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Gibbs, Benjamin G.; Shafer, Kevin; Miles, Aaron – International Journal of Research & Method in Education, 2017
While the use of inferential statistics is a nearly universal practice in the social sciences, there are instances where its application is unnecessary and potentially misleading. This is true for a portion of research using administrative data in educational research in the United States. Surveying all research articles using administrative data…
Descriptors: Statistical Inference, Statistics, Data, Information Utilization
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
Day, Annette – New Review of Academic Librarianship, 2018
This article describes a project undertaken as part of a cross-campus strategic planning effort. The project documented current campus practices and systems in use for collecting, analyzing and reporting key research metrics. The project identified organizational issues around siloed data collection and lack of clarity on data stewards, data…
Descriptors: Strategic Planning, Data Collection, Data Analysis, College Libraries
Makela, Carole J. – Journal of Family and Consumer Sciences, 2016
"Big data" prompts a whole lexicon of terms--data flow; analytics; data mining; data science; smart you name it (cars, houses, cities, wearables, etc.); algorithms; learning analytics; predictive analytics; data aggregation; data dashboards; digital tracks; and big data brokers. New terms are being coined frequently. Are we paying…
Descriptors: Data Analysis, Information Utilization, Data Collection, Consumer Science
Sirinides, Philip; Coffey, Missy – State Education Standard, 2018
Americans share an expectation that government will serve the common good, creating inescapable pressure for public agencies--especially those that serve young children--to perform well. Yet education and human services agencies continually struggle to respond to the complex conditions in which children are born. How can they address the practical…
Descriptors: Early Childhood Education, Evidence Based Practice, Decision Making, Educational Policy
Daniel, Ben Kei – British Journal of Educational Technology, 2019
Big Data refers to large and disparate volumes of data generated by people, applications and machines. It is gaining increasing attention from a variety of domains, including education. What are the challenges of engaging with Big Data research in education? This paper identifies a wide range of critical issues that researchers need to consider…
Descriptors: Data, Educational Research, Information Utilization, Epistemology
Rankin, Jenny Grant – Online Submission, 2016
Even before Florence Nightingale diagrammed soldiers' changing mortality rates to save lives, we have seen clever design used to convey statistics and other complex concepts. In the field of education, student data is used extensively, and those who share such data need to ensure this communication is effective. This article is based on education…
Descriptors: Information Utilization, Educational Research, Best Practices, Student Records
Data Quality Campaign, 2017
When students, parents, educators, and partners have the right information to make decisions, students excel. By forming research partnerships, state education agencies (SEAs), policymakers, practitioners, and education researchers at universities and other research institutes can work together to use research to provide the best education…
Descriptors: Data, Information Utilization, Partnerships in Education, Educational Research
Douglas, Kerrie A.; Bermel, Peter; Alam, Md Monzurul; Madhavan, Krishna – Journal of Learning Analytics, 2016
MOOCs attract a large number of learners with largely unknown diversity in terms of motivation, ability, and goals. To understand more about learners in highly technical engineering MOOCs, this study investigates patterns of learners' (n = 337) behaviour and performance in the Nanophotonic Modelling MOOC, offered through nanoHUB-U. The authors…
Descriptors: Online Courses, Large Group Instruction, Distance Education, Technology Uses in Education
König, Christoph; van de Schoot, Rens – Educational Review, 2018
The ability of a scientific discipline to build cumulative knowledge depends on its predominant method of data analysis. A steady accumulation of knowledge requires approaches which allow researchers to consider results from comparable prior research. Bayesian statistics is especially relevant for establishing a cumulative scientific discipline,…
Descriptors: Bayesian Statistics, Educational Research, Educational Practices, Data Analysis
Wang, Yinying – Education Policy Analysis Archives, 2017
Despite abundant data and increasing data availability brought by technological advances, there has been very limited education policy studies that have capitalized on big data--characterized by large volume, wide variety, and high velocity. Drawing on the recent progress of using big data in public policy and computational social science…
Descriptors: Educational Policy, Educational Research, Misconceptions, Barriers
Prinsloo, Paul; Slade, Sharon – Journal of Learning Analytics, 2016
In light of increasing concerns about surveillance, higher education institutions (HEIs) cannot afford a simple paternalistic approach to student data. Very few HEIs have regulatory frameworks in place and/or share information with students regarding the scope of data that may be collected, analyzed, used, and shared. It is clear from literature…
Descriptors: Data Collection, Data Analysis, Educational Research, Information Security
Daniel, Ben – Educational Technology, 2017
The increasing availability of digital data in higher education provides an extraordinary resource for researchers to undertake educational research, targeted at understanding challenges facing the sector. Big data can stimulate new ways to transform processes relating to learning and teaching, and helps identify useful data, sources of evidence…
Descriptors: Higher Education, Educational Research, Information Utilization, Data
Sun, Jingping; Przybylski, Robert; Johnson, Bob J. – Educational Assessment, Evaluation and Accountability, 2016
Despite the increased worldwide acknowledgment of the importance of teachers' use of formative and/or summative assessment data to improve teaching and learning, empirical research on its impacts on student learning is sparse. Even more so is the lack of studies on the best ways for school leaders to develop teachers' capacity. Teachers generally…
Descriptors: Data, Formative Evaluation, Summative Evaluation, Information Utilization
Green, Jennifer L.; Smith, Wendy M.; Kerby, April T.; Blankenship, Erin E.; Schmid, Kendra K.; Carlson, Mary Alice – Statistics Education Research Journal, 2018
In this study, we examined how in-service middle-level mathematics teachers used statistics in their own classroom research. Using an embedded single-case design, we analyzed a purposefully selected sample of nine teachers' classroom research papers, identifying several themes within each phase of the statistical problem solving process to…
Descriptors: Introductory Courses, Statistics, Inservice Teacher Education, Mathematics Teachers