Modeling First: Applying Learning Science to the Teaching of Introductory Statistics
Students learn many concepts in the introductory statistics course, but even our most successful students end up with rigid, ritualized knowledge that does not transfer easily to new situations. In this article we describe our attempt to apply theories and findings from learning science to the desig...
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2020-10-01
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Online Access: | http://dx.doi.org/10.1080/10691898.2020.1844106 |
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doaj-4f4d4699a4024cd593d130dbd3d15e7c2020-11-25T04:09:17ZengTaylor & Francis GroupJournal of Statistics Education1069-18982020-10-010012310.1080/10691898.2020.18441061844106Modeling First: Applying Learning Science to the Teaching of Introductory StatisticsJi Y. Son0Adam B. Blake1Laura Fries2James W. Stigler3California State UniversityUniversity of CaliforniaUniversity of CaliforniaUniversity of CaliforniaStudents learn many concepts in the introductory statistics course, but even our most successful students end up with rigid, ritualized knowledge that does not transfer easily to new situations. In this article we describe our attempt to apply theories and findings from learning science to the design of a statistics course that aims to help students build a coherent and interconnected representation of the domain. The resulting practicing connections approach provides students with repeated opportunities to practice connections between core concepts (especially the concepts of statistical model, distribution, and randomness), key representations (R programming language and computational techniques such as simulation and bootstrapping), and real-world situations statisticians face as they explore variation, model variation, and evaluate and compare statistical models. We provide a guided tour through our curriculum implemented in an interactive online textbook (CourseKata.org) and then provide some evidence that students who complete the course are able to transfer what they have learned to the learning of new statistical techniques.http://dx.doi.org/10.1080/10691898.2020.1844106statistical modelpracticing connectionstransferintroductory statisticssimulationlearning sciences |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ji Y. Son Adam B. Blake Laura Fries James W. Stigler |
spellingShingle |
Ji Y. Son Adam B. Blake Laura Fries James W. Stigler Modeling First: Applying Learning Science to the Teaching of Introductory Statistics Journal of Statistics Education statistical model practicing connections transfer introductory statistics simulation learning sciences |
author_facet |
Ji Y. Son Adam B. Blake Laura Fries James W. Stigler |
author_sort |
Ji Y. Son |
title |
Modeling First: Applying Learning Science to the Teaching of Introductory Statistics |
title_short |
Modeling First: Applying Learning Science to the Teaching of Introductory Statistics |
title_full |
Modeling First: Applying Learning Science to the Teaching of Introductory Statistics |
title_fullStr |
Modeling First: Applying Learning Science to the Teaching of Introductory Statistics |
title_full_unstemmed |
Modeling First: Applying Learning Science to the Teaching of Introductory Statistics |
title_sort |
modeling first: applying learning science to the teaching of introductory statistics |
publisher |
Taylor & Francis Group |
series |
Journal of Statistics Education |
issn |
1069-1898 |
publishDate |
2020-10-01 |
description |
Students learn many concepts in the introductory statistics course, but even our most successful students end up with rigid, ritualized knowledge that does not transfer easily to new situations. In this article we describe our attempt to apply theories and findings from learning science to the design of a statistics course that aims to help students build a coherent and interconnected representation of the domain. The resulting practicing connections approach provides students with repeated opportunities to practice connections between core concepts (especially the concepts of statistical model, distribution, and randomness), key representations (R programming language and computational techniques such as simulation and bootstrapping), and real-world situations statisticians face as they explore variation, model variation, and evaluate and compare statistical models. We provide a guided tour through our curriculum implemented in an interactive online textbook (CourseKata.org) and then provide some evidence that students who complete the course are able to transfer what they have learned to the learning of new statistical techniques. |
topic |
statistical model practicing connections transfer introductory statistics simulation learning sciences |
url |
http://dx.doi.org/10.1080/10691898.2020.1844106 |
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