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Dvir, Michal; Ben-Zvi, Dani – Instructional Science: An International Journal of the Learning Sciences, 2023
Estimating and accounting for statistical uncertainty have become essential in today's information age, and crucial for cultivating a sound decision making citizenry. Engaging with statistical uncertainty early on can support the gradual development of uncertainty-related considerations that are often challenging to foster at any age. Statistical…
Descriptors: Learning Processes, Computation, Numeracy, Attitudes
Nanette I. Marcum-Dietrich; Meredith Bruozas; Rachel Becker-Klein; Emily Hoffman; Carolyn Staudt – Journal of Science Education and Technology, 2024
The Precipitating Change Project was a 5-year development, implementation, and research study of an innovative 4-week middle school curricular unit in computational weather forecasting that integrates students' learning and use of meteorology and computational thinking (CT) concepts and practices. The project produced a list of CT skills and…
Descriptors: Indigenous Populations, Grade 8, Middle School Students, Urban Areas
Roelle, Julian; Renkl, Alexander – Journal of Educational Psychology, 2020
Example-based learning often uses a design in which learners first receive basic instructional explanations of new principles and concepts and then examples thereof. In this sequence, it is crucial that learners self-explain by using the content of the basic instructional explanations to elaborate on the examples. Typically, learners are not…
Descriptors: Demonstrations (Educational), Self Concept, High School Students, Instructional Effectiveness
Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam – International Educational Data Mining Society, 2015
This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features…
Descriptors: Learning Activities, Learning Processes, Data Collection, Student Behavior