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Full-Text Articles in Education
Factors In The Probability Of Covid-19 Transmission In University Classrooms, Charles Connor
Factors In The Probability Of Covid-19 Transmission In University Classrooms, Charles Connor
Numeracy
University students and faculty members need an effective strategy to evaluate and reduce the probability that an individual will become infected with COVID-19 as a result of classroom interactions. Models are developed here that consider the probability an individual will become infected as a function of: prevalence of the disease in the university community, number of students in class, number of class meetings, and transmission rate in the classroom given the presence of an infected individual. Absolute probabilities that an individual will become infected in a classroom environment cannot be calculated because some of these factors have unknown values. Nevertheless, …
Using Visual Analogies To Teach Introductory Statistical Concepts, Jessica S. Ancker, Melissa D. Begg
Using Visual Analogies To Teach Introductory Statistical Concepts, Jessica S. Ancker, Melissa D. Begg
Numeracy
Introductory statistical concepts are some of the most challenging to convey in quantitative literacy courses. Analogies supplemented by visual illustrations can be highly effective teaching tools. This literature review shows that to exploit the power of analogies, teachers must select analogies familiar to the audience, explicitly link the analog with the target concept, and avert misconceptions by explaining where the analogy fails. We provide guidance for instructors and a series of visual analogies for use in teaching medical and health statistics.
Parts Of The Whole: Error Estimation For Science Students, Dorothy Wallace
Parts Of The Whole: Error Estimation For Science Students, Dorothy Wallace
Numeracy
It is important for science students to understand not only how to estimate error sizes in measurement data, but also to see how these errors contribute to errors in conclusions they may make about the data. Relatively small errors in measurement, errors in assumptions, and roundoff errors in computation may result in large error bounds on computed quantities of interest. In this column, we look closely at a standard method for measuring the volume of cancer tumor xenografts to see how small errors in each of these three factors may contribute to relatively large observed errors in recorded tumor volumes.