Linking Competence to Opportunities to Learn: Models of Competence and Data Mining PDF
Preview Linking Competence to Opportunities to Learn: Models of Competence and Data Mining
INNOVATIONS IN SCIENCE EDUCATION AND TECHNOLOGY 17 Xiufeng Liu Linking Competence to Opportunities to Learn Models of Competence and Data Mining 123 Linking Competence to Opportunities to Learn INNOVATIONS IN SCIENCE EDUCATION AND TECHNOLOGY Volume 17 Series Editor Cohen, Karen C. Weston, MA, USA About this Series As technology rapidly matures and impacts on our ability to understand science as well as on the process of science education, this series focuses on in-depth treatment of topics related to our common goal: global improvement in science education. Each research-based book is written by and for researchers, faculty, teachers, students, and educational technologists. Diverse in content and scope, they reflect the increasingly interdisciplinary and multidisciplinary approaches required to effect change and i mprovement in teaching, policy, and practice and provide an understanding of the use and role of the technologies in bringing benefit globally to all. For other titles published in this series, go to www.springer.com/series/6150 Xiufeng Liu Linking Competence to Opportunities to Learn Models of Competence and Data Mining Xiufeng Liu Graduate School of Education State University of New York at Buffalo Buffalo, NY 14260-1000 USA ISBN 978-1-4020-9910-6 e-ISBN 978-1-4020-9911-3 DOI 10.1007/978-1-4020-9911-3 Library of Congress Control Number: 2009926489 © Springer Science + Business Media B.V. 2009 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Printed on acid-free paper springer.com Preface For many people, a high standard for student learning is desirable. This is what underlies current standard-based science education reforms around the world. As someone who was born and brought up in a less-privileged home and educated in a resource-limited school environment in a developing country, I always had to study hard to meet various standards from elementary to high school to univer- sity. My first book in English published over 10 years ago (Liu, X. [1996]. Mathematics and Science Curriculum Change in the People’s Republic of China. Lewiston, NY: The Edwin Mellen Press) provided me an opportunity to examine standards (i.e., Chinese national science teaching syllabi) from a historical and political point of view. I argued that standards are developed for particular politi- cal agendas in order to maintain the privileged position of certain groups (i.e., urban residents) in a society at expenses of others (i.e., rural residents). Thus, underneath standards is systematic discrimination and injustice. Since then, I have had opportunities to study the issue of standards in much more breadth and depth. This book, Linking Competence to Opportunities to Learn: Models of Competence and data mining, provides me an opportunity to examine standards from a different perspective: opportunity to learn. Opportunity to learn (OTL) refers to the entitlement of every student to receive necessary classroom, school, and family resources and practices to reach the required learning standard or competence. Although the concept of OTL has been around for over three dec- ades, how specific variables of OTL pertaining to science teachers’ teaching practices in the classroom, student family background and home environment, and school contexts may predict the students’ competence status is still not well- known. This book aims at filling this gap in the literature. It has two objectives: (a) developing models of competence in terms of opportunity to learn, and (b) introducing a new approach called data mining for developing models of compe- tence. Each model of competence presents a theory on how specific OTL varia- bles and their interactions are associated with a different status of successfully or unsuccessfully reaching competence. Underlying this current book is my continu- ous belief that learning standards are inherently unfair and high learning stand- ards should be based on equal opportunities for all to learn. It is only fair for a just society to expect this! It is my hope that this book will contribute to theories related to equity in science education. It is also my hope that this book will v vi Preface inform science teaching in the classroom and policy-making at the state and national levels related to standard development and resources allocation. This book is primarily for science education researchers including graduate students who are interested in science curriculum and instructional reforms. For example, it may be used as a main textbook for a graduate (i.e. master’s and doctoral) level course in science education related to science curriculum. Such a course may carry such titles as Seminar on Science Curriculum, Science Education Reform, Research in Science Curriculum, Science Curriculum Theory and Practice, and Current Approaches to Science Curriculum, to name a few. This book may also be used as a reference by national and state education agencies for making deci- sions related to science curriculum standards and resources allocation, and by school district science curriculum, instruction and assessment specialists to conduct teacher professional development. This book would not have come into being without support from many people. First, I thank my family (wife Lily and children Iris and Murton) for their never- fading love and support. I thank Dr. Miguel Ruiz, formerly of University at Buffalo and currently University of Northern Texas, for introducing me to data mining. I thank Dr. Karen Cohen, editor for the Springer book series Innovations in Science Education and Technology, for inviting me to develop a book proposal and for her ongoing support during the development of this book. I thank Mr. Harmen van Paradijs, acquisitions editor at the Springer, for coordinating the review process for this book and recommending to the Springer board for publishing this book. State University of New York at Buffalo Xiufeng Liu, Ph.D. September 2008 Contents Introduction: Equity and Excellence in Standard-Based Education ........................................................................... 