Abstract

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.

摘要

学生在统计入门课程中学习了许多概念，但即使是我们最成功的学生也最终掌握了难以转移到新情况的僵化、仪式化的知识。在本文中，我们描述了我们尝试将学习科学的理论和发现应用于统计学课程的设计，旨在帮助学生建立一个连贯且相互关联的领域表示。由此产生的练习连接方法为学生提供了反复练习核心概念（尤其是统计模型、分布和随机性的概念）、关键表示（R 编程语言和计算技术，如模拟和引导）与统计学家面临的现实世界情况之间的联系的机会他们探索变异、模型变异，并评估和比较统计模型。我们通过交互式在线教科书 (CourseKata.org) 中实施的课程提供导览，然后提供一些证据证明完成课程的学生能够将他们所学的知识转移到新统计技术的学习中。