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Showing 1 to 15 of 24 results Save | Export
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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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Paul Biberstein; Thomas Castleman; Luming Chen; Shriram Krishnamurthi – Informatics in Education, 2024
CODAP is a widely-used programming environment for secondary school data science. Its direct-manipulation-based design offers many advantages to learners, especially younger students. Unfortunately, these same advantages can become a liability when it comes to repeating operations consistently, replaying operations (for reproducibility), and also…
Descriptors: Data Science, Secondary School Students, Programming, Open Source Technology
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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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Nick Hopwood; Tracey-Ann Palmer; Gloria Angela Koh; Mun Yee Lai; Yifei Dong; Sarah Loch; Kun Yu – International Journal of Research & Method in Education, 2025
Student emotions influence assessment task behaviour and performance but are difficult to study empirically. The study combined qualitative data from focus group interviews with 22 students and 4 teachers, with quantitative real-time learning analytics (facial expression, mouse click and keyboard strokes) to examine student emotional engagement in…
Descriptors: Psychological Patterns, Student Evaluation, Learning Analytics, Learner Engagement
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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
Hollylynne S. Lee; Emily P. Thrasher; Matt Grossman; Gemma F. Mojica; Bruce Graham; Adrian Kuhlman – Grantee Submission, 2023
This paper presents the design of an innovative platform to support teachers' personalized learning related to teaching statistics and data science in grades 6-12 (http://instepwithdata.org). Through a study of 32 pilot users, the authors describe how teachers utilized supports such as personalization surveys, tracking of progress on a dashboard,…
Descriptors: Secondary School Teachers, Faculty Development, Statistics Education, Data Science
Emma Mary Klugman – ProQuest LLC, 2024
Statistics & data science are growing, rapidly evolving, and increasingly important for an informed citizenry in a data-saturated world. In this dissertation, I address two central questions: (1) who is taking statistics? and (2) what are statistics courses teaching? I estimate that 920,000 US students take statistics in high school each year,…
Descriptors: Data Science, Statistics Education, High School Students, Profiles
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Wilkerson, Michelle Hoda; Lanouette, Kathryn; Shareff, Rebecca L. – Mathematical Thinking and Learning: An International Journal, 2022
Data preparation (also called "wrangling" or "cleaning") -- the evaluation and manipulation of data prior to formal analysis -- is often dismissed as a precursor to meaningful engagement with a dataset. Here, we re-envision data preparation in light of calls to prepare students for a data-rich world. Traditionally, curricular…
Descriptors: Data Science, Information Literacy, Data Analysis, Secondary School Students
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Jo Boaler; Kira Conte; Ken Cor; Jack A. Dieckmann; Tanya LaMar; Jesse Ramirez; Megan Selbach-Allen – Journal of Statistics and Data Science Education, 2025
This article reports on a multi-method study of a high school course in data science, finding that students who take data science take more mathematics courses than those who do not, there are more under-represented students in data science than is typical for other advanced mathematics courses; that the students who take data science are more…
Descriptors: Mathematics Instruction, Opportunities, High School Students, Data Science
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Toshiya Arakawa; Haruki Miyakawa – Technology, Knowledge and Learning, 2025
Data science education in Japan extends from elementary to high school students. However, some studies show that this has not enhanced interest or curiosity in data science. Therefore, gamification appears to be an efficient method for encouraging high school students' interest in data science, with research indicating that video games are…
Descriptors: Data Science, Educational Games, Statistics Education, Foreign Countries
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Marianthi Grizioti; Chronis Kynigos – Informatics in Education, 2024
Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying…
Descriptors: Computation, Thinking Skills, Data Science, Classification
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Bussani, Andrea; Comici, Cinzia – Physics Teacher, 2023
Data analysis and interpretation has always played a fundamental role in the scientific curricula of high school students. The spread of digitalization has further increased the number of learning environments whereby this topic can be effectively taught: as a matter of fact, the ever-growing diffusion of data science across diverse sectors of…
Descriptors: Learning Analytics, High Schools, Data Interpretation, Data Science
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Gemma F. Mojica; Emily Thrasher; Adrian Kuhlman; Bruce Graham; Hollylynne S. Lee; Michelle Pace – North American Chapter of the International Group for the Psychology of Mathematics Education, 2023
In this study, 82 middle and high school teachers engaged with the InSTEP online professional learning platform to develop their expertise in teaching data science and statistics. We investigated teachers' engagement within the platform, aspects of the platform that were most and least effective in building teachers' expertise, and the extent to…
Descriptors: Middle School Teachers, High School Teachers, Faculty Development, Data Science
Barrie D. Fitzgerald – ProQuest LLC, 2024
Regional comprehensive universities offer accessible and diverse undergraduate educational programs, while grappling with funding cuts and affordability. The study's first research question underscores the enduring importance of factors such as student characteristics, pre-college characteristics, and financial situations. The findings highlight…
Descriptors: Secondary School Curriculum, Curriculum Evaluation, Postsecondary Education, College Freshmen
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Fergusson, Anna; Pfannkuch, Maxine – Mathematical Thinking and Learning: An International Journal, 2022
The advent of data science has led to statistics education researchers re-thinking and expanding their ideas about tools for teaching statistical modeling, such as the use of code-driven tools at the secondary school level. Methods for statistical inference, such as the randomization test, are typically taught within secondary school classrooms…
Descriptors: Foreign Countries, Data Science, Statistics Education, Mathematical Models
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