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Figlio, David; Karbownik, Krzysztof; Salvanes, Kjell – Education Finance and Policy, 2017
Thanks to extraordinary and exponential improvements in data storage and computing capacities, it is now possible to collect, manage, and analyze data in magnitudes and in manners that would have been inconceivable just a short time ago. As the world has developed this remarkable capacity to store and analyze data, so have the world's governments…
Descriptors: Educational Research, Data, Information Utilization, Management Information Systems
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Adejo, Olugbenga; Connolly, Thomas – Journal of Education and Practice, 2017
The increase in education data and advance in technology are bringing about enhanced teaching and learning methodology. The emerging field of Learning Analytics (LA) continues to seek ways to improve the different methods of gathering, analysing, managing and presenting learners' data with the sole aim of using it to improve the student learning…
Descriptors: Higher Education, Program Implementation, Information Utilization, Evidence Based Practice
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Rienties, Bart; Cross, Simon; Marsh, Vicky; Ullmann, Thomas – Open Learning, 2017
Most distance learning institutions collect vast amounts of learning data. Making sense of this 'Big Data' can be a challenge, in particular when data are stored at different data warehouses and require advanced statistical skills to interpret complex patterns of data. As a leading institute on learning analytics, the Open University UK instigated…
Descriptors: Foreign Countries, Distance Education, Data Collection, Data Interpretation
Rihák, Jirí – International Educational Data Mining Society, 2015
In this work we introduce the system for adaptive practice of foundations of mathematics. Adaptivity of the system is primarily provided by selection of suitable tasks, which uses information from a domain model and a student model. The domain model does not use prerequisites but works with splitting skills to more concrete sub-skills. The student…
Descriptors: Mathematics Achievement, Mathematics Skills, Models, Reaction Time
James Irvine Foundation, 2015
The Exploring Engagement Fund provides risk capital for arts nonprofits to experiment with innovative ideas about how to engage diverse Californians. In order to understand the variety of Californians engaged in arts experiences, this guide is intended to support current and future Fund grantees in collecting participant information. Exploring…
Descriptors: Art Activities, Private Financial Support, Participant Characteristics, Low Income Groups
Waters, John K. – Campus Technology, 2012
In the case of higher education, the hills are more like mountains of data that "we're accumulating at a ferocious rate," according to Gerry McCartney, CIO of Purdue University (Indiana). "Every higher education institution has this data, but it just sits there like gold in the ground," complains McCartney. Big Data and the new tools people are…
Descriptors: Higher Education, Educational Change, Data, Data Processing
Data Quality Campaign, 2014
This publication provides an overview of the current federal opportunities to help states advance their data-related activities. Although not exhaustive, this list provides a starting point for federal policymakers to support states' work in this area.
Descriptors: Federal Aid, Data Collection, Data Processing, Information Management
Koh, Byungwan – ProQuest LLC, 2011
The advent of information technology has enabled firms to collect significant amounts of data about individuals and mine the data for developing their strategies. Profiling of individuals is one common use of data collected about them. It refers to using known or inferred information to categorize the type of an individual and to tailor specific…
Descriptors: Screening Tests, Program Effectiveness, Information Technology, Data Collection
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Chen, Chen-Su; Sable, Jennifer; Mitchell, Lindsey; Liu, Fei – National Center for Education Statistics, 2012
The Common Core of Data (CCD) nonfiscal surveys consist of data submitted annually to the National Center for Education Statistics (NCES) by state education agencies (SEAs) in the 50 states, the District of Columbia, Puerto Rico, the four U.S. Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin…
Descriptors: School Surveys, Documentation, State Surveys, Annual Reports