Springer Linking Competence to Opportunities to Learn: Models of Competence and Data Mining: 17 (Innovations in Science Education and Technology, 17)

This book applies data mining methodology to the issue of standardizing achievement in science education and develops frameworks of competence in the ‘Opportunity-to-learn’ model of science education. It is aimed primarily at science education researchers. From the Back Cover The current world-wide movement toward standards-based science education is based on a belief that every student, no matter how different he/she is, can and should reach a prescribed level of competence. Yet there are differences in circumstances between students that lie beyond their control, such as classroom, school and family resources and practices. Thus it is more important than ever to identify the particular resources and practices that significantly predict students’ levels of achievement so that strategies can be developed to help students reach competence. This book applies data mining methodology to the issue of standardizing achievement in science education and develops frameworks of competence in the ‘Opportunity-to-learn’ (OTL) model of science education. It is aimed primarily at science education researchers, but can also be used as a reference by national and state education agencies who are required to make decisions about science curriculum standards and resource allocation. School district personnel will also find it useful in teacher professional development. Opportunity-to-learn (OTL) refers to the entitlement of every student to receive the necessary classroom, school and family resources and practices to reach the expected competence. This book quantifies and stystematizes OTL by developing models showing how the circumstances of classroom, school and family relate to students’ achievement. Liu has also applied data mining techniques to these models. In addition, the text analyzes policy as well as pedagogical implications for standards-based science education reform.