Mei Kuin Lai

Sustained Acceleration of Achievement in Reading Comprehension: The New Zealand Experience

Schools with primarily indigenous and ethnic minorities in low socioeconomic areas have long been associated with low levels of achievement, particularly in literacy. This is true for New Zealand despite high levels of reading comprehension by international comparisons (e.g., PISA). Recent reviews of schooling improvement suggest small gains over the short term are possible with well-designed interventions, but for children in the middle primary school years, the criterion against which effective interventions need to be judged is sustained and systematic acceleration across levels of achievement in order to achieve equitable distributions of achievement. Plotting gains across time is also needed to examine whether “summer effects” can be overcome. The present quasi-experimental design study was a three-year research and development collaboration among schools, government, and researchers to raise reading comprehension through critical discussions of achievement and teacher observation data and linking research on effective comprehension practices to specific needs. The collaboration resulted in increased rates of achievement that were variable but sustained across three years. The growth model showed a step-like pattern with rapid gains over school months and a plateau over summer. Over three years this represented an average achievement gain across cohorts followed longitudinally of one year’s progress in addition to expected progress over that period with stanine effect sizes of d = 0.62. The results show the significance of testing effects against the criterion of sustained and systematic achievement and the need to examine growth over multiple calendar years to better represent the pattern of gains.

Editorial article for the special issue on data-based decision making around the world: from policy to practice to results

This editorial article briefly examines the importance of data-based decision making. It discusses the definition as well as rationale for data-based decision making, its purposes, the use of data at different levels of the educational system, and possible promoting and hindering factors of effective data use. It also examines the effects of data use, intended effects (e.g., increased student achievement), as well as unintended consequences (e.g., cheating with tests). We end with suggestions for new research priorities.

Testing the effectiveness of an intervention model based on data use: a replication series across clusters of schools

Intervention models based on data use can be effective in raising student achievement. This article presents 3 studies of one such model which had reported improved reading comprehension levels in 7 poor urban multicultural schools serving indigenous and ethnic minority communities. The intervention (the Learning Schools Model) used a process comprising critical discussions of achievement and teacher observation data to develop specific and contextualized content for fine-tune instruction. The reliability and generality of the effects of the model were tested in a cluster of “like” schools and a cluster of “unlike” schools. The growth models showed similar effects to the original schools, with gains of between 3 to 4 months additional progress per year over 3 years. The replications show that models that use data to design local program content can be reliably and generally effective, but also there is a need to examine differential effects.

A model of school change for culturally and linguistically diverse students in New Zealand: a summary and evidence from systematic replication

A model of school change has been designed and implemented in a systematic replication series. Key principles are: that teachers need to be able to act as adaptive experts; that local evidence about teaching and learning is necessary to inform instructional design; that school professional learning communities are vehicles for changing teaching practice; that educative research–practice–policy partnerships are needed to solve problems; that instructional leadership in schools is necessary for community functioning and for coherence; and that effective programmes in schools are built by fine tuning existing practices. A three‐stage model has been tested across three clusters of schools: two groups of urban schools serving Māori and Pasifika children from low socio‐economic status communities and a third group comprising all the primary schools in a rural and remote region of New Zealand. The model has been extended to different academic areas (writing as well as reading) and to secondary schools since its initial testing. Evidence is provided for effectiveness for Māori and Pasifika children in urban schools and Māori students in rural and remote schools.

Data-based decision making : conclusions and a data use framework

In this chapter, the results of all the studies presented in this book are summarized. What are the lessons learned? Based on the lessons learned, we developed a data use framework. In this framework, data use is influenced by several enablers and barriers (e.g., the school organization context, data and data systems, and user characteristics). Data can be used in a desirable as well as undesirable manner, but what happens a lot in schools as well is that data are not used. Policy may also influence the use of data, as well as its enablers and barriers. Finally, we argue that if data are used in a desirable manner this can lead to school leader, teacher, and student learning (e.g., increased student achievement).

Analysis and Discussion of Classroom and Achievement Data to Raise Student Achievement

In New Zealand, there is evidence that analysing data in teams can lead to improvements in student achievement. In this country, data discussions in professional learning communities were an important component of research and development interventions in three clusters of schools (n = 48 schools). These interventions significantly improved student achievement over 3 years, and these achievement gains were sustained after the interventions. In this chapter, the authors focus on a central feature of these data discussions, understanding classroom instruction in relation to student achievement patterns. The importance of inter-dependence between schools and external experts, greater pedagogical content knowledge to link classroom instruction to achievement results and the creation and use of school artefacts (e.g., data analysis reports) to facilitate effective data use are also discussed.