The Center for IDEA Early Childhood Data Systems (DaSy Center) was charged with developing a data system framework. This document contains that framework. It was developed over 14 months with extensive input from Individuals with Disabilities Education Act (IDEA) Part C (Early Intervention) and Part B (Early Childhood Special Education) Section…

This paper and the four commissioned works on which it is based are guided by the important question: How can we start valuing practices and outcomes of teaching and learning that are difficult to reduce to numbers? As the process of developing indicators for the Post-2015 education targets unfolds, some of the targets are at risk of being dropped…

As more of commerce, entertainment, communication, and learning are occurring over the Web, the amount of data online activities generate is skyrocketing. Commercial entities have led the way in developing techniques for harvesting insights from this mass of data for use in identifying likely consumers of their products, in refining their products…

The purpose of this paper is to review recent developments related to the development of indicators of educational progress in the context of the Post 2015 deliberations to generate a new international architecture for educational investment through to 2030. There have been a plethora of suggestions and several parallel consultation processes…

This report reviews the conceptual debates and existing data sources that relate to three ambiguous and controversial targets included in the post-2015 education framework: Relevant Learning Outcomes; Knowledge, Skills, Values, and Attitudes (for education for global citizenship and sustainable development); and Teachers and Safe, Inclusive, and…

The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…

Data-driven decision making (DDDM), applied to student achievement testing data, is a central focus of many school and district reform efforts, in part because of federal and state test-based accountability policies. This paper uses RAND research to show how schools and districts are analyzing achievement test results and other types of data to…