Despite growing international interest in the use of data to improve education, few studies examining the effects on student achievement are yet available. In the present study, the effects of a two-year data-based decisionmaking intervention on student achievement growth were investigated. Fifty-three primary schools participated in the project, and student achievement data were collected over the two years before and two years during the intervention. Linear mixed models were used to analyze the differential effect of data use on student achievement. A positive mean intervention effect was estimated, with an average effect of approximately one extra month of schooling. Furthermore, the results suggest that the intervention especially

Providing differentiated instruction (DI) is considered an important but complex teaching skill which many teachers have not mastered and feel unprepared for. In order to design professional development activities, a thorough description of DI is required. The international literature on assessing teachers' differentiation qualities describes the use of various instruments, ranging from self-reports to observation schemes and from perceived-difficulty instruments to student questionnaires. We question whether these instruments truly capture the complexity of differentiation. In order to depict this complexity, a cognitive task analysis (CTA) of the differentiation skill was performed. The resulting differentiation skill hierarchy is presented here, together with the knowledge required for differentiation, and the factors influencing its complexity. Based on the insights of this CTA, professional development trajectories can be designed and a comprehensive assessment instrument can be developed, enabling researchers and practitioners to train, assess, and monitor teaching quality with respect to providing differentiated instruction.

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The use of data for adaptive, tailor-made education can be beneficial for students with learning difficulties. While evaluating the effects of a data-based decision-making (DBDM) intervention on student outcomes, considerable variation between intervention effects, ranging from highintervention effects to small or even negative intervention effects, across schools was found.The main purpose of this study was to investigate whether educator and school organizational characteristics are related to the effects of a DBDM intervention on student achievement growth by comparing 10 primary schools with strong intervention effects with 10 primary schools with no intervention effects on student achievement. Supportive and hindering factors were studied by means of surveys and interviews with school management teams, and by examining school reports from the project trainers. Results indicate that schools with strong intervention effects differed from schools with no intervention effects with regard to their teachers' teaching quality, staff's attitude toward DBDM, and the school data culture.

There is an increasing global emphasis on using data for decision making, with a growing body of research on interventions aimed at implementing and sustaining data-based decision making (DBDM) in schools. Yet, little is known about the school features that facilitate or hinder the implementation of DBDM. Based on a literature review, the authors identified 12 potentially critical factors for the successful implementation of DBDM. Following an intensive DBDM intervention, 16 schools' characteristics were studied and related to the success of DBDM implementation after participating in an intensive DBDM intervention for 2 subsequent academic years. Strong instructional leadership, maximum exposure to the intervention, standardization of work processes, as well as staff continuity and a strong academic coach appear to be strongly related to implementation success. The remaining school context features and the culture and structure of the schools were not associated with the success of DBDM implementation. ARTICLE HISTORY

This paper describes how an interdisciplinary design team used the Four-Component Instructional Design (4C/ID) model and its accompanying Ten Steps design approach to systematically design a professional development program for teaching differentiation skills to primary school teachers. This description illustrates how insights from a cognitive task analysis into classroom differentiation skills were combined with literature-based instructional design principles to arrive at the training blueprint for workplace-based learning. It demonstrates the decision-making processes involved in the systematic design of each of the four components: learning tasks, supportive information, procedural information, and part-task practice. While the design process was time and resource-intensive, it resulted in a detailed blueprint of a five-month professional development program that strategically combines learning activities to stimulate learning processes that are essential for developing the complex skill providing differentiated instruction in a mathematics lesson.

Data‐based decision making (DBDM) is presumed to improve student performance in elementary schools in all subjects. The majority of studies in which DBDM effects have been evaluated have focused on mathematics. A hierarchical multiple single‐subject design was used to measure effects of a 2‐year training, in which entire school teams learned how to implement and sustain DBDM, in 39 elementary schools. In a multilevel modeling approach, student achievement in mathematics and spelling was analyzed to broaden our understanding of the effects of DBDM interventions. Student achievement data covering the period from August 2010 to July 2014 were retrieved from schools’ student monitoring systems. Student performance on standardized tests was scored on a vertical ability scale per subject for Grades 1 to 6. To investigate intervention effects, linear mixed effect analysis was conducted. Findings revealed a positive intervention effect for both mathematics and spelling. Furthermore, low‐SES students and low‐SES schools benefitted most from the intervention for mathematics.

Context Collaboration within school teams is considered to be important to build the capacity school teams need to work in a data-based way. In a school characterized by a strong collaborative culture, teachers may have more access to the knowledge and skills for analyzing data, teachers have more opportunity to discuss the performance goals to be set, and they also can share effective teaching strategies to achieve those goals. Although many studies on data-based decision making (DBDM) foreground the importance of teacher collaboration, our knowledge of what such collaboration looks like and how such collaboration may change during a DBDM reform remains limited. Objective The current study uses a social network perspective to explore how collaboration in 32 elementary schools in the Netherlands takes shape in the interactions among teachers as they engage in a DBDM reform project. Research Design Schools’ social networks were examined at the start of the intervention and after having participated 1 year in the DBDM reform. Social networks regarding three DBDM topics are examined: (1) discussing student achievement; (2) discussing achievement goals; (3) and discussing instructional strategies. The density, reciprocity, and centralization of these networks were calculated, and multivariate multiple regression analysis was used to analyze changes over time. Conclusion Findings suggest that teachers’ DBDM related networks transform during the intervention, especially regarding the discussion of student achievement data: although the number of relationships remains stable, more reciprocal relationships are formed, and this network becomes less centralized around one or a few influential staff members.