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This study investigated how middle school students' comprehension was impacted by reading social studies texts online with a pop-up dictionary function for every word in the text. A quantitative counterbalance design was used to determine how 129 middle school students' reading comprehension test scores for the pop-up dictionary reading differed from test scores for reading hard-copy texts or an online text without the dictionary. The pop-up dictionary reading was shown to be a statistically effective method for improving student test scores. The results suggest pop-up dictionaries may provide a helpful intervention for increasing middle-level learners' reading comprehension.

Capture-Recapture models estimate the unknown sizes of animal populations. For closed populations, with constant size N during the study, eight standard models exist for estimating N. These models allow for variation in animal capture probabilities due to time effects, heterogeneity among animals, and behavioral effects after the first capture. We present Bayesian versions of these eight models. Through simulation, we compare performance of estimation of N under each model. Each model is fit to data sets generated under each of the eight model assumptions, allowing assessment of model robustness. In our simulation conditions, the most robust model in estimating N is found to be the model with behavioral and time effects. Finally, we illustrate our methods by applying them to a population of cottontail rabbits.

Capture-Recapture models estimate unknown population sizes. Eight standard closed population models exist, allowing for time, behavioral, and heterogeneity effects. Bayesian versions of these models are presented and use of Akaike's Information Criterion (AIC) and the Deviance Information Criterion (DIC) are explored as model selection tools, through simulation and real dataset analysis.

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