Data-based decision making (DBDM) is an important element of educational policy in many countries, as it is assumed that student achievement will improve if teachers worked in a data-based way. However, studies that evaluate rigorously the effects of DBDM on student achievement are scarce. In this study, the effects of an intensive DBDM-intervention for Grade 4 teachers on students' mathematical achievement were investigated in a randomized controlled trial. Multilevel analyses showed that although no main effect on students' mathematical achievements was found, students who received "extended instruction" benefited significantly from the intervention. Based on the results, recommendations for the design of new DBDM-interventions and for their evaluation are presented.

No abstract

A Bayesian IRT-model approach was used to investigate the validity and reliability of student perceptions of teaching quality. Furthermore, the student perceptions were compared with ratings of teaching quality by external observers. Grade 4 students (n = 675) filled out a questionnaire that was used to measure their opinions about the lessons of their teachers. Three lessons of 39 teachers were recorded and rated by 4 raters. The analyses showed that student perception and lesson observation scales fit best in an 11-dimensional model, which was an indication of construct validity and discriminant validity. Student perception scales were reliable, although not all items contributed to the scales to the same extent. Student ratings and lesson observations scores generally correlated moderately (ranging from r = .18 to r = .50). Higher correlations were found for scales with a similar content; however, no clear pattern was apparent. Suggestions for future research are presented.

No abstract