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Dianna Walla – International Journal of Multilingualism, 2025
This article compares metalinguistic awareness among emerging bilingual and multilingual learners of English in Norwegian primary school. Participants were 120 students in grades 5-7 (aged 10-13) attending mainstream English classes in Norway and were divided into three groups based on a linguistic background questionnaire: an L1 Norwegian group,…
Descriptors: Comparative Analysis, Metalinguistics, Bilingualism, Multilingualism
Ritchie, Stuart – Education Next, 2023
School shootings are at an all-time high. That's according to the National Center for Education Statistics, which has been keeping track of the numbers for about 20 years. What are schools to do? Is there a "profile" of the typical school shooter that could help us identify those who might commit a shooting in the future? Is there some…
Descriptors: Violence, Weapons, Prediction, Student Characteristics
Rodríguez, Patricio; Villanueva, Alexis; Dombrovskaia, Lioubov; Valenzuela, Juan Pablo – Education and Information Technologies, 2023
School dropout is a structural problem which permanently penalizes students and society in areas such as low qualification jobs, higher poverty levels and lower life expectancy, lower pensions, and higher economic burden for governments. Given these high consequences and the surge of the problem due to COVID-19 pandemic, in this paper we propose a…
Descriptors: Foreign Countries, Schools, Dropout Prevention, Methods
Radhakrishnan, Madhubala – International Journal of Mobile and Blended Learning, 2021
Mobile-based learning provides new experience to the learners to learn anything from anywhere and anytime by using their portable or mobile device. Vast educational contents and also different media formats can be supported by the mobile devices. Access speed of those materials has also improved a lot. With this advancement, providing required…
Descriptors: Telecommunications, Handheld Devices, Educational Technology, Technology Uses in Education
Khosravi, Hassan; Shabaninejad, Shiva; Bakharia, Aneesha; Sadiq, Shazia; Indulska, Marta; Gasevic, Dragan – Journal of Learning Analytics, 2021
Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on…
Descriptors: Learning Analytics, Visual Aids, Artificial Intelligence, Information Retrieval
Hlioui, Fedia; Aloui, Nadia; Gargouri, Faiez – International Journal of Web-Based Learning and Teaching Technologies, 2021
Nowadays, the virtual learning environment has become an ideal tool for professional self-development and bringing courses for various learner audiences across the world. There is currently an increasing interest in researching the topic of learner dropout and low completion in distance learning, with one of the main concerns being elevated rates…
Descriptors: At Risk Students, Withdrawal (Education), Dropouts, Distance Education
Mountjoy, Jack; Hickman Brent R. – National Bureau of Economic Research, 2021
Students who attend different colleges in the U.S. end up with vastly different economic outcomes. We study the role of relative value-added across colleges within student choice sets in producing these outcome disparities. Linking high school, college, and earnings registries spanning the state of Texas, we identify relative college value-added…
Descriptors: Value Added Models, Higher Education, State Universities, Decision Making
Fazlul, Ishtiaque; Koedel, Cory; Parsons, Eric – National Center for Analysis of Longitudinal Data in Education Research (CALDER), 2022
Measures of student disadvantage--or risk--are critical components of equity-focused education policies. However, the risk measures used in contemporary policies have significant limitations, and despite continued advances in data infrastructure and analytic capacity, there has been little innovation in these measures for decades. We develop a new…
Descriptors: Academic Achievement, At Risk Students, Prediction, Disadvantaged
Letukas, Lynn – College Board, 2015
The purpose of this document is to identify and dispel rumors that are frequently cited about the SAT. The following is a compilation of nine popular rumors organized into three areas: "Student Demographics," "Test Preparation/Test Prediction," and "Test Utilization."
Descriptors: College Entrance Examinations, Student Characteristics, Test Preparation, Prediction
Perkins-Gough, Deborah – Educational Leadership, 2013
For the last 11 years, Angela Lee Duckworth of the University of Pennsylvania has been conducting ground breaking studies on "grit"--the quality that enables individuals to work hard and stick to their long-term passions and goals. In this interview with "Educational Leadership," Duckworth describes what her research has shown…
Descriptors: Persistence, Goal Orientation, Achievement, Resilience (Psychology)
Powers, Daniel A. – New Directions for Institutional Research, 2012
The methods and models for categorical data analysis cover considerable ground, ranging from regression-type models for binary and binomial data, count data, to ordered and unordered polytomous variables, as well as regression models that mix qualitative and continuous data. This article focuses on methods for binary or binomial data, which are…
Descriptors: Institutional Research, Educational Research, Data Analysis, Research Methodology
Pike, Gary R.; Rocconi, Louis M. – New Directions for Institutional Research, 2012
Multilevel modeling provides several advantages over traditional ordinary least squares regression analysis; however, reporting results to stakeholders can be challenging. This article suggests some strategies for presenting complex, multilevel data and statistical results to institutional and higher education decision makers. The article is…
Descriptors: Learner Engagement, Least Squares Statistics, Critical Thinking, Student Characteristics
Hein, Vanessa; Smerdon, Becky; Sambolt, Megan – College and Career Readiness and Success Center, 2013
The purpose of this brief is to provide information to state, district, and school personnel seeking support to determine whether their students are on a path to postsecondary success. The College and Career Readiness and Success Center (CCRS Center) has received technical assistance requests from a number of states regarding factors that predict…
Descriptors: Predictor Variables, Postsecondary Education, Success, College Readiness
Miller, T. E.; Herreid, C. H. – College and University, 2008
This article presents a project intended to produce a model for predicting the risk of attrition of individual students enrolled at the University of South Florida. The project is premised upon the principle that college student attrition is as highly individual and personal as any other aspect of the college-going experience. Students make…
Descriptors: Academic Persistence, Student Attrition, Admission (School), Regression (Statistics)

van Enckevort, G. – Distance Education, 1984
Argues that needs research generally is not a good means for deciding whether a new institute should be or can be established and provides examples of needs research undertaken by the Staff Bureau for Research and Evaluation of the Dutch Open University. (Author/MBR)
Descriptors: Adult Education, Educational Planning, Foreign Countries, Higher Education
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