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Burr, Wesley; Chevalier, Fanny; Collins, Christopher; Gibbs, Alison L; Ng, Raymond; Wild, Chris J – Teaching Statistics: An International Journal for Teachers, 2021
In 2010, Nolan and Temple Lang proposed "integration of computing concepts into statistics curricula at all levels." The unprecedented growth in data and emphasis on data science has provided an impetus to finally realizing full implementations of this in new statistics and data science programs and courses. We discuss a proposal for the…
Descriptors: Computation, Mathematics Skills, Teaching Methods, Introductory Courses
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Frischemeier, Daniel; Biehler, Rolf; Podworny, Susanne; Budde, Lea – Teaching Statistics: An International Journal for Teachers, 2021
In this paper, we will describe an introduction to Data Science for secondary school students. We will report on the design and implementation of an introductory unit on "Data and data detectives with CODAP" in which secondary school students used the online tool CODAP to explore real and meaningful survey data on leisure time activities…
Descriptors: Data Analysis, Interdisciplinary Approach, Secondary School Students, Curriculum Design
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Dogucu, Mine; Çetinkaya-Rundel, Mine – Journal of Statistics and Data Science Education, 2021
Best practices in statistics and data science courses include the use of real and relevant data as well as teaching the entire data science cycle starting with importing data. A rich source of real and current data is the web, where data are often presented and stored in a structure that needs some wrangling and transforming before they can be…
Descriptors: Statistics Education, Data Use, Best Practices, Data Analysis
Fisk, Selena – Solution Tree, 2021
Data--done right--has the power to put schools on the path to true change. Rely on this research-backed resource to help you kickstart, implement, and sustain data-informed school-wide transformation. There are many ways to use student data, and the author's 10 steps offer practical, clear methods for establishing a data team, collecting relevant…
Descriptors: Data Use, Educational Change, Data Collection, Data Analysis
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Alturki, Sarah; Hulpu?, Ioana; Stuckenschmidt, Heiner – Technology, Knowledge and Learning, 2022
The tremendous growth of educational institutions' electronic data provides the opportunity to extract information that can be used to predict students' overall success, predict students' dropout rate, evaluate the performance of teachers and instructors, improve the learning material according to students' needs, and much more. This paper aims to…
Descriptors: Grade Prediction, Academic Achievement, Data Use, Dropout Rate
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Schildkamp, Kim; Datnow, Amanda – Leadership and Policy in Schools, 2022
Because learning from failures is just as important as learning from successes, we used qualitative case study data gathered in the Netherlands and the United States to examine instances in which data teams struggle to contribute to school improvement. Similar factors in both the Dutch and U.S. case hindered the work of the data teams, such as…
Descriptors: Foreign Countries, Educational Improvement, Data Use, Failure
Morris, Kelsey; Lewis, Timothy; Mitchell, Barb – Center on Positive Behavioral Interventions and Supports, 2022
This brief provides district PBIS [positive behavioral interventions and supports] leadership teams a framework to examine school-level fidelity and self-assessment data to guide resource, professional development, and technical assistance decision making.
Descriptors: School Districts, Data Use, Data Analysis, Fidelity
Prophet-Bullock, Ebony E. – ProQuest LLC, 2023
This qualitative case study sought to discover how school-level data teams can intentionally use effective data practices to identify and implement high-leverage interventions that support all students, including Black and Latinx boys, in attaining the necessary academic requirements for high school graduation. The researcher analyzed data from…
Descriptors: High School Students, African American Students, Hispanic American Students, Males
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Isabel R. Fulcher; Donald Fejfar; Nichole Kulikowski; Jean-Claude Mugunga; Michael Law; Bethany Hedt-Gauthier – Journal of Statistics and Data Science Education, 2024
During the COVID-19 pandemic, a group of health program implementors and research analysts across seven low- and middle-income countries (LMICs) alongside Boston-based collaborators convened to implement data-driven approaches for public health response. An intensive statistics and data science training short course was developed to ensure that…
Descriptors: Capacity Building, COVID-19, Pandemics, Public Health
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Michael Moore; David Clingenpeel – College and University, 2024
Wake Forest University (WFU) is in the midst of a 28-month student information system (SIS) transition. The authors' offices are deeply involved in this process on a daily basis. As the university collects and analyzes data, discusses operational needs with campus colleagues, builds and configures tenants, tests and validates, and implements a new…
Descriptors: Registrars (School), Universities, Information Systems, Online Systems
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Courtney, Matthew B. – International Journal of Education Policy and Leadership, 2021
Exploratory data analysis (EDA) is an iterative, open-ended data analysis procedure that allows practitioners to examine data without pre-conceived notions to advise improvement processes and make informed decisions. Education is a data-rich field that is primed for a transition into a deeper, more purposeful use of data. This article introduces…
Descriptors: Data Analysis, Data Use, Decision Making, Educational Improvement
Marpaung, Jonathan – ProQuest LLC, 2021
The shortage of qualified administrators who can utilize data analytics means that institutions are not able to harness data analytics to its fullest potential in order to remain competitive in a higher education market that continued to reward institutions that embrace entrepreneurship. Examining how new student affairs professionals in US higher…
Descriptors: Student Personnel Workers, Entry Workers, Readiness, Data Use
Marcia Jean Ham – ProQuest LLC, 2021
Leveraging big data for student data analytics is increasingly integrated throughout university operations from admissions to advising to teaching and learning. Though the possibilities are exciting to consider, they are not without risks to student autonomy, privacy, equity, and educational value. There has been little research showing how…
Descriptors: Educational Policy, Personal Autonomy, Privacy, Equal Education
Leah Dey Cochran Zigmund – ProQuest LLC, 2021
Using data to inform decision-making in the educational environment has increased over the last decade due to accountability requirements at the federal, state, and local level (Bernhardt, 2013; Datnow & Hubbard, 2016; Garland, 2014; Mandinach & Gummer, 2016; McKay, 2018; Wayman, 2005). While research supported the inclusion of data in…
Descriptors: Public School Teachers, Data Use, Decision Making, Teacher Behavior
Molina, Hector M. – ProQuest LLC, 2019
This study seeks to understand the big-data analytics readiness of four-year public and private higher education institutions (HEIs) in North Carolina. The higher education landscape has been experiencing unprecedented challenges including declines in enrollment, graduation rates, and student retention rates. Coupled with cuts in funding at the…
Descriptors: Data Analysis, Readiness, Public Colleges, Private Colleges
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