Ideabook: Libraries for Families

The IDEABOOK is a research-based framework to guide and broaden family engagement in libraries.The framework helps libraries move beyond thinking of family engagement as random, individual activities or programs, but rather as a system where library leadership, activities, and resources that are linked to goals. The framework represents a theory of change that begins with a set of elements—leadership, engagement, and support services—that build a pathway for meaningful family engagement beginning in the early childhood years and extending through young adulthood.This IDEABOOK was developed for anyone who works in a library setting—from library directors and children's and youth librarians, to volunteers and support staff—and shares many innovative ways that libraries support and guide families in children's learning and development

Moving from Data-Constrained to Data-Enabled Research: Experiences and Challenges in Collecting, Validating and Analyzing Large-Scale e-Commerce Data

Widespread e-commerce activity on the Internet has led to new opportunities to collect vast amounts of micro-level market and nonmarket data. In this paper we share our experiences in collecting, validating, storing and analyzing large Internet-based data sets in the area of online auctions, music file sharing and online retailer pricing. We demonstrate how such data can advance knowledge by facilitating sharper and more extensive tests of existing theories and by offering observational underpinnings for the development of new theories. Just as experimental economics pushed the frontiers of economic thought by enabling the testing of numerous theories of economic behavior in the environment of a controlled laboratory, we believe that observing, often over extended periods of time, real-world agents participating in market and nonmarket activity on the Internet can lead us to develop and test a variety of new theories. Internet data gathering is not controlled experimentation. We cannot randomly assign participants to treatments or determine event orderings. Internet data gathering does offer potentially large data sets with repeated observation of individual choices and action. In addition, the automated data collection holds promise for greatly reduced cost per observation. Our methods rely on technological advances in automated data collection agents. Significant challenges remain in developing appropriate sampling techniques integrating data from heterogeneous sources in a variety of formats, constructing generalizable processes and understanding legal constraints. Despite these challenges, the early evidence from those who have harvested and analyzed large amounts of e-commerce data points toward a significant leap in our ability to understand the functioning of electronic commerce.Comment: Published at http://dx.doi.org/10.1214/088342306000000231 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Engineering at San Jose State University, Summer 2008

https://scholarworks.sjsu.edu/engr_news/1006/thumbnail.jp

Information Outlook, October 2004

Volume 8, Issue 10https://scholarworks.sjsu.edu/sla_io_2004/1009/thumbnail.jp

Strategies For Covering the Uninsured: How California Policymakers Could Build on Lessons Learned at the Federal Level

Outlines possible health insurance coverage expansions in California that build on specific approaches from recent federal efforts

The genesis and development of mobile learning in Europe

In the past two decades, European researchers have conducted many significant mobile learning projects. The chapter explores how these projects have arisen and what each one has contributed, so as to show the driving forces and outcomes of European innovation in mobile learning. The authors identify context as a central construct in European researchers’ conceptualizations of mobile learning and examine theories of learning for the mobile world, based on physical, technological, conceptual, social and temporal mobility. The authors also examine the impacts of mobile learning research on educational practices and the implications for policy. Finally, they suggest future challenges for researchers, developers and policy makers in shaping the future of mobile learning

SHEER “smart” database: technical note

The SHEER database brings together a large amount of data of various types: interdisciplinary site data from seven independent episodes, research data and those for the project results dissemination process. This concerns mainly shale gas exploitation test sites, processing procedures, results of data interpretation and recommendations. The smart SHEER database harmonizes data from different fields (geophysical, geochemical, geological, technological, etc.), creates and provides access to an advanced database of case studies of environmental impact indicators associated with shale gas exploitation and exploration, which previously did not exist. A unique component of the SHEER database comes from the monitoring activity performed during the project in one active shale gas exploration and exploitation site at Wysin, Poland, which started from the pre-operational phase. The SHEER database is capable of the adoption of new data such as results of other Work Packages and has developed an over-arching structure for higher-level integration