Curiosity, interest and engagement in technology-pervasive learning environments: a new research agenda

This paper identifies the need for developing new ways to study curiosity in the context of today’s pervasive technologies and unprecedented information access. Curiosity is defined in this paper in a way which incorporates the concomitant constructs of interest and engagement. A theoretical model for curiosity, interest and engagement in new media technology-pervasive learning environments is advanced, taking into consideration personal, situational and contextual factors as influencing variables. While the path associated with curiosity, interest, and engagement during learning and research has remained essentially the same, how individuals tackle research and information-seeking tasks and factors which sustain such efforts have changed. Learning modalities for promoting this theoretical model are discussed leading to a series of recommendations for future research. This article offers a multi-lens perspective on curiosity and suggests a multi-method research agenda for validating such a perspective.     

Human cancer genetics

Cancer is a genetic disorder.Although heredi-tary cancers account for only a small fraction of all tumors,most cancers are caused by a variable mix of heredity and environment that leads to accumulations of genetic alterations and then uncontrolled cell pro-liferations(Kops et al.,2005).In addition,genetic alteration is one of the a few most important bio-logical factors that determine the diagnosis and prognosis of different cancers and dictate the treat-ment strategies for cancer patients.Fu…     

Exploratory analyses to improve model fit: Errors due to misspecification and a strategy to reduce their occurrence

Two Lagrange multiplier (LM) methods may be used in specification searches for adding parameters to models: one based on univariate LM tests and respecification of the model (LM‐respecified method) and the other based on a partitioning of multivariate LM tests (LM‐incremental method). These methods may result in extraneous parameters being included in models due to either sampling error or the model being misspecified. A 2‐stage specification search may be used to reduce errors due to misspecification. In the 1st stage, parameters are added to models based on LM tests to maximize fit. Second, parameters added in the 1st stage are deleted if they are no longer necessary to maintain model fit. Illustrations are presented to demonstrate that errors due to misspecification occur with the LM‐respecified method and are even more likely with the LM‐incremental approach. These illustrations also show how the deletion stage can help eliminate some of these errors.     

The Future of Teacher Education: Framing the questions that matter

As we enter the new century, there are multiple debates about what teachers need to know, who should provide education for teachers, how teachers should be certified and licensed, and what role (if any) university-based teacher preparation should play in school improvement. The author argues that the future of teacher education in the United States depends to a great extent on the ways three fundamental questions are framed: the knowledge question, the learning question, and the outcomes question. To a great extent, the ways these questions are framed and their larger agendas for the purposes and consequences of schooling will determine the future of the field.