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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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Odden, Tor Ole B.; Silvia, Devin W.; Malthe-Sørenssen, Anders – Journal of Research in Science Teaching, 2023
This article reports on a study investigating how computational essays can be used to help students in higher education STEM take up disciplinary epistemic agency--cognitive control and responsibility over one's own learning within the scientific disciplines. Computational essays are a genre of scientific writing that combine live, executable…
Descriptors: Computation, Essays, Undergraduate Students, STEM Education
Nasheen Nur – ProQuest LLC, 2021
The main goal of learning analytics and early detection systems is to extract knowledge from student data to understand students' trends of activities towards success and risk and design intervention methods to improve learning performance and experience. However, many factors contribute to the challenge of designing and building effective…
Descriptors: Artificial Intelligence, Undergraduate Students, Learning Analytics, Time Factors (Learning)
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Helmbrecht, Hawley; Nance, Elizabeth – Chemical Engineering Education, 2022
Tutorials for EXperimentalisT Interactive LEarning (TEXTILE) is an interactive semi-linear module-based curriculum for training students at various educational levels on data science methodologies currently utilized by research laboratories. We show how we developed our eleven module TEXTILE program to train 15 students from high school,…
Descriptors: Data Science, Methods, Science Laboratories, High School Students
Sahar Voghoei – ProQuest LLC, 2021
The importance of retention rate for higher education institutions has encouraged data analysts to present various methods to predict at-risk students. Their objective is to provide timely information that may enable educators to channel the most effective remedial treatments towards precisely targeted students in an efficient manner. The present…
Descriptors: Data Science, Academic Achievement, School Holding Power, Predictor Variables