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National Forum on Education Statistics, 2021
"The Forum Guide to Strategies for Education Data Collection and Reporting (SEDCAR)" was created to provide timely and useful best practices for education agencies that are interested in designing and implementing a strategy for data collection and reporting, focusing on these as key elements of the larger data process. It builds upon…
Descriptors: Data Collection, Educational Research, Statistical Data, Data Analysis
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Cornman, Stephen Q.; O'Reilly, Nora; Ampadu, Osei; Caskey, Melinda; Vidal, Phil – National Center for Education Statistics, 2022
In 2019, the National Center for Education Statistics (NCES) began exploratory data collection for the School Pension Survey (SPS). The SPS is a new data collection of elementary/secondary school teacher pension data collected at the school district level. The SPS was developed primarily in response to public demand for data on teacher and other…
Descriptors: Retirement Benefits, Teacher Retirement, Elementary School Teachers, Secondary School Teachers
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Robert Meyer; Anthony Milanowski; Ryan Veiga; Jessica Doherty – Journal of Education Human Resources, 2025
One potentially fruitful application for human capital analytics is to support policies and practices that might reduce undesirable teacher turnover. Teacher turnover can be harmful to student achievement and faculty cohesiveness and can exacerbate teacher shortages. This article describes an attempt to build a human capital analytics tool to help…
Descriptors: Teacher Persistence, Faculty Mobility, School Districts, Human Capital
von Zastrow, Claus; Roberts, Maxine T.; Squires, John – Education Commission of the States, 2021
State education data systems help policymakers use data to evaluate the impact of their efforts to improve education. By disaggregating the data -- that is, breaking it out by different student subgroups -- policymakers can ensure that their efforts address the needs of students who have been traditionally underserved in educational settings. Yet…
Descriptors: Data Analysis, Student Characteristics, Data Collection, Barriers
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Grasty, Sarah – Science Teacher, 2020
Fisheries scientists make sure that fish populations ("fisheries") are managed properly, neither over- or under-utilized, to maintain long-term economic and ecological stability. Scientists collect data and conduct surveys to determine fish populations, and then make recommendations about how many fish may be caught by commercial and…
Descriptors: Ichthyology, Animal Husbandry, Science Process Skills, Data Collection
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Diane D. Craig; Ruth H. Borger – Journal of Human Sciences & Extension, 2019
Effective communication requires a good message delivered through an effective channel and received by a receptive individual. When that communication is successful, the result is enhanced credibility and trust between the sender and the receiver. Telling the Extension story effectively requires both relevant, credible data to compose a clear…
Descriptors: Extension Education, Data Collection, Communication Strategies, Data Analysis
Kenneth Gloeckner – ProQuest LLC, 2021
Adjunct faculty have become the new majority faculty on campuses across the country. There are two theories why adjunct faculty are so relied upon: cost or contingency. Some institutions may employ adjunct faculty for cost-savings reasons. In contrast, other institutions may depend on the contingent, temporary nature of the employment contracts to…
Descriptors: Adjunct Faculty, Employment Practices, Cost Effectiveness, Contingency Management
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DePaolo, Concetta A.; Jacobs, Aimee – Journal of Information Systems Education, 2021
We present a case study to teach data visualization with Tableau in an introductory business analytics course. The case uses publicly available data sets from touringplans.com that record wait times for various attractions at Disney World in Orlando, Florida. In the case study, students use Tableau to clean, organize, and analyze wait time data,…
Descriptors: Data Analysis, Visual Aids, Computer Software, Teaching Methods
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Tisha L. N. Emerson; KimMarie McGoldrick – Journal of Economic Education, 2024
Using data from 11 institutions, the authors investigate enrollments in intermediate microeconomics to determine characteristics of successful and unsuccessful students and follow the retake behavior of unsuccessful students. Successful students are significantly different from unsuccessful ones, and unsuccessful students differ by type…
Descriptors: Microeconomics, Student Attrition, Withdrawal (Education), Academic Persistence
Tucker, William; Long, Don – National Association of State Boards of Education, 2018
With roots in student-centered pedagogies that go back at least to the 1900s, personalized learning meets students' learning goals, needs, context, and pace while incorporating their interests and preferences. Personalized learning in today's classroom depends upon and creates an abundance of rich student data, which simultaneously fosters new…
Descriptors: Data Collection, Data Analysis, Student Records, Individualized Instruction
Brower, Rebecca L.; Bertrand Jones, Tamara; Osborne-Lampkin, La'Tara; Hu, Shouping; Park-Gaghan, Toby J. – Grantee Submission, 2019
Big qualitative data (Big Qual), or research involving large qualitative data sets, has introduced many newly evolving conventions that have begun to change the fundamental nature of some qualitative research. In this methodological essay, we first distinguish big data from big qual. We define big qual as data sets containing either primary or…
Descriptors: Qualitative Research, Data, Change, Barriers
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Johnson, Nate – Change: The Magazine of Higher Learning, 2023
In recent decades, education leaders and researchers have increasingly sought to disaggregate key higher education outcome data--graduation rates, attainment, employment, and income--by race and ethnicity in order to uncover and narrow equity gaps. The same is true, recently, for affordability, especially as it relates to the differential impact…
Descriptors: Higher Education, Educational Finance, Data Analysis, Educational Equity (Finance)
Shute, Valerie; Rahimi, Seyedahmad; Smith, Ginny – Grantee Submission, 2019
Well-designed digital games hold promise as effective learning environments. However, designing games that support both learning and engagement without disrupting flow is quite tricky. In addition to including various game design features (e.g., interactive problem solving, adaptive challenges, and player control of gameplay) to engage players,…
Descriptors: Physics, Science Instruction, Educational Games, Educational Technology
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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
Xue, Kang; Huggins-Manley, Anne Corinne; Leite, Walter – Educational and Psychological Measurement, 2022
In data collected from virtual learning environments (VLEs), item response theory (IRT) models can be used to guide the ongoing measurement of student ability. However, such applications of IRT rely on unbiased item parameter estimates associated with test items in the VLE. Without formal piloting of the items, one can expect a large amount of…
Descriptors: Virtual Classrooms, Artificial Intelligence, Item Response Theory, Item Analysis
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