Diversifying the Next Generation of Information Scientists: Six Years of Implementation and Outcomes for a Year-Long REU Program

Booth, Kayla M.; Dosono, Bryan; Redmiles, Elissa M.; Morales, Miraida; Depew, Michael J.; Farzan, Rosta; Herman, Everett; Trahan, Keith; Tananis, Cindy

doi:10.1007/978-3-319-78105-1_75

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…In many programs, students are expected to join the program with the background needed to work on their projects and then receive project specific training. Various programs have introduced structured teaching tracks to provide students with the skills needed to succeed in their research [15], [16]. Typically, all projects in a program follow a particular topic, allowing for narrow standardization of a skill curriculum.…”

Section: A Related Workmentioning

confidence: 99%

An Immersive Curriculum to Develop Computational Science and Research Skills in a Cohort-Based Internship Program

Johnson,

Villafañe-Delgado,

Symonette

et al. 2024

Preprint

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Workforce education is a key challenge as computational science, including data science and machine learning, increasingly influences critical application spaces such as public health and medicine, space exploration, national security, autonomous systems and cybersecurity. Developing core software development, analysis, and machine learning skills will enable workers to have impact across a range of spaces. These skills are in high demand in industrial research and development, but we do not believe that traditional recruiting and training models in industry (e.g., internships, continuing education) are serving the needs of the diverse populations of students who will be required to revolutionize these fields. To accelerate workforce development in these key areas, we have designed and executed a machine learning and research skills training curriculum for our cohort-based research internship program, the Cohort-based Integrated Research Community for Undergraduate Innovation and Trailblazing (CIRCUIT). The program targets trailblazing, high-achieving students who face barriers in achieving their goals, and the training program is aimed at accelerating their growth as leaders in data science, machine learning, and artificial intelligence research. The training curricula and support structure is designed to be flexible to student and research project needs during a research internship. Utilizing both existing online material and custom workshops, this model consists of a compressed data science and machine learning curriculum, a series of professional development training workshops, and team-based challenges. Strategies allow for customization of these training efforts for individual students and projects. Over four cohorts, this training curricula has helped students achieve mastery of data science and machine learning concepts, produce key demonstrations and work products in their cohort research projects, and progress to further internships, graduate school, and employment.

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Section: A Related Workmentioning

confidence: 99%

An Immersive Curriculum to Develop Computational Science and Research Skills in a Cohort-Based Internship Program

Johnson,

Villafañe-Delgado,

Symonette

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Research to understand intersectionality in software design falls into three categories. (1) Empirically studying the need for intersectional HCI, e.g., via ethnographic or qualitative studies with members of the population of interest [6,15,25,43,51,52]. This is by far the most frequent in the literature.…”

Section: Related Workmentioning

confidence: 99%

“…At least two factors contribute to the expense. First, intersectionality work has relied heavily upon empirical work with real users in the particular population of interest (ranging from ethnography to statistical lab studies [6,15,25]), which leads to the challenge of gaining access to and the willingness of "enough" members of that particular understudied population to participate.…”

Section: Introductionmentioning

confidence: 99%

Intersectionality Goes Analytical: Taming Combinatorial Explosion Through Type Abstraction

Burnett¹,

Erwig²,

Fallatah³

et al. 2022

Preprint

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HCI researchers' and practitioners' awareness of intersectionality has been expanding, producing knowledge, recommendations, and prototypes for supporting intersectional populations. However, doing intersectional HCI work is uniquely expensive: it leads to a combinatorial explosion of empirical work (expense 1), and little of the work on one intersectional population can be leveraged to serve another (expense 2). In this paper, we explain how representations employed by certain analytical design methods correspond to type abstractions, and use that correspondence to identify a (de)compositional model in which a population's diverse identity properties can be joined and split. We formally prove the model's correctness, and show how it enables HCI designers to harness existing analytical HCI methods for use on new intersectional populations of interest. We illustrate through four design use-cases, how the model can reduce the amount of expense 1 and enable designers to leverage prior work to new intersectional populations, addressing expense 2.CCS Concepts: • Human-centered computing → Human computer interaction (HCI); HCI design and evaluation methods.

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“…In many programs, students are expected to join the program with the background needed to work on their projects and then receive project-specific training. Various programs have introduced structured teaching tracks to provide students with the skills needed to succeed in their research [13,14]. In many of these experiences, all projects follow a similar topic, and students learn narrow skills specific to that theme.…”

Section: Related Workmentioning

confidence: 99%

STEM Leadership and Training for Trailblazing Students in an Immersive Research Environment

Villafane-Delgado¹,

Erik²,

Hughes³

et al. 2020

Preprint

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Educating the workforce of tomorrow is an increasingly critical challenge for areas such as data science, machine learning, and artificial intelligence. These core skills may revolutionize progress in areas such as health care and precision medicine, autonomous systems and robotics, and neuroscience. Skills in data science and artificial intelligence are in high demand in industrial research and development, but we do not believe that traditional recruiting and training models in industry (e.g., internships, continuing education) are serving the needs of the diverse populations of students who will be required to revolutionize these fields. Our program, the Cohort-based Integrated Research Community for Undergraduate Innovation and Trailblazing (CIRCUIT), targets trailblazing, high-achieving students who face barriers in achieving their goals and becoming leaders in data science, machine learning, and artificial intelligence research. Traditional recruitment practices often miss these ambitious and talented students from nontraditional backgrounds, and these students are at a higher risk of not persisting in research careers. In the CIRCUIT program we recruit holistically, selecting students on the basis of their commitment, potential, and need. We designed a training and support model for our internship. This model consists of a compressed data science and machine learning curriculum, a series of professional development training workshops, and a team-based robotics challenge. These activities develop the skills these trailblazing students will need to contribute to the dynamic, team-based engineering teams of the future.

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Diversifying the Next Generation of Information Scientists: Six Years of Implementation and Outcomes for a Year-Long REU Program

Cited by 5 publications

References 11 publications

An Immersive Curriculum to Develop Computational Science and Research Skills in a Cohort-Based Internship Program

An Immersive Curriculum to Develop Computational Science and Research Skills in a Cohort-Based Internship Program

Intersectionality Goes Analytical: Taming Combinatorial Explosion Through Type Abstraction

STEM Leadership and Training for Trailblazing Students in an Immersive Research Environment

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