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.

Although many schools in the Netherlands have purchased adaptive learning systems (ALSs) to reduce workload and improve differentiated instruction, the use of ALSs with teacher dashboards in the classroom does not in itself necessarily improve differentiated instruction. The question is, what skills and knowledge do teachers need to provide differentiated instruction with the help of ALSs with teacher dashboards and how does this differ from a context in which such technologies are not used? By means of a cognitive task analysis, consisting of classroom observations, interviews, and several expert meetings, teachers' skills, and knowledge necessary for providing differentiated instruction when using teacher dashboards and ALSs were disentangled. These findings were compared with the skills and knowledge needed by teachers providing differentiated instruction without these tools. Results show that teachers' dashboards can support teachers when providing differentiated instruction by providing up-to-date information about students' progress. Nevertheless, coping with the new information provided on the dashboards also requires new skills and knowledge such as interpreting information from the dashboard and understanding how the information in the ALS is calculated. Although there are some differences, the core principles of differentiated instruction-being goal-oriented, continually monitoring, adapting instruction and assignments, being ambitious, stimulating students' self-regulation-are critical for high-quality differentiated instruction regardless of the available tools in the classroom.

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 skillproviding 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.

School leaders are assumed to be important for the implementation of data-based decision making (DBDM), but little is known about changes in leadership during this implementation. Educational leadership was measured before, during, and after a two-year, school-wide DBDM intervention in 96 primary schools. Advanced analysis techniques were applied: educational leadership was classified based on multilevel latent class analysis, changes were modeled using multi-state modeling. Results indicate that leadership was stable (44%) or improved (40%) during DBDM implementation. Stability was primarily found for schools with initial high leadership for DBDM, whereas improvement was most likely in schools with lower initial leadership

Existing observation instruments to measure differentiated instruction often lack insight into the degree to which teachers’ decisions match the actual needs of their students, and neglect the importance of preparation and evaluation. This article describes the psychometric evaluation of a comprehensive instrument (Assessing Differentiation in All Phases of Teaching; ADAPT) that does not suffer from these shortcomings. To assess its quality, 41 raters used it to score videos of lessons and interviews of 86 primary school teachers. A 5-dimensional item-response model showed good fit and high internal consistency, and a decision study was conducted to determine the reliability and agreement coefficients for different numbers of raters. For the intended low-stakes use, a single rater would be enough to provide a reliable estimate of a teacher’s overall score. Finally, rater experiences showed that ADAPT has high practical value due to the comprehensive manual and detailed score descriptions and examples. The instrument can therefore not only be used for research purposes, but can also serve as a valuable resource for teachers and teacher educators in practice.