Introduction

This site will contain a structured set of learning modules, each designed to reinforce a particular aspect of quantitative reasoning. These modules are intended to be sufficiently general to be used across a broad range of courses and departments, with an emphasis on active learning. The modules will introduce each topic with the presentation of conceptual material, which we will author or collect from (link to) existing sites. Each module will afford ample opportunities for student self-assessment, and will have an assessment mechanism by which we can measure the satisfaction with and effectiveness of the material. The top level page will also include an assessment mechanism to measure site navigation and overall utility.

The site will be designed to accommodate additional modules as they are created. The unifying theme is quantitative literacy, which will be the only rubric tying the modules together. This theme of broad interest; we expect to assemble a collection of modules that will be of use to students across the social and natural sciences, including mathematics. The modules will have a common look and feel so that they adhere together into a single site. The design of the site will facilitate the common interface and built-in assessment.

Initial Set of Modules

The initial site design will include a top level page and an initial set of at least seven learning modules. The initial set of modules will include:

1) Properties of a Line [Description]

2) Calculating Line of Best Fit [Description]

3) Contingency Tables. This module will discuss two-way percentage tables. Students will practice assessing the statistical significance of the crosstabulation. Discussion of conditional probability as a measure of association will include an interactive exercise with a graphical component.

4) Coin-Toss Experiment. The fundamental concepts of population, sample, and sampling distribution can be effectively illustrated using the "coin toss" experiment. Based on the student's intuitive grasp of the probability of a given outcome of a single coin-toss, this module will help the student construct and understand more complex sample spaces, and model the outcomes of binomial trials.

5) Hypothesis Testing. Hypothesis testing is one of the most fundamental yet least-well understood techniques in introductory statistics. This module will enable the user to: a, recognize the basic features common to all hypothesis tests; b, read critically a "word problem," identifying and categorizing the information presented; and c, perform the calculations needed to determine the outcome of a statistical test. The module will include built-in assessment tools.

6) Curve Fitting [Description]

7) Levels of Measurement. Introduction to the concept of quantitative measurement. Properties of variables will be discussed and compared across nominal, ordinal, interval and ratio measures. The module will explain why you can recode down the hierarchy of levels of measurement, but not up the hierarchy. The practice activity will involve identifying the level of measurement for various physical, social and demographic variables.

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