Schildkamp / Lai / Earl Data-based Decision Making in Education

Challenges and Opportunities
2013
ISBN: 978-94-007-4816-3
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 17, 221 Seiten

Reihe: Studies in Educational Leadership

In a context where schools are held more and more accountable for the education they provide, data-based decision making has become increasingly important. This book brings together scholars from several countries to examine data-based decision making. Data-based decision making in this book refers to making decisions based on a broad range of evidence, such as scores on students' assessments, classroom observations etc.This book supports policy-makers, people working with schools, researchers and school leaders and teachers in the use of data, by bringing together the current research conducted on data use across multiple countries into a single volume. Some of these studies are 'best practice' studies, where effective data use has led to improvements in student learning. Others provide insight into challenges in both policy and practice environments. Each of them draws on research and literature in the field.

Autoren/Hrsg.

Schildkamp, Kim

Earl, Lorna

Lai, Mei Kuin

Inhaltsverzeichnis

1;Contents;6
2;About the Authors;8
3;About the Editors;13
4;Chapter 1 Introduction;15
4.1;1.1 Introduction;15
4.2;1.2 How Will This Book Help You?;16
4.3;1.3 Organization of Chapters;18
4.4;References;21
5;Chapter 2 Data-based Decision Making: An Overview;22
5.1;2.1 Introduction;22
5.2;2.2 Broadening Our Understanding of Data;22
5.3;2.3 Why Data?;25
5.3.1;2.3.1 The Nature of Effective Teaching and School Leadership;26
5.3.2;2.3.2 Evidence of Improvements in Student Learning and Achievement;27
5.4;2.4 The Process of Using Data;28
5.5;2.5 How Data Can be Used;31
5.6;2.6 Reflection Questions;32
5.7;References;33
6;Chapter 3 Analysis and Discussion of Classroom andAchievement Data to Raise Student Achievement;35
6.1;3.1 Introduction: Context Description;36
6.2;3.2 Why Decision-Making Using Data Requires Linking Achievement Patterns to Classroom Practices;38
6.3;3.3 The Overall Intervention Model in Three Clusters;40
6.4;3.4 Gains in Achievement;41
6.5;3.5 Analysis and Discussion of Data;42
6.5.1;3.5.1 General Process of Analysing and Discussing Achievement Data;43
6.5.2;3.5.2 General Process of Analysing and Discussing Observation Data;45
6.5.3;3.5.3 Linking Student Achievement to Classroom Observations;48
6.6;3.6 Enablers and Discussion;51
6.7;3.7 Conclusions and Next Steps;55
6.8;3.8 Reflection Questions;56
6.9;References;57
7;Chapter 4 From ``Intuition''- to ``Data''-based Decision Making in Dutch Secondary Schools?;60
7.1;4.1 Introduction: Context Description;60
7.2;4.2 Two Stories of Data-based Decision Making;64
7.2.1;4.2.1 Data-based Decision Making in the Real World:School Level;64
7.2.2;4.2.2 Data-based Decision Making in the Real World:Classroom Level;65
7.2.3;4.2.3 Data-based Decision Making in a Perfect World:School Level;66
7.2.4;4.2.4 Data-based Decision Making in a Perfect World:Classroom Level;70
7.3;4.3 Supporting and Hindering Factors;72
7.4;4.4 Possible Effects and Side Effects;73
7.5;4.5 Conclusion and Discussion;74
7.6;4.6 Reflection Questions;75
7.7;References;76
8;Chapter 5 Professional Attitudes to the Use of Data in England;79
8.1;5.1 Introduction and Background;80
8.2;5.2 Research Questions and Research Base;83
8.3;5.3 Selecting and Recruiting the Participating Schools;84
8.4;5.4 Collection of Data;85
8.5;5.5 Discussion of Findings;86
8.5.1;5.5.1 Use of Pupil Attainment and Progress Data;86
8.5.2;5.5.2 Teachers' Understanding of Pupil Attainment and Progress Data and Confidence in Their Skills to Access, Utilise and Interpret Data;88
8.5.3;5.5.3 The Impact of Training and Continuing Professional Development;90
8.5.4;5.5.4 Management, Analysis and Interpretation of Pupil Attainment and Progress Data: Who Does Whatand Who Should Do What in Schools?;91
8.5.5;5.5.5 The Rationale for Collecting Pupil Attainment and Progress Data: What Teachers Perceive It To Be and What They Consider It Should Be;93
8.6;5.6 Summary and Conclusion;95
8.7;5.7 Reflection Questions;97
8.8;References;98
9;Chapter 6 Approaches to Effective Data Use: Does One Size Fit All?;100
9.1;6.1 Introduction: Context;101
9.2;6.2 Data Dissemination and Data Use: How the One Influences the Other;103
9.3;6.3 Research Design and Methodology;105
