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Download full textPreel-Dumas, Camille; Hendra, Richard; Denison, Dakota – MDRC, 2023
This brief explores data science methods that workforce programs can use to predict participant success. With access to vast amounts of data on their programs, workforce training providers can leverage their management information systems (MIS) to understand and improve their programs' outcomes. By predicting which participants are at greater risk…
Descriptors: Labor Force Development, Programs, Prediction, Success
Direct linkAparajita Jaiswal – ProQuest LLC, 2022
The discipline of data science has gained substantial attention recently. This is mainly attributed to the technological advancement that led to an exponential increase in computing power and has made the generation and recording of enormous amounts of data possible on an everyday basis. It has become crucial for industries to wrangle, curate, and…
Descriptors: Data Science, Skill Development, Undergraduate Students, Science Process Skills
Peer reviewedMakar, Katie; Fry, Kym; English, Lyn – ZDM: Mathematics Education, 2023
Much of the mathematics that children experience in school neglect the skills increasingly needed for citizenship, particularly the power of complex data to investigate and make sense of the world. We draw on the relatively new field of data science as a multi-disciplinary approach to investigate problems through analysis of massive, non-standard,…
Descriptors: Elementary School Students, Citizenship Education, Data Science, Mathematics Education
Teruni Lamberg, Editor; Diana Moss, Editor – North American Chapter of the International Group for the Psychology of Mathematics Education, 2023
The Forty Fifth Annual meeting of the North American chapter of the International Group for the Psychology of Mathematics Education was held PME-NA 45 in Reno, Nevada, October 1-4, 2023. The conference theme is "Engaging All Learners." Math learning should be a joyful experience for all students. When students are engaged and inspired,…
Descriptors: Mathematics Education, Learner Engagement, Student Motivation, Student Interests
Amanda Barany; Andi Danielle Scarola; Alex Acquah; Sayed Mohsin Reza; Michael A. Johnson; Justice Walker – Information and Learning Sciences, 2024
Purpose: There is a need for precollege learning designs that empower youth to be epistemic agents in contexts that intersect burgeoning areas of computing, big data and social media. The purpose of this study is to explore how "sandbox" or open-inquiry data science with social media supports learning. Design/methodology/approach: This…
Descriptors: Student Empowerment, Data Science, Social Media, Open Education
Nathan A. Quarderer; Leah Wasser; Anne U. Gold; Patricia Montaño; Lauren Herwehe; Katherine Halama; Emily Biggane; Jessica Logan; David Parr; Sylvia Brady; James Sanovia; Charles Jason Tinant; Elisha Yellow Thunder; Justina White Eyes; LaShell Poor Bear/Bagola; Madison Phelps; Trey Orion Phelps; Brett Alberts; Michela Johnson; Nathan Korinek; William Travis; Naomi Jacquez; Kaiea Rohlehr; Emily Ward; Elsa Culler; R. Chelsea Nagy; Jennifer Balch – Journal of Statistics and Data Science Education, 2025
Today's data-driven world requires earth and environmental scientists to have skills at the intersection of domain and data science. These skills are imperative to harness information contained in a growing volume of complex data to solve the world's most pressing environmental challenges. Despite the importance of these skills, Earth and…
Descriptors: Electronic Learning, Earth Science, Environmental Education, Science Education
Integrating Computational Data Science in University Curriculum for the New Generation of Scientists
Renu, N.; Sunil, K. – Higher Education for the Future, 2023
Integration of computational data science (CDS) into the university curriculum offers several advantages for students, faculty and the institution. This article discusses the benefits to students of introducing CDS into the university curriculum with a focus on developing skills in cheminformatics, data analysis, structure--activity relationships,…
Descriptors: Data Science, Higher Education, College Students, Skill Development
Anna Khalemsky; Yelena Stukalin – Statistics Education Research Journal, 2024
The article describes the inclusive perspective of instruction of multi-stage practical projects in undergraduate non-STEM statistics and data mining courses at an academic college in Israel. The student population is highly diverse, comprising individuals from various cultural and ethnic groups. The study examines the impact of diversity on…
Descriptors: Foreign Countries, Undergraduate Students, Statistics Education, Data Science
Rethlefsen, Melissa L.; Norton, Hannah F.; Meyer, Sarah L.; MacWilkinson, Katherine A.; Smith, Plato L.; Ye, Hao – Journal of Statistics and Data Science Education, 2022
Research Reproducibility: Educating for Reproducibility, Pathways to Research Integrity was an interdisciplinary, conference hosted virtually by the University of Florida in December 2020. This event brought together educators, researchers, students, policy makers, and industry representatives from across the globe to explore best practices,…
Descriptors: Interdisciplinary Approach, Educational Research, Replication (Evaluation), Integrity
Tanya Mae Lamar – ProQuest LLC, 2023
The divide between those who do and those who do not excel in mathematics is patterned in problematic ways. Women and people of color are typically underrepresented in Science, Technology, Engineering, and Math (STEM) and other quantitative fields (ex. Finance) where mathematics plays gatekeeper. However, mathematics is not a subject these groups…
Descriptors: Data Science, STEM Education, High School Students, Student Attitudes
Love, Patrick – ProQuest LLC, 2019
Circulation studies, as the theory of ecological spread of information, impacts public perception of knowledge-making, and digital circulation (i.e. online information sharing) impacts what people expect online knowledge-making and online education is or should be. Online education is becoming a new norm for students and universities at a time…
Descriptors: Electronic Learning, Online Courses, Information Dissemination, Writing Instruction
Herro, Danielle; Madison, Matthew; Arastoopour Irgens, Golnaz; Hirsch, Shanna; Abimbade, Oluwadara; Adisa, Oluwajoba – Journal of Technology and Teacher Education, 2022
Data science and computational thinking (CT) skills are important STEM literacies necessary to make informed daily decisions. In elementary schools, particularly in rural areas, there is little instruction and limited research towards understanding and developing these literacies. Using a Research-Practice Partnership model (RPP; Coburn &…
Descriptors: Elementary School Teachers, Data Science, Curriculum Design, Faculty Development
Yi Zheng; Fern Van Vliet; Jeong Im Jin – Educational Research and Evaluation, 2024
This case study examined the current assessment practices in the math school of a large research university in the United States. After reviewing a sample of course syllabi offered in the spring 2021 semester, we descriptively summarized the use of 19 assessment methods in the school and examined the assessment patterns by subjects, class…
Descriptors: Student Centered Learning, Student Evaluation, College Mathematics, College Students
Ian Lowrie – ProQuest LLC, 2017
This dissertation focuses on elite efforts to restructure work and education in the Russian data sciences. Russia has long had a strong national program in theoretical mathematics, but has been substantially less successful at applying this expertise to develop modern computational science, infrastructure, and business. As the Russian extraction…
Descriptors: Artificial Intelligence, Electronic Learning, Technology Uses in Education, Data Science
P. Janelle McFeetors – Sage Research Methods Cases, 2016
This case study describes an experience of using constructivist grounded theory to analyze data. The project investigated how high school students improved their approaches to learning mathematics. Over 4 months, students participated in processes which supported their learning while simultaneously generating data, including interactive writing,…
Descriptors: High School Students, Mathematics Education, Data Analysis, Data Interpretation
