GraphoGame

This chapter investigates the effectiveness of a digital reading tool, called GraphoGame, that could be employed as one of the solutions to the poor reading results of learners that have been revealed in both national and international assessments in Namibia, specifically, and Sub-Saharan Africa in general. Following a research study, this chapter sets out to demonstrate that, through pre-and post-tests, GraphoGame Afrikaans improved the initial reading skills of Grade 1 learners. The results have implications for the utilization of computer-assisted tools to support reading acquisition in the lower grades. As GraphoGame employs a scaffolded approach by presenting learners with letters and words, it can be utilized to support learners individually in classes with large numbers, as is typical in Africa.

Scientifically Speaking We know a lot, or do we? A model of knowledge using the Electromagnetic Spectrum

A quantitative model of human knowledge of the physical world based on the use of various parts of the electromagnetic spectrum is proposed. The model is epistemologically effective in demonstrating limits to what scientific knowledge can provide us about the physical world, thus enhancing for students the potentially exploratory opportunities available in scientific research, based on how little we really know. Additionally, the model provides a pedagogically useful way to engage students in considerations of the role of measurement, scale and estimation; these features are urgently needed by many students according to international assessments and are particularly important for non-science majors.

Correction to: Examining educational inequalities: insights in the context of improved mathematics performance on national and international assessments at primary level in Ireland

An amendment to this paper has been published and can be accessed via the original article.

Poland: Polish Education Reforms and Evidence from International Assessments

Over the last two decades, the Polish education system has been reformed several times, with the comprehensive structural reform in 1999, curriculum and evaluation reform in 2007, and early education reform introduced gradually until 2014. Student outcomes, as documented by PISA, but also other international assessments, largely improved over the last 20 years. Poland moved from below the OECD average to a group of top-performing countries in Europe. This chapter describes the reforms and research on their effects. It also discusses how it was possible to find political support for the reversal of changes that seemed to be highly successful. It provides three lessons from the Polish experience. First, the evidence should be widely disseminated among all stakeholders to sustain reforms. Second, the sole reliance on international studies is not sufficient. Additional investment into secondary analyses and national studies is necessary to develop evidence for better-informed political discussions. Third, some positive changes are more difficult to reverse. In Poland, increased school autonomy, but also external examinations, broader access to preschool and higher education, are among the changes that the new government could not alter.

Setting up the Scene: Lessons Learned from PISA 2018 Statistics and Other International Student Assessments

PISA 2018 was the largest large-scale international assessment to date. Its results confirm the improvements of some countries, the challenges other countries face, and the decline observed in a few others. This chapter reflects on the detailed analyses of ten countries policies, constraints, and evolutions. It highlights key factors, such as investment, curriculum, teaching, and student assessment. And it concludes by arguing that curriculum coherence, an emphasis on knowledge, student observable outcomes, assessment, and public transparency are key elements. These elements are crucial both for education success in general and for its reflection on PISA and other international assessments.

Does early tracking affect learning inequalities? Revisiting difference-in-differences modeling strategies with international assessments

The development of international surveys on children’s learning like PISA, PIRLS and TIMSS—delivering comparable achievement measures across educational systems—has revealed large cross-country variability in average performance and in the degree of inequality across social groups. A key question is whether and how institutional differences affect the level and distribution of educational outcomes. In this contribution, we discuss the difference-in-differences strategies employed in the existing literature to evaluate the effect of early tracking on learning inequalities exploiting international assessments administered at different age/grades. In their seminal paper, Hanushek and Woessmann (Econ J 116:C63–C76, 2006) analyze with two-step estimation the effect of early tracking on overall inequalities, measured by test scores’ variability indexes. Later work of other scholars in the economics and sociology of education focuses instead on inequalities among children of different family background, using individual-level models on pooled data from different countries and assessments. In this contribution, we show that individual pooled difference-in-differences models are quite restrictive and that in essence they estimate the effect of tracking by double differentiating the estimated cross-sectional family background regression coefficients between tracking regimes and learning assessments. Starting from a simple learning growth model, we show that if test scores at different surveys are not measured on the same scale, as occurs for international learning assessments, pooled individual models may deliver severely biased results. Instead, the scaling problem does not affect the two-step approach. For this reason, we suggest using two-step estimation also to analyze family-background achievement inequalities. Against this background, using PIRLS-2006 and PISA-2012 we conduct two-step analyses, finding new evidence that early tracking fosters both overall inequalities and family background differentials in reading literacy.