Review of Applied Spatial Data Analysis with R by R. S. Bivand, E. J. Pebesma, and V. Gomez-Rubio

Most authors struggle to pick a title that adequately conveys all of the material covered in a book. When I first saw Applied Spatial Data Analysis with R, I expected a review of spatial statistical models and their applications in packages (libraries) from the CRAN site of R. The authors’ title is not misleading, but I was very pleasantly surprised by how deep the word “applied” is here. The first half of the book essentially covers how R handles spatial data. To some statisticians this may be boring. Do you want, or need, to know the difference between S3 and S4 classes, how spatial objects in R are organized, and how various methods work on the spatial objects? A few years ago I would have said “no,” especially to the “want” part. Just let me slap my EXCEL spreadsheet into R and run some spatial functions on it. Unfortunately, the world is not so simple, and ultimately we want to minimize effort to get all of our spatial analyses accomplished. The first half of this book certainly convinced me that some extra effort in organizing my data into certain spatial class structures makes the analysis easier and less subject to mistakes. I also admit that I found it very interesting and I learned a lot.

Student and Faculty Perceptions of Technology’s Usefulness in Community College General Education Courses

Educational institutions of all levels invest large amounts of time and resources into instructional technology, with the goal of enhancing the educational effectiveness of the learning environment. The decisions made by instructors and institutions regarding the implementation of technology are guided by perceptions of usefulness held by those who are in control. The primary objective of this mixed methods study was to examine the student and faculty perceptions of technology being used in general education courses at a community college. This study builds upon and challenges the assertions of writers such as Prensky (2001a, 2001b) and Tapscott (1998) who claim that a vast difference in technology perception exists between generational groups, resulting in a diminished usefulness of technology in instruction. In this study, data were gathered through student surveys and interviews, and through faculty surveys and interviews. Analysis of the data used Kendall’s Tau test for correlation between various student and faculty variables in various groupings, and also typological analysis of the transcribed interview data. The analysis of the quantitative data revealed no relationship between age and perception of technology’s usefulness. A positive relationship was found to exist between the perception of the frequency of technology use and the perception of technology’s effectiveness, suggesting that both faculty members and students believed that the more technology is used, the more useful it is in instruction. The analysis of the qualitative data revealed that both faculty and students perceive technology to be useful, and that the most significant barriers to technology’s usefulness include faulty hardware and software systems,lack of user support, and lack of training for faculty. The results of the study suggest that the differences in perception of technology between generations that are proposed by Prensky may not exist when comparing adults from the younger generation with adults from the older generation. Further, the study suggests that institutions continue to invest in instructional technology, with a focus on high levels of support and training for faculty, and more universal availability of specific technologies, including web access, in class video, and presentation software. Adviser: Ronald Joekel