9.3.1;6.3.1 The Feedback System;105
9.3.2;6.3.2 Sample;107
9.3.3;6.3.3 Instruments and Data Collection;107
9.3.4;6.3.4 Analysis;108
9.4;6.4 Results;109
9.4.1;6.4.1 Pieter (School A/E);109
9.4.1.1;6.4.1.1 Data Path: Team Approach;109
9.4.1.2;6.4.1.2 Types of Use;111
9.4.2;6.4.2 Rajesh (School E);112
9.4.2.1;6.4.2.1 Data Path---Cascade Approach;112
9.4.2.2;6.4.2.2 Types of Use;113
9.4.3;6.4.3 Sibongile (School S);115
9.4.3.1;6.4.3.1 Data Path: Top-Down Approach;115
9.4.3.2;6.4.3.2 Types of Use;116
9.5;6.5 Discussions and Conclusions;116
9.5.1;6.5.1 Reflection Questions for Practitioners;119
9.5.2;6.5.2 Reflection Questions for Policy Makers;119
9.5.3;6.5.3 Reflection Questions for Researchers;119
9.6;References;120
10;Chapter 7 Improving Data Literacy in Schools: Lessons from the School Feedback Project;122
10.1;7.1 Introduction: Context Description;123
10.2;7.2 Conceptual Framework;125
10.2.1;7.2.1 What We Understand by the Term Data Literacy;125
10.2.2;7.2.2 Variation in Types of Support in the School Feedback Project;126
10.3;7.3 The Collection of Evaluation Data;130
10.4;7.4 Findings;131
10.4.1;7.4.1 Findings with Regard to Data Literacy: Strengths and Weaknesses;131
10.4.2;7.4.2 Findings with Regard to the Various Types of Support;132
10.4.3;7.4.3 Findings with a View to Designing a Combined Support Route;137
10.5;7.5 Conclusion;139
10.6;7.6 Reflection Questions;141
10.7;References;142
11;Chapter 8 Implementation of a Data Initiative in the NCLB Era;144
11.1;8.1 A Tale of Two Schools;144
11.2;8.2 Introduction: Accountability and the NCLB;145
11.3;8.3 No Child Left Behind: A Brief Primer;147
11.4;8.4 Cohen and Moffitt's Framework;148
11.4.1;8.4.1 Aims and Ambiguity;148
11.4.2;8.4.2 Instruments;149
11.4.3;8.4.3 Capability;150
11.4.4;8.4.4 Environment;150
11.5;8.5 Effective Data Use Under NCLB;151
11.5.1;8.5.1 Aims and Ambiguities;152
11.5.2;8.5.2 Instruments;152
11.5.3;8.5.3 Capability;153
11.5.4;8.5.4 Environment;154
11.5.4.1;8.5.4.1 Collaboration;154
11.5.4.2;8.5.4.2 Common Understandings;155
11.5.4.3;8.5.4.3 Principal Leadership;156
11.5.4.4;8.5.4.4 Professional Learning;156
11.5.4.5;8.5.4.5 Technology;157
11.5.4.6;8.5.4.6 Time;157
11.6;8.6 Conclusion;158
11.7;8.7 Reflection Questions;159
11.8;References;160
12;Chapter 9 Towards Data-Informed Decisions: From Ministry Policy to School Practice;163
12.1;9.1 Introduction;164
12.2;9.2 Ontario's Public Education Context;165
12.2.1;9.2.1 Ontario's Reform Approach and Data Use;165
12.3;9.3 Ontario's MISA/PNC Initiative;166
12.3.1;9.3.1 Electronic Data Management;171
12.3.2;9.3.2 Capacity Building;171
12.4;9.4 One PNC in Action: The Greater Toronto Area Professional Network Centre;172
12.4.1;9.4.1 Using Professional Learning Networks and PLCs to Foster Data-based Decision Making;173
12.5;9.5 One District's MISA/PNC-Related Activities;175
12.5.1;9.5.1 Refining and Extending the District Data System and Data Warehouse;175
12.5.2;9.5.2 Analyzing District Data for Trends and Relationships;176
12.5.3;9.5.3 Building Data Use Capacity in District Networks;177
12.5.4;9.5.4 A Focus on Student Learning;177
12.6;9.6 Conclusions/Lessons Learned;178
12.7;9.7 Reflection Questions;181
12.8;References;182
13;Chapter 10 Conclusions and a Data Use Framework;184
13.1;10.1 Introduction;184
13.2;10.2 Lessons Learned: A Data Use Theoretical Framework;185
13.3;10.3 Data Use;185
13.4;10.4 Enablers and Barriers to Data Use;188
13.4.1;10.4.1 School Organization and Context;188
13.4.2;10.4.2 Data and Data Systems;191
13.4.3;10.4.3 Data Users;193
13.5;10.5 Policy;194
13.6;10.6 Outcomes;195
13.7;10.7 Reflection Questions;196
13.8;References;197
14;Chapter 11 Data Use: Where to from Here?;199
14.1;11.1 Situating Data Use in Education in a Policy and Politics Context;200
14.1.1;11.1.1 Prevailing Views of Accountability and the Associated Political Stakes;202
14.1.2;11.1.2 Policies Made at Different Levels May Have Different Consequences for Data Use;203
14.1.3;11.1.3 How Does School Autonomy Affect Data Use?;204
14.1.4;11.1.4 Do the Findings of These Cases Generalize to Different Settings?;204
14.2;11.2 Where Good Knowledge Lies;205
14.2.1;11.2.1 Good Questions;205
14.2.2;11.2.2 Good Data;206
14.2.3;11.2.3 Good Thinking;208
14.3;11.3 A Cautionary Tale;209
14.4;References;210
15;Further Reading;211
15.1;Further Reading for Practitioners;211
15.2;Further Reading for Policy Makers;212
15.3;Further Reading for Researchers;212
16;Index;214