Geographic boundary analysis is a relatively new approach unfamiliar to many spatial analysts. It is best viewed as a technique for de®ning objects ± geographic boundaries ± on spatial ®elds, and for evaluating the statistical signi®cance of characteristics of those boundary objects. This is accomplished using null spatial models representative of the spatial processes expected in the absence of boundary-generating phenomena. Close ties to the object-®eld dialectic eminently suit boundary analysis to GIS data. The majority of existing spatial methods are ®eld-based in that they describe, estimate, or predict how attributes (variables de®ning the ®eld) vary through geographic space. Such methods are appropriate for ®eld representations but not object representations. As the object-®eld paradigm gains currency in geographic information science, appropriate techniques for the statistical analysis of objects are required. The methods reviewed in this paper are a promising foundation. Geographic boundary analysis is clearly a valuable addition to the spatial statistical toolbox. This paper presents the philosophy of, and motivations for geographic boundary analysis. It de®nes commonly used statistics for quantifying boundaries and their characteristics, as well as simulation procedures for evaluating their signi®cance. We review applications of these techniques, with the objective of making this promising approach accessible to the GIS-spatial analysis community. We also describe the implementation of these methods within geographic boundary analysis software: GEM.

Diffusion and osmosis are central concepts in biology, both at the cellular and organ levels. They are presented several times throughout most introductory biology textbooks (e.g., Freeman, 2002), yet both processes are often difficult for students to understand (Odom, 1995;Zuckerman, 1994;Sanger et al., 2001; and results herein). Students have deep-rooted misconceptions about how diffusion and osmosis work, especially at the molecular level. We hypothesized that this might be in part due to the inability to see and explore these processes at the molecular level. In order to investigate this, we developed new software, OsmoBeaker, which allows students to perform inquiry-based experiments at the molecular level. Here we show that these simulated laboratories do indeed teach diffusion and osmosis and help overcome some, but not all, student misconceptions.

Although evolutionary theory is considered to be a unifying foundation for biological education, misconceptions about basic evolutionary processes such as natural selection inhibit student understanding. Even after instruction, students harbor misconceptions about natural selection, suggesting that traditional teaching methods are insufficient for correcting these confusions. This has spurred an effort to develop new teaching methods and tools that effectively confront student misconceptions. In this study, we designed an interactive computer-based simulated laboratory to teach the principles of evolution through natural selection and to correct common student misconceptions about this process. We quantified undergraduate student misconceptions and understanding of natural selection before and after instruction with multiple-choice and open-response test questions and compared student performance across gender and academic levels. While our lab appeared to be effective at dispelling some common misconceptions about natural selection, we did not find evidence that it was as successful at increasing student mastery of the major principles of natural selection. Student performance varied across student academic level and question type, but students performed equally across gender. Beginner students were more likely to use misconceptions before instruction. Advanced students showed greater improvement than beginners on multiple-choice questions, while beginner students reduced their use of misconceptions in the open-response questions to a greater extent. These results suggest that misconceptions can be effectively addressed through computer-based simulated laboratories. Given the level of misconception use by beginner and advanced undergraduates and the gains in performance recorded after instruction at both academic levels, natural selection should continue to be reviewed through upper-level biology courses.

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To understand evolutionary theory, students must be able to understand and use evolutionary trees and their underlying concepts. Active, hands-on curricula relevant to macroevolution can be challenging to implement across large college-level classes where textbook learning is the norm. We evaluated two approaches to helping students learn macroevolutionary topics. Treatment 1 is a laboratory for the software program EvoBeaker designed to teach students about evolutionary trees. We tested Treatment 1 among nine college-level biology classes and administered pre/posttests to assess learning gains. We then sought to determine whether the learning gains from Treatment 1 were comparable to those derived from an alternate hands-on treatment, specifically the combination of a prerecorded lecture on DVD and paper-based activity based on Goldsmith's Great Clade Race (Treatment 2). Comparisons of pre-and posttests among participants using either Treatment 1 or 2 show large learning gains on some misconceptions and skills beyond knowledge gained from reading standard textbook entries. Both treatments performed equivalently in overall learning gains, though both had areas where they performed better or worse. Furthermore, gains among students who used Treatment 1 representing a wide range of universities suggest that outcomes are potentially applicable to a variety of "real-world" biology classes.