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Boumi, Shahab; Vela, Adan – International Educational Data Mining Society, 2019
Simplified categorizations have often led to college students being labeled as full-time or part-time students. However, at many universities student enrollment patterns can be much more complicated, as it is not uncommon for students to alternate between full-time and part-time enrollment each semester based on finances, scheduling, or family…
Descriptors: Markov Processes, Enrollment, College Students, Full Time Students
Moore, Colleen; Bracco, Kathy Reeves; Nodine, Thad; Esch, Camille; Grubb, Brock – Education Insights Center, 2019
California does not have a statewide data system that tracks student progress through K-12 and higher education and into the workforce. As a result, educators and policymakers cannot answer critical questions about student progress, which limits their ability to make evidence-based changes to support better and more equitable opportunities for…
Descriptors: Progress Monitoring, Data Collection, Elementary Secondary Education, Higher Education
McCormick, Meghan – MDRC, 2019
This post is one in a series highlighting MDRC's methodological work. Contributors discuss the refinement and practical use of research methods being employed across MDRC. Part I of this two-part post discussed MDRC's work with practitioners from the Boston Public Schools (BPS) Department of Early Childhood to construct valid and reliable measures…
Descriptors: Fidelity, Program Implementation, Research Methodology, Educational Research
Weingarten, Zachary; Bailey, Tessie Rose; Peterson, Amy – National Center on Intensive Intervention, 2019
If you are like most educators, you agree with the idea of providing intensive intervention for students with the most intractable academic and behavior problems. The question you may be asking is, how do I find the time? Intensive intervention requires time for planning and delivering individualized instruction, as well as time for collecting and…
Descriptors: Intervention, Scheduling, Individualized Instruction, Data Use
Rhim, Lauren Morando; Kothari, Shaini; Lancet, Stephanie – National Center for Special Education in Charter Schools, 2019
The National Center for Special Education in Charter Schools (the Center) is deeply committed to ensuring that students with disabilities have equal access to charter schools and that charter schools are designed and operated to enable success for all students. To accomplish this goal, the Center conducts analyses and releases a comprehensive…
Descriptors: Educational Trends, Special Education, Charter Schools, Data Analysis
Manansala, Ed; Cottingham, Benjamin W. – Policy Analysis for California Education, PACE, 2019
County offices of education (COEs) are expected to provide ongoing support to districts and other local education agencies to drive continuous improvement within California's education system. Fulfilling this role has required COEs to carry out their historical role as compliance monitors while simultaneously developing the necessary mindsets,…
Descriptors: Public Agencies, Counties, County School Districts, Educational Improvement
American Association of Community Colleges, 2019
This issue of "DataPoints" shows student military service by institution type. About 6 percent of all undergraduate students indicate that they are currently serving in the military, in the reserves or National Guard, or are a veteran of military service, according to data in the National Postsecondary Student Aid Study of 2015-16.
Descriptors: Military Personnel, Military Service, Veterans, Institutional Characteristics
dos Santos, Renato P. – Themes in Science and Technology Education, 2015
Big Data already passed out of hype, is now a field that deserves serious academic investigation, and natural scientists should also become familiar with Analytics. On the other hand, there is little empirical evidence that any science taught in school is helping people to lead happier, more prosperous, or more politically well-informed lives. In…
Descriptors: Data, Philosophy, Constructivism (Learning), Science Education
Abel, Todd; Poling, Lisa – Teaching Statistics: An International Journal for Teachers, 2015
Working with practicing teachers, this article demonstrates, through the facilitation of a statistical activity, how to introduce and investigate the unique qualities of the statistical process including: formulate a question, collect data, analyze data, and interpret data.
Descriptors: Statistical Analysis, Concept Formation, Data Collection, Data Interpretation
Rhemtulla, Mijke; van Bork, Riet; Borsboom, Denny – Measurement: Interdisciplinary Research and Perspectives, 2015
In this commentary, Mijke Rhemtulla, Riet van Bork, and Denny Borsboom write that they were delighted to see Bainter and Bollen's paper as a focus article in "Measurement." In their view, psychological researchers who use SEM rely too reflexively on reflective measurement, without sufficiently considering whether their indicators are…
Descriptors: Causal Models, Measurement, Data Interpretation, Statistical Data
Liu, Sanya; Ni, Cheng; Liu, Zhi; Peng, Xian; Cheng, Hercy N. H. – International Journal of Distance Education Technologies, 2017
Nowadays, Massive Open Online Courses (MOOCs) have obtained a rapid development and drawn much attention from the areas of learning analytics and artificial intelligence. There are lots of unstructured data being generated in online reviews area. The learning behavioral data become more and more diverse, and they prompt the emergence of big data…
Descriptors: Online Courses, Student Records, Learning Strategies, Cognitive Style
Alase, Abayomi – International Journal of Education and Literacy Studies, 2017
As a research methodology, qualitative research method infuses an added advantage to the exploratory capability that researchers need to explore and investigate their research studies. Qualitative methodology allows researchers to advance and apply their interpersonal and subjectivity skills to their research exploratory processes. However, in a…
Descriptors: Phenomenology, Qualitative Research, Research Methodology, Semi Structured Interviews
Millei, Zsuzsa; Gallagher, Jannelle – Early Child Development and Care, 2017
Australian early childhood education still labours with the achievement of universal access and the production of comprehensive and consistent data to underpin a national evidence base. In this article, we attend to the processes led by numbers whereby new practices of quantification, rationalization and reporting are introduced and mastered in a…
Descriptors: Foreign Countries, Early Childhood Education, Preschool Education, Access to Education
Gudivada, Venkat N. – Educational Technology, 2017
Various types of structured data collected by learning management systems such as Moodle have been used to improve student learning outcomes. Learning analytics refers to an assortment of data analysis methods used for this task. These methods typically do not consider unstructured data such as blogs, discussions, e-mail, and course messages.…
Descriptors: Data Collection, Data Analysis, Educational Research, Technology Uses in Education
Williamson, Ben – E-Learning and Digital Media, 2017
"Education data science" is an emerging methodological field which possesses the algorithm-driven technologies required to generate insights and knowledge from educational big data. This article consists of an analysis of the Lytics Lab, Stanford University's laboratory for research and development in learning analytics, and the Center…
Descriptors: Educational Theories, Educational Research, Data Collection, Data Analysis

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