Meeting the highly qualified teachers challenge

The moulding of the future of a nation depends on the teachers and the education they provide in schools. Research evidence from the US. National Assessment of Educational Progress (NAEP) confirms this to be the case. Quantitative analysis of data indicates that measures associated with pre-service teacher preparation are by far the strongest correlates of student achievement (Darling-Hammond 2000). The research report of Wilson, Floden, and Ferrini-Mundy (2001) also suggests a strong relationship between teacher preparation and teacher effectiveness. Other research studies similarly indicate that differences in teacher quality are the most crucial for student achievement when compared with variables such as class size, streaming, or the amount of homework (Anderson & Helms 2001; Mendro 1998; Powell & Anderson 2002; Rivkin, Hanushek & Kain 2001; Saunders & Horn 1994; Strong & Tucker 2000) as possible contributory factors. In the pursuit of improved student achievement, we therefore need to examine seriously the role of teacher variables, ranging from teacher qualifications and their subject matter knowledge, to what motivates them to take up teaching as a career and as a consequence, how they teach in classrooms.     

The implementation of the Bologna process

Abstract

This study identifies the responsibilities of the bodies and institutions involved in the implementation of the Bologna Process. They include the levels of Europe, nations, higher education institutions, departments, degree programmes, teachers and students. The future planning is analysed using the Balanced Scorecard approach designed for the communication and implementation of the strategy. The approach is used to analyse the objectives of European education policy from the different perspectives. Strategic planning and quality assurance are most efficient when they are transformed into action at the closest possible point to teaching and learning.     

The Tunisian Educational Reform: From Quantity to Quality and the Need for Monitoring and Assessment

The Tunisian education system, like that of many other Southern nations of intermediate wealth, finds itself pulled in opposing directions. While quantitative advances at different levels of instruction have been easily achieved, a reorientation towards qualitative matters is nowadays more than ever necessary. Reforms at the secondary level, and the perspectives that they open up in terms of an improvement in the quality of educational services for young people, will receive special attention in this article. Radical changes in the traditional status of school inspectors are needed. Furthermore, a new national agency for monitoring and evaluating students' outcomes and schools' performance is indispensable.     

Document retrieval on repetitive string collections

Most of the fastest-growing string collections today are repetitive, that is, most of the constituent documents are similar to many others. As these collections keep growing, a key approach to handling them is to exploit their repetitiveness, which can reduce their space usage by orders of magnitude. We study the problem of indexing repetitive string collections in order to perform efficient document retrieval operations on them. Document retrieval problems are routinely solved by search engines on large natural language collections, but the techniques are less developed on generic string collections. The case of repetitive string collections is even less understood, and there are very few existing solutions. We develop two novel ideas, interleaved LCPs and precomputed document lists, that yield highly compressed indexes solving the problem of document listing (find all the documents where a string appears), top-k document retrieval (find the k documents where a string appears most often), and document counting (count the number of documents where a string appears). We also show that a classical data structure supporting the latter query becomes highly compressible on repetitive data. Finally, we show how the tools we developed can be combined to solve ranked conjunctive and disjunctive multi-term queries under the simple \({\textsf{tf}}{\textsf{-}}{\textsf{idf}}\) model of relevance. We thoroughly evaluate the resulting techniques in various real-life repetitiveness scenarios, and recommend the best choices for each case.