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Ting Zhang; Paul Bailey; Yuqi Liao; Emmanuel Sikali – Large-scale Assessments in Education, 2024
The EdSurvey package helps users download, explore variables in, extract data from, and run analyses on large-scale assessment data. The analysis functions in EdSurvey account for the use of plausible values for test scores, survey sampling weights, and their associated variance estimator. We describe the capabilities of the package in the context…
Descriptors: National Competency Tests, Information Retrieval, Data Collection, Test Validity
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Bowen, Natasha K.; Lucio, Robert; Patak-Pietrafesa, Michele; Bowen, Gary L. – Children & Schools, 2020
To support student success effectively, school teams need information on known predictors of youth behavior and academic performance. In contrast to measures of behavioral and academic outcomes that are commonly relied on in schools, the School Success Profile (SSP) for middle and high school students provides comprehensive information on…
Descriptors: Success, Predictor Variables, Behavior, Expectation
Nancy Montes; Fernanda Luna – UNESCO International Institute for Educational Planning, 2024
This article characterizes and reflects on the possible uses of early warning systems (hereafter, EWS) in the region as effective tools to support educational pathways, whenever they identify risks of dropout, difficulties for the achievement of substantive learning, and the possibility of organizing specific actions. This article was developed in…
Descriptors: Data Collection, Data Use, At Risk Students, Foreign Countries
Hoffman, Nancy; O'Connor, Anna; Mawhinney, Joanna – Jobs for the Future, 2022
The purpose of this brief is to provide school-level examples of how early college practitioners are collecting and using data to improve their practices. Examples three and four are school-level data from two early college partnerships: the MetroWest CPC (Framingham, Milford, Waltham), and Lawrence. The brief begins, however, with the national…
Descriptors: College School Cooperation, Partnerships in Education, High Schools, Universities
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Sandlin, Michele – College and University, 2019
This feature focuses on the five areas an institution needs to know before implementing holistic measures. These include: what does a holistic review entail, how to be legally complaint, Sedlacek's noncognitive variables, applying student success measures, and the vital importance of training.
Descriptors: Predictor Variables, Success, Holistic Approach, Compliance (Legal)
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Hughes, John; Petscher, Yaacov – Regional Educational Laboratory Southeast, 2016
The high rate of students taking developmental education courses suggests that many students graduate from high school unready to meet college expectations. A college readiness screener can help colleges and school districts better identify students who are not ready for college credit courses. The primary audience for this guide is leaders and…
Descriptors: College Readiness, Screening Tests, Test Construction, Predictor Variables
Wiggins, Afi Y. – Online Submission, 2015
This supplemental report provides technical documentation for the main report (published separately). A significantly higher percentage of AISD graduates enrolled in postsecondary institutions in 2014 (66%) than enrolled in 2013 (63%). Eighty-one percent of Class of 2013 graduates enrolled and persisted in a postsecondary institution 2 consecutive…
Descriptors: College Enrollment, High School Graduates, School Districts, Academic Persistence
National Forum on Education Statistics, 2018
The Forum Guide to Early Warning Systems provides information and best practices to help education agencies plan, develop, implement, and use an early warning system in their agency to inform interventions that improve student outcomes. The document includes a review of early warning systems and their use in education agencies and explains the…
Descriptors: Educational Indicators, Best Practices, Elementary Secondary Education, Data Collection
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Ren, Zhiyun; Rangwala, Huzefa; Johri, Aditya – International Educational Data Mining Society, 2016
The past few years has seen the rapid growth of data mining approaches for the analysis of data obtained from Massive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a student may achieve on a given grade-related assessment based on information, considered as prior performance or prior…
Descriptors: Large Group Instruction, Online Courses, Educational Technology, Technology Uses in Education
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Sole, Marla A. – Mathematics Teacher, 2016
Every day, students collect, organize, and analyze data to make decisions. In this data-driven world, people need to assess how much trust they can place in summary statistics. The results of every survey and the safety of every drug that undergoes a clinical trial depend on the correct application of appropriate statistics. Recognizing the…
Descriptors: Statistics, Mathematics Instruction, Data Collection, Teaching Methods
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Lei, Wu; Qing, Fang; Zhou, Jin – International Journal of Distance Education Technologies, 2016
There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper presents a recommendation method based on rating prediction using causal association rules.…
Descriptors: Causal Models, Attribution Theory, Correlation, Evaluation Methods
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Wright, Mary C.; McKay, Timothy; Hershock, Chad; Miller, Kate; Tritz, Jared – Change: The Magazine of Higher Learning, 2014
Learning Analytics (LA) has been identified as one of the top technology trends in higher education today (Johnson et al., 2013). LA is based on the idea that datasets generated through normal administrative, teaching, or learning activities--such as registrar data or interactions with learning management systems--can be analyzed to enhance…
Descriptors: STEM Education, Introductory Courses, Physics, Technology Uses in Education
Lawrence, K. S. – National Center on Schoolwide Inclusive School Reform: The SWIFT Center, 2016
This brief describes how to use a free online behavior screener to identify student support needs in middle and high schools. Inclusive Behavior Instruction utilizes data to identify appropriate social-emotional supports for all students. The Lane et al. (2016) study demonstrated system-wide use of a free online behavior screener at the middle and…
Descriptors: Screening Tests, Student Behavior, Behavior Problems, Middle School Students
Finster, Matthew – Teacher Incentive Fund, US Department of Education, 2015
To effectively address teacher turnover, Teacher Incentive Fund (TIF) grantees need to follow an approach that entails aligning the tracking, diagnosing, and intervening processes. Unfortunately, too often retention strategies are implemented without regard to the various types of teacher turnover and specific data about the causes of turnover.…
Descriptors: Teacher Persistence, Faculty Mobility, Labor Turnover, Incentive Grants
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Lee, In Heok – Career and Technical Education Research, 2012
Researchers in career and technical education often ignore more effective ways of reporting and treating missing data and instead implement traditional, but ineffective, missing data methods (Gemici, Rojewski, & Lee, 2012). The recent methodological, and even the non-methodological, literature has increasingly emphasized the importance of…
Descriptors: Vocational Education, Data Collection, Maximum Likelihood Statistics, Educational Research
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