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Kirsty Wilding; Megan Wright; Sophie von Stumm – Educational Psychology Review, 2024
Recent advances in genomics make it possible to predict individual differences in education from polygenic scores that are person-specific aggregates of inherited DNA differences. Here, we systematically reviewed and meta-analyzed the strength of these DNA-based predictions for educational attainment (e.g., years spent in full-time education) and…
Descriptors: Genetics, Heredity, Educational Attainment, Predictor Variables
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Melisa Diaz Lema; Melvin Vooren; Marta Cannistrà; Chris van Klaveren; Tommaso Agasisti; Ilja Cornelisz – Studies in Higher Education, 2024
Study success in Higher Education is of primary importance in the European policy agenda. Yet, given the diverse educational landscape across countries and institutions, more coordinated action is needed to gain a more solid knowledge of the dropout phenomenon. This study aims to gain a better insight into students' dropout based on an integrated…
Descriptors: Foreign Countries, Dropout Research, College Students, Dropouts
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Adetona, Abel Adekanmi – International Journal of Evaluation and Research in Education, 2017
The study aimed at assessing how students and teachers factor taken together influence students' achievement in Statistics as well as their relative contribution to the prediction. Two research questions were raised and purposive sampling was adopted to select national diploma year 2 students since they are already in their final level in the…
Descriptors: Teacher Student Relationship, Statistics, Academic Achievement, Foreign Countries
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Chen, Yu; Upah, Sylvester – Journal of College Student Retention: Research, Theory & Practice, 2020
Science, Technology, Engineering, and Mathematics student success is an important topic in higher education research. Recently, the use of data analytics in higher education administration has gain popularity. However, very few studies have examined how data analytics may influence Science, Technology, Engineering, and Mathematics student success.…
Descriptors: STEM Education, Academic Advising, Data Analysis, Majors (Students)
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Kayri, Murat – Educational Sciences: Theory and Practice, 2015
The objective of this study is twofold: (1) to investigate the factors that affect the success of university students by employing two artificial neural network methods (i.e., multilayer perceptron [MLP] and radial basis function [RBF]); and (2) to compare the effects of these methods on educational data in terms of predictive ability. The…
Descriptors: Artificial Intelligence, Influences, Academic Achievement, College Students
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Leckie, George; Goldstein, Harvey – British Educational Research Journal, 2017
Since 1992, the UK Government has published so-called "school league tables" summarising the average General Certificate of Secondary Education (GCSE) "attainment" and "progress" made by pupils in each state-funded secondary school in England. While the headline measure of school attainment has remained the percentage…
Descriptors: Foreign Countries, Achievement Rating, Academic Achievement, Secondary School Students
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Johnston, Ron; Manley, David; Jones, Kelvyn; Harris, Richard; Hoare, Anthony – Higher Education Quarterly, 2016
The United Kingdom's Department for Education has recently changed the nature of the AS-level examinations normally taken by students aspiring to enter higher education degree courses one year into their post-compulsory education. In the face of protests from universities and other institutions that this would both harm students' progression…
Descriptors: Foreign Countries, College Admission, Predictor Variables, Higher Education
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Alkharusi, Hussain – Electronic Journal of Research in Educational Psychology, 2016
Introduction: Students are daily exposed to a variety of assessment tasks in the classroom. It has long been recognized that students' perceptions of the assessment tasks may influence student academic achievement. The present study aimed at predicting academic achievement in mathematics from perceptions of the assessment tasks after controlling…
Descriptors: Academic Achievement, Predictive Measurement, Predictor Variables, Learning Strategies
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Williamson, Ben – Journal of Education Policy, 2016
Educational institutions and governing practices are increasingly augmented with digital database technologies that function as new kinds of policy instruments. This article surveys and maps the landscape of digital policy instrumentation in education and provides two detailed case studies of new digital data systems. The Learning Curve is a…
Descriptors: Visualization, Synchronous Communication, Governance, Data Collection
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Stadler, Matthias J.; Becker, Nicolas; Greiff, Samuel; Spinath, Frank M. – Higher Education Research and Development, 2016
Successful completion of a university degree is a complex matter. Based on considerations regarding the demands of acquiring a university degree, the aim of this paper was to investigate the utility of complex problem-solving (CPS) skills in the prediction of objective and subjective university success (SUS). The key finding of this study was that…
Descriptors: Success, Predictive Validity, Predictor Variables, Problem Solving
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Conijn, Rianne; Snijders, Chris; Kleingeld, Ad; Matzat, Uwe – IEEE Transactions on Learning Technologies, 2017
With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students' online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables…
Descriptors: Blended Learning, Predictor Variables, Predictive Validity, Predictive Measurement
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Berlin, Noémi; Tavani, Jean-Louis; Beasançon, Maud – Education Economics, 2016
We investigate the link between schooling achievement and creativity scores, controlling for personality traits and other individual characteristics. Our study is based on field data collected in a secondary school situated in a Parisian suburb. Four scores of creativity were measured on 9th graders. Verbal divergent thinking negatively predicts…
Descriptors: Creativity, Academic Achievement, Scores, Personality Traits
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Peters, S. Colby; Woolley, Michael E. – Children & Schools, 2015
Data from the School Success Profile generated by 19,228 middle and high school students were organized into three broad categories of risk and protective factors--control, support, and challenge--to examine the relative and combined power of aggregate scale scores in each category so as to predict academic success. It was hypothesized that higher…
Descriptors: Academic Achievement, Success, Risk, Risk Assessment
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Wladis, Claire; Conway, Katherine M.; Hachey, Alyse C. – Online Learning, 2016
This study explored the interaction between student characteristics and the online environment in predicting course performance and subsequent college persistence among students in a large urban U.S. university system. Multilevel modeling, propensity score matching, and the KHB decomposition method were used. The most consistent pattern observed…
Descriptors: Online Courses, Electronic Learning, Learning Readiness, Student Characteristics
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Huang, Shaobo; Fang, Ning – Computers & Education, 2013
Predicting student academic performance has long been an important research topic in many academic disciplines. The present study is the first study that develops and compares four types of mathematical models to predict student academic performance in engineering dynamics--a high-enrollment, high-impact, and core course that many engineering…
Descriptors: Academic Achievement, Grade Point Average, Accuracy, Prediction
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