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Lu, Yonggang; Zheng, Qiujie; Quinn, Daniel – Journal of Statistics and Data Science Education, 2023
We present an instructional approach to teaching causal inference using Bayesian networks and "do"-Calculus, which requires less prerequisite knowledge of statistics than existing approaches and can be consistently implemented in beginner to advanced levels courses. Moreover, this approach aims to address the central question in causal…
Descriptors: Bayesian Statistics, Learning Motivation, Calculus, Advanced Courses
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Bao, Lei; Koenig, Kathleen; Xiao, Yang; Fritchman, Joseph; Zhou, Shaona; Chen, Cheng – Physical Review Physics Education Research, 2022
Abilities in scientific thinking and reasoning have been emphasized as core areas of initiatives, such as the Next Generation Science Standards or the College Board Standards for College Success in Science, which focus on the skills the future will demand of today's students. Although there is rich literature on studies of how these abilities…
Descriptors: Physics, Science Instruction, Teaching Methods, Thinking Skills
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Lee, Michael D. – Cognitive Science, 2006
We consider human performance on an optimal stopping problem where people are presented with a list of numbers independently chosen from a uniform distribution. People are told how many numbers are in the list, and how they were chosen. People are then shown the numbers one at a time, and are instructed to choose the maximum, subject to the…
Descriptors: Bayesian Statistics, Inferences, Numbers, Cognitive Processes