVERSITY conservation, INTRODUCED species, SPECIES diversity, BIOLOGICAL invasions
Abstract

The biotic resistance hypothesis predicts that diverse native communities are more resistant to invasion. However, past studies vary in their support for this hypothesis due to an apparent contradiction between experimental studies, which support biotic resistance, and observational studies, which find that native and non‐native species richness are positively related at broad scales (small‐scale studies are more variable). Here, we present a novel analysis of the biotic resistance hypothesis using 24 456 observations of plant richness spanning four community types and seven ecoregions of the United States. Non‐native plant occurrence was negatively related to native plant richness across all community types and ecoregions, although the strength of biotic resistance varied across different ecological, anthropogenic and climatic contexts. Our results strongly support the biotic resistance hypothesis, thus reconciling differences between experimental and observational studies and providing evidence for the shared benefits between invasive species management and native biodiversity conservation. [ABSTRACT FROM AUTHOR]

Published
2020
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6. How We Teach CS2All, and What to Do About Database Decay.

Author

Barr, Valerie, Stonebraker, Michael, Fernandez, Raul Castro, Dong Deng, and Brodie, Michael

Subjects
*COMPUTER science education, *HIGHER education, *DATABASE management, *CULTURAL pluralism, *MOTIVATION (Psychology), *WOMEN in computer science, *MINORITY college students, *DATABASE administrators, *COMPUTER science students, SOCIAL aspects
Abstract

The article discusses efforts to incorporate culturally diverse students in computer science (CS) at U.S. universities and the prevention of database decay by database administrators (DBAs). An overview of underrepresented minority (URM) computer science students in the U.S., including the potential motivating factors that would incorporate women into CS curricula, is provided.

Published
2017
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7. Introducing CS to Newcomers, and JES As a Teaching Tool.

Author

Barr, Valerie and Guzdial, Mark

Subjects
*COMPUTER science education, *JYTHON (Computer program language), *COMPUTER programming education, *COMPUTER logic, *PROBLEM solving methodology
Abstract

The article discusses teaching computer science to Science and Technology Entry Program students at Union College's Kenney Community Center and the benefits of Jython Environment for Students, the text editing software for Python programming language. Topics include the use of a visual perception card game to analyze how human's solve problems versus how to program computers to solve problems, data representation inside computers, and the use of JES to facilitate programming in Media Computation.

Published
2016
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8. The Value of Ada.

Author

Barr, Valerie

Subjects
*COMPUTER systems, *COMPUTERS
Abstract

A personal narrative is presented which explores the author's experience of representing the Association for Computing Machinery (ACM) and ACM's Committee on Women in Computing (ACM-W) at the "Ada Lovelace Symposium" held at Oxford University in England.

Published
2016
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9. How Men Can Help Women in CS; Winning ‘Computing’s Nobel Prize’.

Author

Barr, Valerie and Stonebraker, Michael

Subjects
*WOMEN computer scientists, *ACM A.M. Turing Award, *COMPUTER science awards, *SOCIAL networks, *ACADEMIC-industrial collaboration, *VOCATIONAL guidance, *COMPUTER science
Abstract

The article presents a discussion on how men can promote women in computer science and also offers a personal narrative from one of the authors on receiving the Turing Award in computer science. To help women in computing, the article suggests men expand professional networks, read publications by women in the field, and encourage companies to assist educational programs.

Published
2015
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11. Advice on Teaching CS, and the Learnability of Programming Languages.

Author

Barr, Valerie and Guzdial, Mark

Subjects
*COMPUTER science education, *HIGHER education, *PROGRAMMING languages
Abstract

Two blog posts from the publication's website are presented, one on strategies for effective teaching of college computer science courses and one on the challenge and the importance of rendering computer programming languages easier to learn and to use for non-specialists.

Published
2015
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12. A Valuable Lesson, and Whither Hadoop?

Author

Barr, Valerie, Stonebraker, Michael, and Rofer, C.

Subjects
*OPEN source software
Abstract

The article presents a pair of excerpts from blogs at the publication's Website http://cacm.acm.org, one by computer science professor Valerie Barr about comments on women in technology from Microsoft Corp. chief executive Satya Nadella, and the other by computer science professor Michael Stonebraker about the Hadoop open-source software program.

Published
2015
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13. Gender Diversity in Computing: Are We Making Any Progress?

Author

Barr, Valerie

Subjects
*WOMEN in computer science, *SEX discrimination against women, *COMPUTER science education, *HIGHER education, *WOMEN computer scientists, *SEX discrimination in employment
Abstract

The article discusses gender diversity within the computer science (CS) field. Topics include an increase in the number of women enrolling in and staying in CS degree programs, a decline in the percentage of CS degrees that went to women between 1984 and 2015, and the role of Association for Computing Machinery (ACM) in changing the attitudes and behavior of people within the CS field related to gender bias.

Published
2017
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