Digital Library for Earth Science Education, DLESE / Digital libraries offer a unique and unprecedented resource through which teachers can facilitate student inquiry. In the recent National Research Council publication quoted above, National Science Education Standards, emphasis on inquiry is pervasive. Yet, when it comes to textbooks and curricula as they exist today, the clear emphasis is on learning science content disconnected from experience. Although digital libraries can't change pedagogy or textbooks, they can make it possible for students to have access to scientific information and data which interests them, a fundamental requirement for authentic inquiry. Digital libraries can provide teachers with a feasible way to let students pursue their own interests within the bounds of the curriculum and without creating an enormous amount of extra work in providing students with materials to support their investigations. This article will explore the ways in which digital libraries can support inquiry learning. We are looking at the benefits of digital libraries in high schools and middle schools through our experiences with implementation of University of Michigan's Digital Library (UMDL). In particular, we will focus here on students asking their own questions, and learning through sustained inquiry. This article will address the following questions: Why is it important for students to ask their own questions and how does it contribute to inquiry based learning? How do digital libraries help make inquiry learning possible? How is UMDL supporting sustained inquiry? What is our research telling us about tools and techniques needed to make it happen?

Digital Library for Earth Science Education, DLESE / This thesis looks at a computer-mediated communication (CMC) and publishing system used to facilitate collaborative peer review of multimedia educational objects. The occurrence of electronic scholarly publishing has increased dramatically in recent years due in part to the immediacy and overall reach of the Internet and it’s ability to transmit diverse forms of electronic media. Previous studies indicate, however, that there is a perceived lack of prestige and legitimacy associated with electronic journals as well as electronically enabled peer review. This is due in part to a perceived lack of permanence associated with electronic media, a lack of familiarity with electronic media and a lack of fully developed conventions of citation. New forms of electronically based peer review have been explored that enable a collaborative review process among reviewers and authors, breaking from traditional models where communication channels are mediated through an editor. The ability of CMC to enable collaboration within geographically dispersed communities offers strong motivation for its use. This thesis develops a framework for collaborative peer review based on social capital that suggests an overall benefit for scholarly communities that incorporate collaborative forms of review. An examination is performed of collaborative peer review used in a new journal that features multimedia-rich geoscience educational objects: the Journal of Earth System Science in Education (JESSE). Technical issues surrounding the preparation of these objects for the CMC review environment are discussed and a process model for publishing is developed. A redesign of the toolkit used to prepare objects for the review environment is implemented and task-centered usability assessments are performed. The outcome of these steps suggested a potential for increased legitimacy and prestige of electronic publishing could develop out of a well-designed CMC environment and collaborative review model. It was found that scholars who participated in the peer review perceived a benefit from the collaborative process and that the process was seen as providing a separate service from traditional peer review. On the publishing end, the redesigned toolkit implementation was seen as providing greater accessibility to non-technically oriented users.

The study of uncertainty in Geographic Information Systems (GIS) and environmental models has received increasing attention in recent years, due in part to the widespread use of GIS for resource management. This study used GIS-based techniques in order to compare several different forest inventory and forest cover datasets. These datasets pertain to an area of the boreal mixedwood forest in northeastern Alberta which covers roughly 73,000 km2, and which has recently been approved for logging. The datasets include two forest inventories based on aerial photographs, and a forest cover classification based on remotely sensed satellite data. Simple logical operations were used to transform the datasets to a form suitable for comparison. Standard GIS overlay techniques were used to compare the agreement among different datasets. Visualization techniques were used to display patterns of agreement in attribute space (contingency tables), and in geographic space (maps of uncertainty). Agreement between the two forest inventories was about 50% (Percent Correctly Classified), with a Kappa value of 0.4, for a classification based on species composition. In general, much of the misclassification was between ecologically similar types, particularly between different combinations of aspen and white spruce. Comparison of the forest inventories with the classified satellite image was done using a simplified land cover classification with five categories. Agreement was about 55% (Percent Correctly Classified), with a Kappa value of 0.3. Possible sources of discrepancy among datasets include change over time, differences in spatial scale, differences in category definitions, positional inaccuracy, boundary effects and misclassification. Analyses were conducted to characterize the effect of each of these sources of disagreement. The agreement was strongly affected by the distance to boundary, indicating a boundary effect extending to more than 100 meters. Differences in spatial scale accounted for a small proportion of discrepancy. None of the other possible sources had a measurable effect on the discrepancy. It was therefore inferred that misclassification accounted for a large proportion of the discrepancy. Estimated levels of uncertainty were propagated through models including simple growth and yield tables and a more complex harvest scheduling model. It was found that uncertainty in model outputs was strongly affected by uncertainty in inventory data, uncertainty in volume yield curves, and perhaps most importantly, by a poor understanding of disturbance and forest dynamics in the region. The results of the analysis show that these uncertainties may have significant economic and ecological implications.

