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Adelson, Jill L.; Dickinson, Emily R.; Cunningham, Brittany C. – Educational Researcher, 2016
This brief examined the patterns of reading achievement using statewide data from all students (Grades 3-10) in multiple years to examine gaps based on student, school, and district characteristics. Results indicate reading achievement varied most between students within schools and that students' prior achievement was the strongest predictor of…
Descriptors: Reading Achievement, Achievement Gap, School Districts, Institutional Characteristics
Mo, Yun; Singh, Kusum; Chang, Mido – Educational Research for Policy and Practice, 2013
This study examined the individual, class, and school level variability of the students' science achievement. It was hypothesized that there are school or teacher effects which contribute toward explaining achievement differences, besides the student level differences. Owing to the nested structure of the data in Trends in International…
Descriptors: Academic Achievement, Science Achievement, Learner Engagement, Grade 8
Magner, Ulrike Irmgard Elisabeth; Glogger, Inga; Renkl, Alexander – Educational Psychology, 2016
How can illustrations motivate learners in multimedia learning? Which features make illustrations interesting? Beside the theoretical relevance of addressing these questions, these issues are practically relevant when instructional designers are to decide which features of illustrations can trigger situational interest irrespective of individual…
Descriptors: Foreign Countries, Illustrations, Multimedia Materials, Multimedia Instruction
Kozina, Ana – Educational Studies, 2015
In this study, we analyse the predictive power of home and school environment-related factors for determining pupils' aggression. The multiple regression analyses are performed for fourth- and eighth-grade pupils based on the Trends in Mathematics and Science Study (TIMSS) 2007 (N = 8394) and TIMSS 2011 (N = 9415) databases for Slovenia. At the…
Descriptors: Aggression, Elementary Schools, Predictive Validity, Educational Environment
Feldman, Betsy J.; Rabe-Hesketh, Sophia – Journal of Educational and Behavioral Statistics, 2012
In longitudinal education studies, assuming that dropout and missing data occur completely at random is often unrealistic. When the probability of dropout depends on covariates and observed responses (called "missing at random" [MAR]), or on values of responses that are missing (called "informative" or "not missing at random" [NMAR]),…
Descriptors: Dropouts, Academic Achievement, Longitudinal Studies, Computation
Faria, Ann-Marie; Greenberg, Ariela; Meakin, John; Bichay, Krystal; Heppen, Jessica – Society for Research on Educational Effectiveness, 2014
Educators have long used test scores to make educational decisions, but only within the last decade has the availability of data been systematic (Abelman, Elmore, Even, Kenyon, & Marshall, 1999). In recent years, interest has spiked in data-driven decision making in education (Marsh, Pane, & Hamilton, 2006). With technological advances and…
Descriptors: Data Analysis, Academic Achievement, Urban Schools, Correlation