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Ihrmark, Daniel; Tyrkkö, Jukka – Education for Information, 2023
The combination of the quantitative turn in linguistics and the emergence of text analytics has created a demand for new methodological skills among linguists and data scientists. This paper introduces KNIME as a low-code programming platform for linguists interested in learning text analytic methods, while highlighting the considerations…
Descriptors: Linguistics, Data Science, Programming, Data Analysis
Tongxi Liu – Journal of Educational Computing Research, 2024
Addressing cognitive disparities has become a paramount concern in computational thinking (CT) education. The intricate and nuanced relationships between CT and cognitive variations emphasize the needs to accommodate diverse cognitive profiles when fostering CT skills, recognizing that these cognitive functions can manifest as either strengths or…
Descriptors: Executive Function, Computation, Thinking Skills, Data Science
Andrew A. Tawfik; Linda Payne; Andrew M. Olney – Technology, Knowledge and Learning, 2024
Theorists and educators increasingly highlight the importance of computational thinking in STEM education. While various scaffolding strategies describe how to best support this skillset (i.e., paired programming, worked examples), less research has focused on the design and development of these digital tools. One way to support computational…
Descriptors: Thinking Skills, Computation, STEM Education, Scaffolding (Teaching Technique)
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
Ibrahim Oluwajoba Adisa; Danielle Herro; Oluwadara Abimbade; Golnaz Arastoopour Irgens – Information and Learning Sciences, 2024
Purpose: This study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms. Design/methodology/approach: This paper describes a pedagogical approach that uses a data science…
Descriptors: Learner Engagement, Elementary School Students, Data Science, Computation
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
Lischka, Alyson E., Ed.; Dyer, Elizabeth B., Ed.; Jones, Ryan Seth, Ed.; Lovett, Jennifer N., Ed.; Strayer, Jeremy, Ed.; Drown, Samantha, Ed. – North American Chapter of the International Group for the Psychology of Mathematics Education, 2022
These proceedings are a written record of the research presented at the 44th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (PME-NA) held in Nashville, Tennessee, and virtually. This year's conference theme is "Critical Dissonance and Resonant Harmony." The aim of this…
Descriptors: Educational Psychology, Mathematics Education, Conferences (Gatherings), Conference Papers