Quality Research In Literacy And Science Education

Statistical models attempt to describe and quantify relationships between variables. In the models presented in this chapter, there is a response variable (sometimes called dependent variable) and at least one predictor variable (sometimes called independent or explanatory variable). When investigating a possible cause-and-effect type of relationship, the response variable is the putative effect and the predictors are the hypothesized causes. Typically, there is a main predictor variable of interest; other predictors in the model are called covariates. Unknown covariates or other independent variables not controlled in an experiment or analysis can affect the dependent or outcome variable and mislead the conclusions made from the inquiry (Bock, Velleman, & De Veaux, 2009). A p value (p) measures the statistical significance of the observed relationship; given the model, p is the probability that a relationship is seen by mere chance. The smaller the p value, the more confident we can be that the pattern seen in the data 2 is not random. In the type of models examined here, the R measures the prop- tion of the variation in the response variable that is explained by the predictors 2 specified in the model; if R is close to 1, then almost all the variation in the response variable has been explained. This measure is also known as the multiple correlation coefficient. Statistical studies can be grouped into two types: experimental and observational.

In August 2003 over 400 researchers in the field of science education from all over the world met at the 4th ESERA conference in Noordwijkerhout, The Netherlands. During the conference 300 papers about actual issues in the field, such as the learning of scientific concepts and skills, scientific literacy, informal science learning, science teacher education, modeling in science education were presented. The book contains 40 of the most outstanding papers presented during the conference. These papers reflect the quality and variety of the conference and represent the state of the art in the field of research in science education.

This book highlights recent developments in literacy research in science teaching and learning from countries such as Australia, Brazil, China, Finland, Germany, Hong Kong, New Zealand, Norway, Singapore, Spain, South Africa, Sweden, Taiwan, and the United States. It includes multiple topics and perspectives on the role of literacy in enhancing science teaching and learning, such as the struggles faced by students in science literacy learning, case studies and evaluations of classroom-based interventions, and the challenges encountered in the science classrooms. It offers a critical and comprehensive investigation on numerous emerging themes in the area of literacy and science education, including disciplinary literacy, scientific literacy, classroom discourse, multimodality, language and representations of science, and content and language integrated learning (CLIL). The diversity of views and research contexts in this volume presents a useful introductory handbook for academics, researchers, and graduate students working in this specialized niche area. With a wealth of instructional ideas and innovations, it is also highly relevant for teachers and teacher educators seeking to improve science teaching and learning through the use of literacy.

Statistical models attempt to describe and quantify relationships between variables. In the models presented in this chapter, there is a response variable (sometimes called dependent variable) and at least one predictor variable (sometimes called independent or explanatory variable). When investigating a possible cause-and-effect type of relationship, the response variable is the putative effect and the predictors are the hypothesized causes. Typically, there is a main predictor variable of interest; other predictors in the model are called covariates. Unknown covariates or other independent variables not controlled in an experiment or analysis can affect the dependent or outcome variable and mislead the conclusions made from the inquiry (Bock, Velleman, & De Veaux, 2009). A p value (p) measures the statistical significance of the observed relationship; given the model, p is the probability that a relationship is seen by mere chance. The smaller the p value, the more confident we can be that the pattern seen in the data 2 is not random. In the type of models examined here, the R measures the prop- tion of the variation in the response variable that is explained by the predictors 2 specified in the model; if R is close to 1, then almost all the variation in the response variable has been explained. This measure is also known as the multiple correlation coefficient. Statistical studies can be grouped into two types: experimental and observational.

As more schools begin to implement the National Science Education Standards, adults who care about the quality of K-12 science education in their communities may want to help their local schools make the transition. This booklet provides guidance to parents and others, explains why high-quality science education is important for all children and young adults, and shows how the quality of school science programs can be measured. Center for Science, Mathematics, and Engineering Education Staff; 1998, 32 pages, 8.5 x 11, single copy, $10.00; 2-9 copies, $7.00 each; 10 or more copies, $4.50 each (no other discounts apply).

In August 2005, over 500 researchers from the field of science education met at the 5th European Science Education Research Association conference. Two of the main topics at this conference were: the decrease in the number of students interested in school science and concern about the worldwide outcomes of studies on students’ scientific literacy. This volume includes edited versions of 37 outstanding papers presented, including the lectures of the keynote speakers.

Science in secondary schools has tended to be viewed mainly as a 'practical subject', and language and literacy in science education have been neglected. But learning the language of science is a major part of science education: every science lesson is a language lesson, and language is a major barrier to most school students in learning science. This accessible book explores the main difficulties in the language of science and examines practical ways to aid students in retaining, understanding, reading, speaking and writing scientific language. Jerry Wellington and Jonathan Osborne draw together and synthesize current good practice, thinking and research in this field. They use many practical examples, illustrations and tried-and-tested materials to exemplify principles and to provide guidelines in developing language and literacy in the learning of science. They also consider the impact that the growing use of information and communications technology has had, and will have, on writing, reading and information handling in science lessons. The authors argue that paying more attention to language in science classrooms is one of the most important acts in improving the quality of science education. This is a significant and very readable book for all student and practising secondary school science teachers, for science advisers and school mentors.

Introducing the National Science Education Standards is a booklet that provides an overview and background of the vision and principles of the National Science Education Standards. Each of the six types of standards is described: content, teaching, assessment, professional development, program, and system. Designed for a general audience, Introducing the National Science Education Standards clarifies what the Standards are and responds to typical questions about them. It helps readers determine how the Standards could be useful to them.