In educational technology and learning sciences, there are multiple uses for a predictive model of whether a student will perform a task correctly or not. For example, an intelligent tutoring system may use such a model to estimate whether or not a student has mastered a skill. We analyze the significance of data recency in making such…

Monitoring Trends in Educational Growth (MTEG) offers a flexible, collaborative approach to developing and implementing an assessment of learning outcomes that yields high-quality, nationally relevant data. MTEG is a service that involves ACER staff working closely with each country to develop an assessment program that meets the country's…

Predicting the future is now in the hands of K12 administrators. While for years districts have collected thousands of pieces of student data, educators have been using them only for data-driven decision-making or formative assessments, which give a "rear-view" perspective only. Now, using predictive analysis--the pulling together of data over…

In most PISA-participating countries and economies, the average socio-economic background of students who attend privately managed schools is more advantaged than that of those who attend public schools. Yet in some countries, there is little difference in the socio-economic profiles between public and private schools. Why? An analysis of PISA…