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Showing 1 to 15 of 26 results Save | Export
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Tenzin Doleck; Pedram Agand; Dylan Pirrotta – Education and Information Technologies, 2025
As is rapidly becoming clear, data science increasingly permeates many aspects of life. Educational research recognizes the importance and complexity of learning data science. In line with this imperative, there is a growing need to investigate the factors that influence student performance in data science tasks. In this paper, we aimed to apply…
Descriptors: Prediction, Data Science, Performance, Data Analysis
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Peter J. Woods; Camillia Matuk; Kayla DesPortes; Ralph Vacca; Marian Tes; Veena Vasudevan; Anna Amato – Critical Studies in Education, 2024
As visual cultures scholars have argued, visual expression and aesthetic artifacts largely comprise the modern world. This includes the production of the school as an institution. A critical approach to education therefore must reinscribe students with the ability to see what educational processes attempt to hide and to construct an understanding…
Descriptors: Data Science, Statistics Education, Visualization, Aesthetics
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Travis Weiland; Immanuel Williams – Journal of Statistics and Data Science Education, 2024
In this article, we consider how to make data more meaningful to students through the choice of data and the activities we use them in drawing upon students lived experiences more in the teaching of statistics and data science courses. In translating scholarship around culturally relevant pedagogy from the fields of education and mathematics…
Descriptors: Undergraduate Students, Predominantly White Institutions, Statistics Education, Culturally Relevant Education
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Rick A. Hudson; Gemma F. Mojica; Hollylynne S. Lee; Stephanie Casey – Computers in the Schools, 2025
Innovative dynamic data tools afford opportunities for K-12 students and teachers to explore multivariate data and create linked data representations. These tools also support engagement in data moves, which are transnumerative actions to process, organize, and visualize data. The current study sought to understand how prospective K-12 mathematics…
Descriptors: Statistics Education, Visualization, Data Science, Preservice Teachers
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Barb Bennie; Richard A. Erickson – Journal of Statistics and Data Science Education, 2024
Effective undergraduate statistical education requires training using real-world data. Textbook datasets seldom match the complexities and messiness of real-world data and finding these datasets can be challenging for educators. Consulting and industrial datasets often have nondisclosure agreements. Academic datasets often require subject area…
Descriptors: Undergraduate Students, Statistics Education, Data Science, Earth Science
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Gail Burrill; Maxine Pfannkuch – ZDM: Mathematics Education, 2024
The rapidly increasing capacity of technology to collect, organize, and manage data has spurred changes in the practice of statistics: new methods of collecting data, large data sets, new forms of data, different ways to visualize and represent data, and recognition of the importance of being able to understand and to communicate data-based…
Descriptors: Statistics Education, Educational Trends, Data Science, Context Effect
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Gabriella Roby Dodd; Cedric Gondro; Tasia M.Taxis; Margaret Young; Breno Fragomeni – NACTA Journal, 2024
The objectives of this study were to identify gaps in educational training for undergraduate and graduate students in agricultural data science, propose paths for filling these gaps, and provide an annotated list of resources currently available to different training levels. Data in this study was collected through three voluntary surveys catered…
Descriptors: Data Science, Statistics Education, Agriculture, Genetics
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Victoria Delaney; Victor R. Lee – Information and Learning Sciences, 2024
With increased focus on data literacy and data science education in K-12, little is known about what makes a data set preferable for use by classroom teachers. Given that educational designers often privilege authenticity, the purpose of this study is to examine how teachers use features of data sets to determine their suitability for authentic…
Descriptors: High School Teachers, Data Use, Information Literacy, Aesthetics
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Wole Michael Olatokun; Oluyemi Folorunso Ayanbode; Sunday Oluwadare Oladipo – Education and Information Technologies, 2025
This study examined the data science career preference, data science skills, and core competencies of 416 students from fourteen Nigerian universities using a Google Forms-created structured online questionnaire. A convenience sampling technique was adopted to select the participants. Data were analysed using both descriptive and inferential…
Descriptors: Data Science, Preferences, College Students, Foreign Countries
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Gaoxia Zhu; Chew Lee Teo; Aloysius Kian-Keong Ong; Katherine Guangji Yuan; Chin Lee Ker; Yuqin Yang – Education and Information Technologies, 2025
Preparing the new generation to be data-literate citizens is a pressing challenge, and some explorations have been made to cultivate K-12 students' data science skills and attitudes. However, there is a lack of instructional models to guide the design of data science programs in K-12 due to its complex and interdisciplinary nature as well as the…
Descriptors: Data Science, Skill Development, Secondary School Students, Cooperative Learning
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Gafny, Ronit; Ben-Zvi, Dani – Teaching Statistics: An International Journal for Teachers, 2023
In recent years, big data has become ubiquitous in our day-to-day lives. Therefore, it is imperative for educators to integrate nontraditional (big) data into statistics education to ensure that students are prepared for a big data reality. This study examined graduate students' expressions of uncertainty while engaging with traditional and…
Descriptors: Student Attitudes, Data Science, Data Analysis, Models
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Carrie Wright; Qier Meng; Michael R. Breshock; Lyla Atta; Margaret A. Taub; Leah R. Jager; John Muschelli; Stephanie C. Hicks – Journal of Statistics and Data Science Education, 2024
With unprecedented and growing interest in data science education, there are limited educator materials that provide meaningful opportunities for learners to practice "statistical thinking," as defined by Wild and Pfannkuch, with messy data addressing real-world challenges. As a solution, Nolan and Speed advocated for bringing…
Descriptors: Statistics, Statistics Education, Open Educational Resources, Case Method (Teaching Technique)
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Ram B. Basnet; David J. Lemay; Paul Bazelais – Knowledge Management & E-Learning, 2024
Academic and practitioner interest in data science has increased considerably. Yet scholarly understanding of what motivates students to learn data science is still limited. Drawing on the theory of planned behavior, we propose a research model to examine the determinants of behavioral intentions to learn data science. In the proposed research…
Descriptors: Student Attitudes, Intention, Data Science, Statistics Education
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Reza Moeti; Abolfazl Rafiepour; Mohammad Reza Fadaee – Mathematics Teaching Research Journal, 2024
Despite the increasing interest in data science education in the world, its teaching is not included in the curricula (junior secondary) and there is little information about it. Google Trends is discussed as a tool and database in school data science. Also, in different subjects, students were able to create and interpret graphs using this tool.…
Descriptors: Foreign Countries, Data Science, Statistics Education, Middle School Students
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Hildreth, Laura A.; Miley, Michelle; Strickland, Erin; Swisher, Jacob – Journal of Statistics and Data Science Education, 2023
Being able to communicate effectively is an essential skill for statisticians and data scientists. Despite this, communication skills are not frequently taught or emphasized in statistics and data science courses. In this article, we describe a series of four workshops that were developed to enhance the written communication skills of statistics…
Descriptors: Writing Workshops, Writing Skills, Communication Skills, Statistics Education
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