Multi-source and multimodal data fusion for predicting academic performance in blended learning university courses

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dc.careerEscuela de Ingeniería en Sistemases
dc.category.authorprincipalen_US
dc.contributor.authorChango Sailema, Wilson Gustavo
dc.contributor.correspondingChango Sailema, Wilson Gustavo
dc.countryEcuadores
dc.date.accessioned2023-11-04T21:32:36Z
dc.date.available2023-11-04T21:32:36Z
dc.date.issued2021-01
dc.dedication.authorTCes
dc.description.abstractIn this paper we apply data fusion approaches for predicting the final academic performance of university students using multiple-source, multimodal data from blended learning environments. We collect and preprocess data about first-year university students from different sources: theory classes, practical sessions, on-line Moodle sessions, and a final exam. Our objective is to discover which data fusion approach produces the best results using our data. We carry out experiments by applying four different data fusion approaches and six classification algorithms. The results show that the best predictions are produced using ensembles and selecting the best attributes approach with discretized data. The best prediction models show us that the level of attention in theory classes, scores in Moodle quizzes, and the level of activity in Moodle forums are the best set of attributes for predicting students’ final performance in our courses.en_US
dc.facultyIngenieríaes
dc.id.author1803126679
dc.id.type1
dc.identifier.doihttps://doi.org/10.1016/j.compeleceng.2020.106908
dc.identifier.issn0045-7906
dc.identifier.urihttps://repositorio.puce.edu.ec/handle/123456789/5052
dc.identifier.urihttps://www.sciencedirect.com/science/article/abs/pii/S0045790620307606
dc.indexed.databaseOtheres
dc.language.isoen
dc.list.authorsChango, W., Cerezo, R., & Romero, C.
dc.magazine.pageRange1-20
dc.magazine.titleComputers & Electrical Engineeringen_US
dc.magazine.volumeChapter89
dc.rightsOpenAccessen
dc.statepublisheden_US
dc.subjectTeoría de la predicciónes
dc.subjectRendimiento académicoes
dc.subjectAprendizaje combinadoes
dc.subjectTeoría de la predicción
dc.subjectRendimiento académico
dc.subjectAprendizaje combinado
dc.titleMulti-source and multimodal data fusion for predicting academic performance in blended learning university coursesen_US
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