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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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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
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Jose L. Salas; Xinran Wang; Mary C. Tucker; Ji Y. Son – Online Learning, 2024
Students believe mathematics is best learned by memorization; however, endorsing memorization as a study strategy is associated with a decrease in learning (Schoenfeld, 1989). When the world changed with the onset of the COVID-19 global pandemic, instruction transitioned to fully remote instruction where many assignments and examinations became…
Descriptors: Distance Education, Memorization, Pandemics, COVID-19
Aparajita 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