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Improving prediction of students' performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources
The aim of this study was to predict university students' learning performance using different sources of data from an Intelligent Tutoring System.
Multi-source and multimodal data fusion for predicting academic performance in blended learning university courses
The results showed that the best predictions were produced using ensembles and selecting the best attributes approach with discretized data.
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