NEUROCIENCIA COGNITIVA
NEUROCIENCIA COGNITIVA
KU Leuven
Lovaina, BélgicaPublicaciones en colaboración con investigadores/as de KU Leuven (12)
2023
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Field-based upper-body motor variability as determinant of stroke performance in the main tennis strokes
Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology
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Predicting Effortful Control at 3 Years of Age from Measures of Attention and Home Environment in Infancy: A Machine Learning Approach
Children, Vol. 10, Núm. 6
2022
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Fronto-parietal homotopy in resting-state functional connectivity predicts task-switching performance
Brain Structure and Function, Vol. 227, Núm. 2, pp. 655-672
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Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach
Mind, Brain, and Education, Vol. 16, Núm. 4, pp. 300-317
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Personality and mental health: Factors impacting perceived health risks and protective behaviors during the early COVID-19 quarantine
Cognition, Brain, Behavior. An Interdisciplinary Journal , Vol. 26, Núm. 1, pp. 37-65
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Predicting attribution of letter writing performance in secondary school: A machine learning approach
Frontiers in Education, Vol. 7
2021
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Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation
Computers and Education: Artificial Intelligence, Vol. 2
2020
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Identifying Reliable Predictors of Educational Outcomes Through Machine-Learning Predictive Modeling
Frontiers in Education, Vol. 5
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Predicting key educational outcomes in academic trajectories: a machine-learning approach
Higher Education, Vol. 80, Núm. 5, pp. 875-894
2019
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Individual differences in basic cognitive processes and self-regulated learning: Their interaction effects on math performance
Learning and Individual Differences, Vol. 71, pp. 58-70
2014
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Modelling for understanding and for prediction/classification-the power of neural networks in research
Frontline Learning Research, Vol. 2, Núm. 5, pp. 67-81
2013
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Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks
Frontline Learning Research, Vol. 1, Núm. 1, pp. 42-71