@inproceedings{be6241151fab46bf96e8f62dd4e0fe16,
title = "Looking for the best data fusion model in Smart Learning Environments for detecting at risk university students",
abstract = "This paper proposes to discover which data fusion approach and classification algorithm produced the best results from smart classrooms data, and how useful would be the prediction models for detecting University students at risk of failing or dropout. The results showed that the best predictions were produced using ensembles and selecting the best attributes approach with discretized data; the REPTree algorithm demonstrated the highest prediction values. The best predictions also show the teacher what set of attributes and values are the most important for predicting student performance, such as the level of attention in theory classes, scores in Moodle quizzes and the level of activity in Moodle forums.",
keywords = "Data fusion, smart classroom, student prediction models",
author = "Wilson Chango and Rebeca Cerezo and Crist{\'o}bal Romero",
note = "Publisher Copyright: {\textcopyright} 2022 Copyright is held by the author(s).; 15th International Conference on Educational Data Mining, EDM 2022 ; Conference date: 24-07-2022 Through 27-07-2022",
year = "2022",
doi = "10.5281/zenodo.6853089",
language = "English",
isbn = "9781733673631",
series = "Proceedings of the International Conference on Educational Data Mining",
publisher = "International Educational Data Mining Society",
editor = "[given-name]Antonija Mitrovic and Nigel Bosch",
booktitle = "Proceedings of the 15th International Conference on Educational Data Mining, EDM 2022",
address = "United States",
}