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Data Quality Campaign, 2024
Recent data from statewide assessments, scores on the National Assessment of Educational Progress (NAEP), and college remediation needs show that an increasing number of K-12 students are not performing at grade level. As schools look to support these students' learning, some districts are turning to a proven strategy for identifying the students…
Descriptors: National Competency Tests, Academic Achievement, Elementary Secondary Education, At Risk Students
Roger Sheng So – ProQuest LLC, 2024
Understanding student engagement with the institution from the first day of classes to the end of the semester would help inform the institution of the potential risk that a student will drop out of a class or of the school. Learning Management Systems (LMS) record student interactions with the system and might be able to be used to identify…
Descriptors: Learning Management Systems, Data Use, At Risk Students, Learner Engagement
Ishtiaque Fazlul; Cory Koedel; Eric Parsons – Brookings Institution, 2024
There have been substantial advances in the development of states' education data systems over the past 20 years, supported by large investments from the federal government. However, the availability of modern data systems has not translated into meaningful improvements in how consequential state policies, such as funding and accountability…
Descriptors: At Risk Students, Public Schools, Elementary Secondary Education, Academic Achievement
Freidenbloom, David Carl – ProQuest LLC, 2023
This phenomenological study sought to examine the lived experiences of Pennsylvania school leaders in elementary or middle schools with an economically disadvantaged student population of 50% or greater and English Language Arts proficiency exceeding 60% as measured by the 2021-2022 Pennsylvania System of School Assessment. Across the Commonwealth…
Descriptors: Elementary Schools, Middle Schools, Economically Disadvantaged, At Risk Students
Huang, Anna Y. Q.; Lu, Owen H. T.; Huang, Jeff C. H.; Yin, C. J.; Yang, Stephen J. H. – Interactive Learning Environments, 2020
In order to enhance the experience of learning, many educators applied learning analytics in a classroom, the major principle of learning analytics is targeting at-risk student and given timely intervention according to the results of student behavior analysis. However, when researchers applied machine learning to train a risk identifying model,…
Descriptors: Academic Achievement, Data Use, Learning Analytics, Classification
Herodotou, Christothea; Rienties, Bart; Boroowa, Avinash; Zdrahal, Zdenek; Hlosta, Martin – Educational Technology Research and Development, 2019
By collecting longitudinal learner and learning data from a range of resources, predictive learning analytics (PLA) are used to identify learners who may not complete a course, typically described as being at risk. Mixed effects are observed as to how teachers perceive, use, and interpret PLA data, necessitating further research in this direction.…
Descriptors: Prediction, Learning Analytics, Teacher Role, Teacher Attitudes
Briesch, Amy M.; Chafouleas, Sandra M.; Dineen, Jennifer N.; McCoach, D. Betsy; Donaldson, Aberdine – Journal of Positive Behavior Interventions, 2022
Research conducted to date provides a limited understanding of the landscape of school-based screening practices across academic, behavioral, and health domains, thus providing an impetus for the current survey study. A total of 475 K-Grade 12 school building administrators representing 409 unique school districts across the United States…
Descriptors: Elementary Secondary Education, School Districts, Screening Tests, Administrator Role
Squires, John; Roberts, Maxine T. – Education Commission of the States, 2021
The growing divides across the educational spectrum all underscore the need to change systems to promote the talents and success of every student, particularly those who are racially minoritized or poverty-impacted within a state. Enacting a process to establish policy that addresses the needs of these student groups can help create the conditions…
Descriptors: Equal Education, Disadvantaged Youth, Economically Disadvantaged, Educational Policy
Briesch, Amy M.; Chafouleas, Sandra M.; Dineen, Jennifer N.; McCoach, D. Betsy; Donaldson, Aberdine – Grantee Submission, 2021
Research conducted to date provides a limited understanding of the landscape of school-based screening practices across academic, behavioral, and health domains, thus providing impetus for the current survey study. A total of 475 K-12 school building administrators representing 409 unique school districts across the United States completed an…
Descriptors: Elementary Secondary Education, School Districts, Screening Tests, Administrator Role
Rao, A. Ravishshankar – Advances in Engineering Education, 2020
Studies show that a significant fraction of students graduating from high schools in the U.S. is ill prepared for college and careers. Some problems include weak grounding in math and writing, lack of motivation, and insufficient conscientiousness. Academic institutions are under pressure to improve student retention and graduate rates, whereas…
Descriptors: Learner Engagement, Student Motivation, Prediction, Academic Achievement
Miller, Cynthia; Cohen, Benjamin; Yang, Edith; Pellegrino, Lauren – MDRC, 2020
College students have a better chance of succeeding in school when they receive high-quality advising. High-quality advising, when characterized by frequent communications between advisers and students, early outreach to students showing signs of academic or nonacademic struggles, and personalized guidance that addresses individual student needs,…
Descriptors: College Students, Academic Advising, Technology Uses in Education, Faculty Advisers
Using Data from Schools and Child Welfare Agencies to Predict Near-Term Academic Risks. REL 2020-027
Bruch, Julie; Gellar, Jonathan; Cattell, Lindsay; Hotchkiss, John; Killewald, Phil – Regional Educational Laboratory Mid-Atlantic, 2020
This report provides information for administrators, researchers, and student support staff in local education agencies who are interested in identifying students who are likely to have near-term academic problems such as absenteeism, suspensions, poor grades, and low performance on state tests. The report describes an approach for developing a…
Descriptors: At Risk Students, Data Use, Child Welfare, Predictor Variables
Bulgakov-Cooke, Dina; Singh, Malkeet – Wake County Public School System, 2018
The Multi-Tiered System of Support (MTSS) framework, which uses a systems approach to promote school improvement and support all students in improving academics and behavior using data-based problem-solving, is a key part of the Wake County Public Schools System (WCPSS) Strategic Plan. As of 2017-18, MTSS schools were at the initial stages of MTSS…
Descriptors: Program Effectiveness, Reading Achievement, Academic Achievement, Student Behavior