Europe 2020 strategy sets out two educational targets: to reduce the share of early leavers from 15% to less than 10% and to increase the number of 30-34 yr olds with tertiary education from 31% to at least 40% (European Commission, 2010). An additional benchmark of ET 2020 strategy is that the share of low-achieving 15 yr olds in reading, mathematics and science should be less than 15% (Official journal of the EU, C 119 28.5.2009. p.7). These targets are of paramount importance for the European economy considering that education is thought to impact competitiveness and employability (Gross and Roth, 2008), and that Asian countries consistently outperform European countries in major international assessments of student achievement such are PISA (OECD, 2010) and TIMSS (Mullis, I.V.S. et al, 2012).
To attain aforementioned goals means that students at all levels of schooling need to have greater learning gains during their education. This can happen if: 1) schools know what their impact on student leaning progress is, separately from other influences; 2) schools know what teaching and schooling practices are the most effective in improving student learning progress, and 3) these findings are applied in practice.
Prior work of researchers in this project convinced them that these three topics are essential questions for education policy and practice; there are no other issues that they would rather select as the focus of this project. However, there are obstacles to accomplishing them.
First, many schools in Europe do not have palpable information on how they are educating their students. School internal evaluations and inspections can be quite subjective. Student achievement tests often don’t focus on students’ long-term learning progress, but on cross-sectional snapshots of student achievement, which is a result of many schooling and non schooling factors. They also neglect to take into consideration the diversity of student populations in different schools and therefore unfairly compare schools with affluent students with schools with disadvantaged students. Appropriate assessment of learning over time can be done by statistically equating schools by their students’ background variables so that schools appear to be educating the same population of students. Schools that, after the adjustment, perform better than the school with average characteristics have a positive value added, while those that perform worse, have a negative value added. This information is vital to schools because they can undertake relevant improvement activities, and also to policymakers, since they can identify schools that are in most need of improvement: those with low absolute test scores and negative value added (Braun H. et al, 2010). Unfortunately, considerable student data collected at various time points and sophisticated statistics are needed to determine value added of schools, so not many educational systems do it. Considering the time, money and effort needed to develop school performance feedback systems (SPFS), it is crucial that already available data and existing systems across Europe be identified, connected, modified and exploited to the fullest, rather than have each country re-invent the wheel by itself.
Second, once schools are statistically equated in terms of their student bodies, then the most effective teacher and school factors can be identified and subsequently improved. However, most educational effectiveness research (EER) suffers from the following flaw: the choice of schooling-related variables included in studies is somewhat haphazard and dependent on available data; this leads to conflicting results between the studies and lack of consensus on the most important variables. The most recently developed conceptual model in the field – Dynamic model of educational effectiveness (Creemers, B.P.M. & Kyriakides, L., 2008) – not only simultaneously and holistically examines the effectiveness of all relevant variables (identified in most pertinent prior research), but it also postulates their precedence within the model and measures them using rich indicators. Eight most effective teacher-related practices postulated by the model are: (1) orientation (explaining and challenging students to identify why particular lesson activities are taking place), (2) structuring (outlining the content, emphasizing main ideas, reviewing), (3) questioning (frequently attempting to involve students in the lesson), (4) teaching-modelling (teaching higher-order thinking skills), (5) application (providing needed practice through seatwork or small group tasks), (6) classroom as a learning environment (creating positive interactions, order), (7) management of time (organizing efficient learning environment and maximizing student engagement rates), and (8) assessment (especially formative). Four overarching school-level factors are: (1) policy and actions regarding improvement of teaching; (2) evaluation of the policy and actions; (3) policy and actions toward creating and improving school learning environment; (4) evaluation of the learning environment. The model has been praised on theoretical grounds by the most prominent researchers in the field (Reynolds, D. et al, ICSEI 2011); however, it has been used in empirical studies only several times, and it needs further confirmation of the obtained promising results. The confirmation would firmly establish the relevance of certain generalizable schooling factors which could subsequently be improved.
Third, it is crucial that the most effective teacher and school practices be applied in classrooms and schools. So far, the impact of EER on practice has been small. It is thought that the complex methodology used in EER overshadows its potential immense benefits for practice (Reynolds, D. et al, ICSEI 2011). Therefore, there need to be more attempts to translate EER findings into concrete school development programs, teacher training seminars, and materials in order to improve student learning progress (Creemers, B.P.M. & Kyriakides, L., 2006).