Using web-based tools for teaching statistical concepts and experimentation skills
This paper describes a number of generally accessible web-based tools the authors developed over the last couple of years and used in several statistics courses, ranging from the introductory statistics course to a specialized design of experiments course. The first set of tools (VESTAC) illustrates statistical concepts, the second one (VIRTEX) consists of virtual experiments. We discuss some of our experiences with regard to the development and maintenance of these tools and their use in ex cathedra teaching sessions, in guided practice sessions and in student projects. Finally we discuss a third type of tools: distance experiments.
Learning statistical inference through computer-supported simulation and data analysis
This dissertation explored the effects of two different interventions on the learning of statistics. Each intervention corresponded to a different conception of statistical learning and used a particular type of computer-tool. One intervention used data analysis tools and focused on authentic situations of statistical activity. The other intervention used simulations and focused on formal aspects of probability. Data Analysis (data) and Probability (chance) are the constituent parts of statistical inference and the two lens from which is possible to present this topic. In this study, both perspectives were compared in their effectiveness to teach ANOVA, a central topic in inferential statistics. The results of this study showed that the intervention that used simulations improved students’ knowledge about probability, sampling and sample size effects. Protocol analysis of students’ answers indicated that the gains in probability knowledge did not alter the way students explained group differences. The intervention that used data analysis tools showed no significant effects on students’ data analysis knowledge. Studying the evolution of a sub sample of students suggested that data analysis knowledge develops over periods of time longer than those of this study. Additionally, protocol analysis of students solving statistical questions showed that students use simple decision rules to evaluate sampling and data analysis problems. These rules allowed students coordinating simple descriptions of the problem’s elements with conclusions about significance and sampling effects.
Optimal night temperatures for greenhouse seedlings
Abstract A method is described which can be used to estimate the most economical night-time greenhouse temperature for a particular crop. The method requires growth data, engineering data and economic data to produce a solution. Most critical are the growth data, which probably limit the usefulness of the method at the present time. An example is presented to illustrate the method. Growth-chamber data for tomato seedlings are used to obtain the sensitivity of that crop to deviations from the physiologically optimal night temperature. The available data seem to indicate that a 12 K deviation from that optimum results in a yield decrease of 20%. The recommended night temperature in the example is about half-way between the physiological optimum and the outside temperature.