Predicting academic performance of university students from multi-sources data in blended learning

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dc.careerEscuela de Ingeniería en Sistemases
dc.category.authorprincipalen_US
dc.contributorChango Sailema, Wilson Gustavo
dc.contributor.authorChango Sailema, Wilson Gustavo
dc.date2019-12-02
dc.date.accessioned2023-11-04T21:24:34Z
dc.date.available2023-11-04T21:24:34Z
dc.dedication.authorTCes
dc.description.abstractIn this paper, we propose to predict academic performance of university students from multi-sources data in multimodal and blended learning environments using data fusion and data mining. We have gathered data from 65 university students and different variables from four different sources. Firstly, we apply data fusion and preprocessing for creating a summary dataset in numerical and categorical format. Then, we have applied different white box classification algorithms provided by Weka data mining tool in order to select the best algorithm. Finally, we show the best predicting model in order to help instructor to take remedial actions with students at risk of dropout or failing.en_US
dc.event.cityDubaies
dc.event.countryEmiratos Árabes Unidoses
dc.event.nameDATA'19: International Conference on Data Science, E-learning and Information Systems 2019es
dc.event.paperPredicting academic performance of university students from multi-sources data in blended learningen_US
dc.event.publicationDATA'19: International Conference on Data Science, E-learning and Information Systems 2019en_US
dc.facultyIngenieríaes
dc.id.author1803126679
dc.id.type1
dc.identifier.urihttps://repositorio.puce.edu.ec/handle/123456789/4212
dc.indexed.databaseEventoes
dc.language.isoen
dc.list.authorsChango, W., Cerezo, R., & Romero, C.
dc.rightsOpenAccessen
dc.titlePredicting academic performance of university students from multi-sources data in blended learningen_US
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