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Education & Language - Science Education | Linking Competence to Opportunities to Learn - Models of Competence and Data Mining

Linking Competence to Opportunities to Learn

Models of Competence and Data Mining

Liu, Xiufeng

2009, VIII, 140 p.

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  • Links opportunities to learn to student competence status
  • Introduces a powerful new methodology – data mining, including tutorials
  • Contains easy-to-interpret graphical models
  • Expands the concept of equity to include opportunity-to-learn
  • Promotes a collaborative partnership among teachers, schools, and parents in student learning

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.

Content Level » Research

Keywords » algorithms - competence - curriculum standards - data mining - equity - learning - opportunity to learn - science education

Related subjects » Education & Language - Learning & Instruction - Science Education

Table of contents 

Preface Introduction Equity and Excellence in Standard-based Education Chapter 1 Competence and Opportunity-To-Learn Chapter 2 Models of Competence and Data Mining Chapter 3 Models of Competence and Opportunities to Learn in the Classroom Chapter 4 Models of Competence and Opportunities to Learn at Home Chapter 5 Models of Competence and Opportunities to Learn in Schools Chapter 6 Pedagogical and Policy Implications Appendix A Variables Related to Teaching Practices Measured in 1996 Grades 4 and 8 NAEP Science Appendix B Variables Related to Family Background and Home Environment Measured in 1996 Grades 4 and 8 NAEP Science Appendix C Variables Related to School Context Measured in 1996 Grades 4 and 8 NAEP Science Appendix D Accuracy Measures of Competence Models Appendix E Tutorial on the Weka Machine Learning Workbench Appendix F Machine Learning Algorithms Implemented in Weka

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