ABSTRACT
184In the majority of European countries, the evaluation of schools is at the heart of the educational system as a means to guarantee the quality of education. Every year, in most countries around the world, students perform national examinations. Their results are stored in (administrative) datasets and analyzed by several stakeholders, including governmental agencies, media, and researchers on educational issues. These are not the only data that can be considered useful for evaluative purposes, however. Indeed, quantitative indicators also cover wider areas of school characteristics: inputs (financial and human resources), processes (hours of lessons, types of materials that are used, classroom schedules, etc.), and outputs other than tests (i.e., grades on several subjects, scores assigned to disciplinary behaviors, etc.). Overall, this amount of data constitutes a patrimony that schools could use to improve the quality of the educational processes that happen within them. At present, advances in information and communication technology (ICT) and data analysis techniques allow schools to make use of massive amounts of data in their daily management. This chapter focuses in particular on the use of students’ data to benchmark schools and to assess their performances. It illustrates the potential contribution of information gathered through data analytics to promote the continuous improvement of schools’ educational processes. Overall, we show that such objectives can be pursued by leveraging the information that is contained in administrative datasets. Such databases are collected for different purposes— especially for purely administrative duties— and can instead be really fruitful in assisting analysts in school assessment. For this purpose, techniques of data analysis are a crucial standpoint for making the most out of school evaluations. Once the data are available, the analytical problem lies in having the right instruments for using them and correctly interpreting the results. The use of data depends on two critical resources: (1) political commitment (the decision makers must be convinced of the importance of informing policies through evidence and quantitative information) and (2) the involvement of skilled workforce that can apply innovative and advanced methodologies to work in data analytics.
