John D. Foshay

Updating Knowledge and Skills of Practitioners in Rural Areas: A Web-based Model

Technology-mediated distance education models have had a major impact on preservice and inservice preparation of special education and related services personnel over the last several decades. The increasing availability of Internet access and the multimedia capabilities of the World Wide Web have combined to spur the growth of online programs as a medium for both initial certification and continuing activities. This study reports the development, implementation, and evaluation of four courses designed as professional development activities for practicing personnel working in early intervention, early childhood special education, elementary and secondary special education, and adult disability services in rural areas of West Virginia and the surrounding Appalachian region. The article explains the process used to create learning materials and technology formats for presentation of content and interaction with learners, outlines the steps in developing and implementing the courses, and presents the results of evaluation activities conducted to assess learning outcomes and participant perceptions of the online learning experience. The findings of this study suggest that Web-based instruction is a viable mode for delivering staff development. Participants demonstrated the acquisition of new knowledge and skills, expressed satisfaction with most aspects of online teaching and learning, and reported specific applications of the information in their own classroom and intervention program settings.

Book and Software Review: Rethinking Education in the Age of Technology: The Digital Revolution and Schooling in AmericaRethinking Education in the Age of Technology: The Digital Revolution and Schooling in America, by CollinsAllan & HalvesonRichard, New York: Teachers College Press, 2009. 176 pp.,

A number of recent books have been concerned with the techno-evangelism of the so-called “digital education revolution,” principally in relation to “Web 2.0 tools” and the “digital literacy” skills required by today’s learners (Lankshear & Knobel, 2009; Wankel & Kinglsey, 2009). The title of this book suggested a similarly exciting volume of collective papers all referenced and relevant to a developing social and technologically enabled community of Twenty-First Century learners. The introduction, albeit brief, by John Seely Brown Rethinking Education in the Age of Technology: The Digital Revolution and Schooling in America

54.00 cloth,

Overview of Focus for this BookA John Wiley web page states that “Estimation techniques that account systematically fornonresponse and at the same time succeed in delivering acceptable accuracy are muchneeded. Estimation in Surveys with Nonresponse provides an overview of thesetechniques:::” From my reading of this book, it does at least mention a variety oftechniques, but focuses on the calibration approach to the reduction of nonresponse biasthat may be found in a previous issue of this journal (Lundstro¨m and Sa¨rndal 1999).The primary approach espoused in this book is as follows: (1) start by imputing for itemnonresponse, perhaps by regression or a “donor-based” method, to fill out the response set,r, a subset of the sample, s; and then (2) adjust weights to cover both the unit nonresponseand that part of the population not sampled, calibrated in accordance with auxiliary data.The auxiliary data are very important for the methodology used by the authors, as is astratification approach. Statisticians are familiar with the use of auxiliary data andstratification for improving estimation when there is full response to a sample, but theauthors deal with how to make these techniques work to reduce nonresponse bias as well.They seem to generally assume a sample is taken, but the techniques should be applicableto adjust for nonresponse in a census as well. The authors consider that they are addressinga common situation confronted by national statistical agencies: large sample surveys withconsiderable nonresponse. They implicitly assume that reliable data may be obtained forboth relevant auxiliary data and study variable data in all categories under a design-basedsample. (This I have found problematic with regard to “small” establishments in theelectric power industry, and I would think this would be true elsewhere as well. Low dataquality can be a problem. Household surveys and establishment surveys can have differentproblems.)For auxiliary data and stratification to help in reducing nonresponse bias, such dataand groupings must be related to response rates. While I was reading this book, theability to do this seemed like an assumption that might bear out well in some cases butnot in many. After all, if we form what we think are “response homogeneity groups”