Digital Elevation Models have been in use for more than three decades and have become a major component of geographic information processing. The intensive use of DEMs has given rise to many accuracy investigations. The accuracy estimate is usually given in a form of a global measure such as root-mean-square error (RMSE), mostly from a producer's point of view. Seldom are the errors described in terms of their spatial distribution or how the resolution of the DEM interacts with the variability of terrain. There is a wide range of topographic variation present in different terrain surfaces. Thus, in defining the accuracy of a DEM, one needs ultimately to know the global and local characteristics of the terrain and how the resolution interacts with them. In this thesis, DEMs of various resolutions (i.e., 10 arc-minutes, 5 arc-minutes, 2 km, 1 km, and 50 m) in the study area (Prince George, British Columbia) were compared to each other and their mismatches were examined. Based on the preliminary test results, some observations were made regarding the relations among the spatial distribution of DEM errors, DEM resolution and the roughness of terrain. A hypothesis was proposed that knowledge of the landscape characteristics might provide some insights into the nature of the inherent error (or uncertainty) in a DEM. To test this statistically, the global characteristics of the study area surfaces were first examined by measures such as grain and those derived from spectral analysis, nested analysis of variance and fractal analysis of DEMs. Some important scale breaks were identified for each surface and this information on the surface global characteristics was then used to guide the selection of the moving window sizes for the extraction of the local roughness measures. The spatial variation and complexity of various study area surfaces was characterized by means of seven local geomorphometric parameters. The local measures were extracted from DEMs with different resolutions and using different moving window sizes. Then the multivariate cluster analysis was used for automated terrain classification in which relatively homogeneous terrain types at different scale levels were identified. Several different variable groups were used in the cluster analysis and the different classification results were compared to each other and interpreted in relation to each roughness measure. Finally, the correlations between the DEM errors and each of the local roughness measures were examined and the variation of DEM errors within various terrain clusters resulting from multivariate classifications were statistically evaluated. The effectiveness of using different moving window sizes for the extraction of the local measures and the appropriateness of different variable groups for terrain classification were also evaluated. The major conclusion of this study is that knowledge of topographic characteristics does provide some insights into the nature of the inherent error (or uncertainty) in a DEM and can be useful for DEM error modelling. The measures of topographic complexity are related to the observed patterns of discrepancy between DEMs of differing resolution, but there are variations from case to case. Several patterns can be identified in terms of relation between DEM errors and the roughness of terrain. First of all, the DEM errors (or elevation differences) do show certain consistent correlations with each of the various local roughness variables. With most variables, the general pattern is that the higher the roughness measure, the more points with higher absolute elevation differences (i.e., horn-shaped scatter of points indicating heteroscedasticity). Further statistical test results indicate that various DEM errors in the study area do show significant variation between different clusters resulting from terrain classifications based on different variable groups and window sizes. Cluster analysis was considered successful in grouping the areas according to their overall roughness and useful in DEM error modelling. In general, the rougher the cluster, the larger the DEM error (measured with either the standard deviation of the elevation differences or the mean of the absolute elevation differences in each cluster). However, there is still some of the total variation of various DEM errors that could not be accounted for by the cluster structure derived from multivariate classification. This could be attributed to the random errors inherent in any of the DEMs and the errors introduced in the interpolation process. Another conclusion is that the multivariate approach to the classification of topographic surfaces for DEM error modelling is not necessarily more successful than using only a single roughness measure in characterizing the overall roughness of terrain. When comparing the DEM error modelling results for surfaces with different global characteristics, the size of the moving window used in geomorphometric parameter abstraction also has certain impact on the modelling results. It shows that some understanding of the global characteristics of the surface is useful in the selection of appropriate/optimal window sizes for the extraction of local measures for DEM error modelling. Finally, directions for further research are suggested.

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Thesis (MTransportSystemEngineering)--University of South Australia, 2004.

Thesis (PhD)--University of South Australia, 2000

Thesis (PhD)--University of South Australia, 1996

Thesis (PhD)--University of South Australia, 1996