Citation for published version (APA): Bolhuis, E. D. (2017). How teacher educators learn to use data in a data team

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1 UvA-DARE (Digital Academic Repository) How teacher educators learn to use data in a data team Bolhuis, E.D. Link to publication Citation for published version (APA): Bolhuis, E. D. (2017). How teacher educators learn to use data in a data team General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam ( Download date: 20 Jan 2018

2 How Teacher Educators Learn to Use Data in a Data Team Erik Bolhuis

3 How Teacher Educators Learn to Use Data in a Data Team ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus prof. dr. ir. K. I. J. Maex, ten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel op dinsdag 21 november 2017, te uur door Egbert Dirk Bolhuis geboren te Groningen

4 Promotiecommissie: Promotor: Prof. Dr. J.M. Voogt Universiteit van Amsterdam Co-promotor: Dr. K. Schildkamp Universiteit Twente Overige leden: Prof. dr. M.L.L. Volman Universiteit van Amsterdam Prof. dr. C.A.M. van Boxtel Universiteit van Amsterdam Dr. W. Schenke Dr. J. Vanhoof Prof. dr. K. van Veen Prof. dr. S.E. McKenney Universiteit van Amsterdam Universiteit Antwerpen Universiteit Groningen Universiteit Twente Faculteit: Faculteit der Maatschappij- en Gedragswetenschappen

5 This dissertation has been approved by the promotor and co-promotor: Promotor: Co-promotor: Prof. dr. J.M. Voogt Dr. K. Schildkamp The research reported here was carried out at the University of Amsterdam, in cooperation with the University of Twente and Windesheim University of Applied Sciences. Bolhuis, E.D. Title: How Teacher Educators Learn to Use Data in a Data Team Thesis University of Amsterdam, Amsterdam, The Netherlands Copyright 2017 E.D. Bolhuis ISBN: Cover & Lay-ou by: Iris Bolhuis Printed by Ipskamp Printing, The Netherlands

6 Table of Contents Table of Contents 4 List of tables and figures Theoretical Framework The Context Research Questions Reading Guide for the Dissertation Teacher Educators Data Use Theoretical Framework Method Results Conclusion and Discussion Improving Teacher Education in the Netherlands: Data Team as Learning Team? Method Results Conclusions Data-Based Decision-Making in Teams: Enablers and Barriers Conceptual Framework Method Results Conclusion, Discussion, and Implications A Case Study of a Data Team Intervention for Teacher Educators: The Development of Data Use, Data Skills, and Attitudes Theoretical Framework Method Results

7 5.5 Conclusion Summary and Discussion Summary and Outcomes of the Studies Reflection on the Study Recommendations References 139 Appendix 155 Dutch Summary / Nederlandse samenvatting 165 Publications Related tot This Study 181 Papers in this dissertation and contribution of co-authors 183 Acknowledgements / Dankwoord 185 5

8 List of Tables and Figures Tables Table 1.1 Table 1.2 Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 2.5 Table 2.6 Table 2.7 Table 2.8 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 4.5 Table 4.6 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Factors influencing data use in education (based on Hoogland et al., ) The case-study, based on the hypotheses, the conclusions and the 21 implemented improvement measures Different ways to use data in education, with an example, the 31 rationale behind data use, and different kinds of data Factors impacting data use in education 33 The constructs used and codes per sub-question 36 Questionnaire Data Use 37 Reliability of the items of the survey regarding data use 39 Data use in the curriculum of the teacher education college 40 Descriptive statistics for data use at teacher education colleges 43 Results from the regression analysis 43 Data team members 58 Overview of the data team meetings: meeting number, (number of) 59 members present, and the data team activity during the meeting Overview of instruments in relation to the sub-questions 59 Overview of the themes, codes, source, and code descriptions 60 The extent of depth (percentages) per meeting (M1 11) 65 Factors regarding 1) data and data information systems, 2) user, and 81 3) the organisation that impact depth of inquiry The case study based on the data team's hypotheses, conclusions, 83 and improvement measures Data team members 83 Code book used for coding 86 Comparison of the meetings with some depth and partial depth, 90 with regard to the factors that influence the depth of inquiry Factors relating to depth of inquiry with newly discovered factors in 93 this study printed in bold The hypotheses, the data used, data analysis, the conclusions 106 during the data team meetings, and the improvement measures taken Data team members 106 Instruments associated with sub-questions 107 The constructs and codes used per sub-question 107 6

9 Table 5.5 Table 5.6 Table 5.7 Table 5.8 Table 5.9 Table 5.10 Table 5.11 Table 5.12 Table 5.13 Table 5.14 Table 5.15 Table 5.16 Instruments related to the research questions and the constructs 108 with sample questions Knowledge Test: Data Literacy 110 Agatha s scores on the survey (Data Skills, Attitudes, and Data Use) 111 and the Knowledge Test (data literacy) Agatha s scores on the Knowledge Test (data literacy) 111 Ann s scores on the survey (Data Skills, Attitudes, and Data Use) and 113 the Knowledge Test (data literacy) Ann s scores on the Knowledge Test (data literacy) 113 George s scores on the survey (Data Skills, Attitudes, and Data Use) 114 and the Knowledge Test (data literacy) George s scores on the Knowledge Test (data literacy) 114 Hedy s scores on the survey (Data Skills, Attitudes, and Data Use) 115 and the Knowledge Test (data literacy) Hedy s scores on the Knowledge Test (data literacy) 116 Reese s scores on the survey (Data Skills, Attitudes, and Data Use) 117 and the Knowledge Test (data literacy) Reese s scores on the Knowledge Test (data literacy) 117 Figures Figure 1.1 Figure 3.1 Figure 3.2 Figure 4.1 The activity cycle of the data team (Schildkamp & Ehren, 2013, 56) The overall mean depth per meeting The percentage of expert and coach interventions per meeting The calculated mean depth scores per meeting

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11 Science and art have in common intense seeing, the wide-eyed observing that generates multiple information. It is about how seeing turns into showing, how empirical observations turn into explanations and evidence - Edward Tufte, 2006, 9.