Computer-based Assessment of Foreign Language Speaking Skills

In the frame of the current effort to establish an indicator of foreign language competency in Europe via the implementation of a language survey in 2011, a Conference on computer-based assessment (CBA) of speaking skills was held in Brussels in June 2010. The sponsors of this event, the Centre for Research on Lifelong Learning of the European Commission in Ispra and the Directorate General of Education and Culture, are pleased to put forward this report with the Conference proceedings. It is our hope that it can serve, not only to inform the implementation of the language survey, but also to assist Member States and their representatives in the task of gathering current evidence on the benefits and challenges linked to the implementation of CBA.JRC.DG.G.9-Econometrics and applied statistic

Regional inequalities in PISA: the case of Italy and Spain

This technical brief analyses the regional distribution of skills in Italy and Spain. Educational attainment rates have frequently been used as an indicator of regional educational development in EU Member States (MS). These rates indicate significant regional disparities in education within countries. However, recent evidence shows that the quality of education, as measured by the level of specific skills, is more important than the number of years one spends in school, in particular when considering the relationship between the cognitive (and non-cognitive) skills and economic growth. International large scale assessments (ILSA) of student performance measure these cognitive skills in key areas. OECD’s Program for International Student Assessment (PISA) provides a very useful and important source of information of students' performance in key cognitive skills. When analysing PISA data, researchers and commentators often focus on cross-country comparisons. However, vast within-country differences exist, also in terms of educational attainment and PISA test scores. A focus on country averages alone would hence provide only a partial view of the status of education within countries. However, the possibility of exploring within-country differences with PISA data is limited to only a few countries. In this report we focus on regional inequalities in cognitive skills (as measured by PISA test scores) in Italy and Spain, using regional PISA data from the most recent 2015 wave, and we analyse the factors that are associated with these inequalities. In order to insure full comparability between the two countries we define regions at the level of NUTS1 (macro-region), following Eurostat’s official NUTS 2013 classification. We investigate regional inequalities by using descriptive statistics, by running a range of OLS regression models that allow us to analyse the associations between PISA 2015 science scores and the explanatory variables within regions and finally by using the Blinder-Oaxaca decomposition method to specify the factors that are related to within-country differences. The results show that there are significant regional differences in PISA scores within both MS. There are several factors that are associated with regional differences within Italy and Spain. The factors most consistently positively associated with regional science achievement are teacher-directed teaching and epistemological beliefs, while grade repetition and truancy are significantly negatively related with achievement. Still, there is also a range of other relevant factors varying between and within both MS. The Blinder-Oaxaca decomposition also shows that variables such as the socio-economic background, the students’ expected occupation, learning outside school time, truancy, immigrant status and grade repetition matter for within-country differences. Our results suggest that policy makers should focus on finding solutions to limit truancy and rethink grade repetition to leverage scores in lower performing regions. Moreover, our results with regard to epistemological beliefs and teaching practices challenge thinking about how science should be taught in schools in Italy and Spain. The specific results for each region may allow policy makers to consider more in detail how a region stands in comparison to the rest of the country, and the specific factors that need to be addressed to improve the within-country inequality related to educational achievement.JRC.B.4-Human Capital and Employmen

Teacher Costs

The purpose of this technical brief is to assess current methodologies for the collection and calculation of teacher costs in European Union (EU) Member States in view of improving data series and indicators related to teacher salaries and teacher costs. To this end, CRELL compares the Eurydice collection on teacher salaries with the similar Organisation for Economic Co-operation and Development (OECD) data collection and calculates teacher costs based on the methodology established by Statistics Canada as explained in Indicator B7 in Education at a Glance (OECD, 2014). This indicator allows for analysing the different factors that influence teacher costs: teacher salaries, teaching time, instruction hours and student/teacher ratios, as well as class size. The analyses will provide specific information on the contribution of the different factors used to derive the Salary Cost of Teachers per Student (CCS) and how they might depend on the way data for the different factors are collected. On the basis of assessing the different forms of data collection with the same methodology, suggestions for development work that could be undertaken to align the Eurydice and OECD data collections are offered.JRC.DDG.01-Econometrics and applied statistic

