Discrimination of the Contextual Features of Top Performers in Scientific Literacy Using a Machine Learning Approach
Authors:
- 28 March 2019
- journal article
- research article
- Published by Springer Nature in Research in Science Education
- Vol. 51 (S1) , 129-158
Abstract
No abstract availableKeywords
This publication has 73 references indexed in Scilit:
- An exploration of the gap between highest and lowest ability readers across 20 countriesEducational Studies, 2013
- Predicting business failure using support vector machines with straightforward wrapper: A re-sampling studyExpert Systems with Applications, 2011
- LIBSVMACM Transactions on Intelligent Systems and Technology, 2011
- Prediction of Individual Brain Maturity Using fMRIScience, 2010
- Exploring the within- and between-class correlation distributions for tumor classificationProceedings of the National Academy of Sciences, 2010
- A Comparison of Support Vector Machine and Decision Tree Classifications Using Satellite Data of Langkawi IslandInformation Technology Journal, 2008
- Using data mining to predict K–12 students' performance on large‐scale assessment items related to energyJournal of Research in Science Teaching, 2008
- Estimation for Multiple Phase Samples2003
- Application of support vector machines in financial time series forecastingOmega, 2001
- The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions.American Psychologist, 2001