1 1 Competence and Opportunity to Learn ................................................. 5 Measurement ............................................................................................... 5 Student Population ....................................................................................... 7 Content ........................................................................................................ 7 Judgment ...................................................................................................... 8 2 Models of Competence and Data Mining ............................................... 13 3 Models of Competence and Opportunities to Learn in the Classroom ....................................................................... 19 Grade 4 Competence Model ........................................................................ 24 Grade 8 Competence Model ........................................................................ 33 4 Models of Competence and Opportunities to Learn at Home ............. 43 Grade 4 Competence Model ........................................................................ 47 Grade 8 Competence Model ........................................................................ 57 5 Models of Competence and Opportunities to Learn in Schools .......... 65 Grade 4 Competence Model ........................................................................ 68 Grade 8 Competence Model ........................................................................ 75 6 Pedagogical and Policy Implications ...................................................... 83 Pedagogical Implications ............................................................................. 84 Policy Implications ...................................................................................... 86 Conclusion ................................................................................................... 88 References ....................................................................................................... 89 vii viii Contents Appendix A Variables Related to Teaching Practices Measured in 1996 for Grades 4 and 8 NAEP Science ........ 95 Appendix B Variables Related to Family Background and Home Environment Measured in 1996 for Grades 4 and 8 NAEP Science ...................................................................... 101 Appendix C Variables Related to School Context Measured in 1996 for Grades 4 and 8 NAEP Science ........................... 105 Appendix D Accuracy Measures of Competence Models ........................ 113 Appendix E Tutorial on the Weka Machine Learning Workbench ........ 119 Appendix F Machine Learning Algorithms Implemented in Weka ....... 129 Author Index................................................................................................... 135 Subject Index .................................................................................................. 136 Introduction Equity and Excellence in Standard-Based Education Imaginary Student A: Developing Country Born in a remote village in a developing country, she was considered, by her c lassmates, as being “smart.” She always did well on tests of all subjects, particularly math and science. She studied hard; her parents always supported her by providing her with necessary school supplies. However, most of her secondary school teachers did not have university degrees; some of them were high school graduates themselves. She never had any hands-on experiences in her science class, not even a teacher demonstration, because there was no science laboratory; nor were there any science supplies in the school. At the end of high school, she had to compete with millions of her fellow high school graduates all over the country, including those in big cities where teacher quality and school resources were more than adequate. She ended up scoring low on the national unified university entrance examination, but nonetheless passed the minimal acceptance score for a third-tiered college majoring in agricultural science, a subject she was never interested in. Imaginary Student B: United States Born to a poor family in a large city in the United States, he lived with his mother because his parents were divorced when he was just starting kindergarten. Although his mother did not have a university degree, she always valued education and would do anything to enable her children to pursue university education. He was a good student in high school based on his grades on his report cards. Unfortunately, many of his classmates and their parents did not care about education. As a result, his study was constantly interrupted by violence in the school and community. Not all his teachers, particularly math and science teachers, were certified because certified teachers constantly left for teaching positions in suburban schools and filling the teaching vacancy proved difficult. During his high school years, he had to pass the state mandatory graduation exams. Although he passed those graduation exams, his scores were not that high. He was not able to take any Advanced Placement (AP) X. Liu, Linking Competence to Opportunities to Learn, Innovations 1 in Science Education and Technology 17, © Springer Science + Business Media B.V. 2009
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