Quality of Teaching and Learning in Science

Quality teaching and learning is linked to the structural and process characteristics of educational systems. Importantly, the role of education policies, of schools and of teachers in promoting high student performance is increasingly recognized (IEA, 2016; Hanushek & Woessmann, 2014). International large-scale surveys (ILSA) such as PISA allow for envisioning what is amenable to change beyond what is determined by culture and to consider reforms that improve learning conditions (Hanushek & Woessmann, 2014). This report focuses on identifying the variation in different teaching practices in the Science classroom and their relation with students’ achievement. Using PISA 2015 data collected in the student and school questionnaires, the report offers an overview of the variations in teaching practices across European Member States (EU MS) and how they relate to students’ Science achievement. For this purpose, we present univariate statistics and we explore the proportion of variance in students’ achievement that can be explained by the use of different teaching practices. More specifically, this report answers the following research question: What is the relationship between teaching practices, the learning environment and students’ achievement in EU MS? A multilevel analysis is used for the available PISA 2015 data including different levels of analysis. These analyses contribute to our understanding of the differences and similarities among countries and provide evidence regarding teaching effectiveness, giving an overview about what works well in the Science classroom in EU MS. This information strengthens the evidence-base and can be used at the EU level to share knowledge about good practices and to inform policy initiatives that focus on high quality teaching (European Commission, 2016). Specific actions in this area are intended to help raise the skills’ levels of pupils and the workforce by improving the effectiveness of education and training systems (European Commission, 2015).JRC.B.4-Human Capital and Employmen

Differential Item Functioning (DIF): What Functions Differentlyfor Immigrant Students in PISA 2009 Reading Items?

This analysis focuses on identifying items that present Differential Item Functioning (DIF) between native and immigrant students in the PISA 2009 reading assessment. The findings indicate that thirty-five percent of the 95 items or questions included in the analysis present DIF. This is an important finding because it can help us detect content that is treated differently between different groups of students. In particular, we found that immigrant students perform better than native ones in about half of the items with DIF and that they tend to perform better in questions that relate to situations and types of reading typically done in school settings. Policy measures aimed at ensuring educational achievement for all students can be informed by studies of this kindJRC.G.3-Econometrics and applied statistic

Reading Literacy in EU Countries: Evidences from PIRLS

In this report we used data from the Progress in International Reading Literacy Study (PIRLS) 2011 in order to identify the school, class and individual student background factors that explain reading literacy achievement. We aim to identify the factors associated with achievement at different levels of analysis, both at the EU level and at the individual country level using a multilevel model. Additionally, we intend to establish trends in students reading achievement by comparing PIRLS 2006 and PIRLS 2011 cycles. For the data from 2011 we found that our aggregated model explains 37% of the variance in students’ achievement and that the variables with the highest impact on students´ overall reading score relate to home resources and practices, students´ attitudes toward reading and pre-reading knowledge. Moreover, the results of the country-level analysis indicate that the variables with the strongest influence on students’ reading performance are the same, despite of the wide variation across countries in terms of their magnitude due to the characteristics of each country. Furthermore, these findings are in line with the previous analysis performed for PIRLS 2006 (Araújo & Costa, 2012). Our results have important policy implications as they show which factors can be addressed by policy measures to improve students´ achievement.JRC.DDG.01-Econometrics and applied statistic

Strategies for Teaching English Language Learners

Features a review of research on the literacy development of English Language Learners during the elementary school years.JRC.DDG.01-Econometrics and applied statistic

O Problema do Ensino em Contexto é que pode se muito Limitativo [The problem with teaching in context is that it can be very limiting]

This paper presented at a round table discussed the findings of international large scale surveys such as TIMSS and PISA regarding the Mathematics achievement of students in different countries. Accordingly, the discussion focused on the identification of Mathematics topics and types of problems students are asked to engage with and solutions for improvement were advanced. More specifically, a critique of the pedagogical practice of teaching in context for problem solving in mathematics was presented.JRC.G.3-Econometrics and applied statistic

Strategies for teaching English Language Learners: helping Children Develop Conversational and Academical Language Skills

Features a review of research on the literacy development of English Language Learners during the elementary school years.JRC.DDG.01-Econometrics and applied statistic

English Language Learning

The Program for International Reading Literacy Study, developed by the International Association for the Evaluation of Educational Achievement (IEA) every 5 years, assesses the reading attainment of 4th grade students. Portugal participated in 1991 and then again in 2011. In the latter year, Portuguese students obtained a result slightly above the international mean computed for the 49 participating countries (Mullis, Martin, Foy, & Drucker, 2012). This chapter presents an analysis of the results for Portugal and describes the relationship between predictors of reading comprehension, such as home book reading before the start of compulsory education, and reading achievement. In addition, it contextualizes the results for Portugal in comparison with other countries and discusses them in light of what reading research has to say about how to create good readers.JRC.DDG.01-Econometrics and applied statistic