This paper is a technology case study that addresses the theme of using technology in a large lecture format undergraduate introduction to statistics class to develop student conceptual understanding of inference. In the activities described, each student in the lecture performs a simulation once on a calculator and the results are collected via a personal response system (clicker). This provides not only an active learning environment, but also allows students to experience statistical concepts such as distributions or models, variability, and the Central Limit Theorem, in ways that they cannot experience without these technologies. The large class, therefore, becomes a learning asset, rather than a liability. The two activities that are described in detail are part of a set of twelve activities that were designed to improve conceptual understanding of statistical inference.

Meaningful assessments that reveal student thinking are vital to the success of addressing the GAISE recommendation: use assessments to improve and evaluate student learning. Constructed-response questions, also known as open-response or short answer questions, in which students must write an answer in their own words, have been shown to better reveal students' understanding than multiple-choice questions, but they are much more time consuming to grade for classroom use or code for research purposes. This paper describes and illustrates the use of two different software packages to analyze open-response data collected from undergraduate students’ writing. The analysis and results produced by the two packages are contrasted with each other and with the results obtained from hand coding of the same data sets. The article concludes with a discussion of the advantages and limitations of the analysis options for statistics education research.