Volume 2, Issue 1, 2008
For the past 15 years, pre-university students in many countries including the United States have encountered data analysis and probability as separate, mostly independent strands. Classroom-based research suggests, however, that some of the difficulties students have in learning basic skills in Exploratory Data Analysis stem from a lack of rudimentary ideas in probability. We describe a recent project that is developing materials to support middle-school students in coming to see the “data in chance” and the “chance in data.” Instruction focuses on four main ideas: model fit, distribution, signal-noise, and the Law of Large Numbers. Central to our approach is a new modeling and simulation capability that we are building into a future version of the data-analysis software TinkerPlots. We describe three classroom-tested probability investigations that employ an iterative model-fit process in which students evaluate successive theories by collecting and analyzing data. As distribution features become a focal point of students’ explorations, signal and noise components of data become visible as variation around an “expected” distribution in repeated samples. An important part of students’ learning experience, and one enhanced through visual aspects of TinkerPlots, is becoming able to see things in data they were previously unable to see.
The Iterative Evaluation Model for Improving Online Educational Resources (IEM) was developed to provide a valid evaluation model to be used to improve online resources, to make them more effective and have a greater positive impact on student learning. The model focuses on the iterative evaluation of four components: (a) evaluation planning, (b) web design and content, (c) use of the educational resource, and (d) educational impact. This paper describes the IEM which was developed as part of the NSF-funded ARTIST (Assessment Resource Tools for Improving Statistical Thinking) project and used to evaluate the online resources developed by this project. The ARTIST evaluation is described in order to illustrate how the IEM may be used.
This paper describes two innovative online introductory statistics courses that utilize technology to create unique interactive learning environments. In these courses, technology is used to enable students to collaborate and learn from each other, in addition to learning from required course materials and the instructor. Technology is also introduced into the courses as a way to better illustrate important statistical concepts and provide students with tools to describe and analyze data. In this paper, special attention is paid to the way in which the GAISE recommendations have been implemented in one key component of the online courses: small-group discussion. Evaluative data gathered from students is used to describe how students perceive the discussion component of the courses, as well as how desired learning outcomes are being achieved. The paper concludes with a discussion of lessons learned from teaching an online statistics course, and implications for future development of online staiststics courses.