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