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WAVE: an architecture for predicting dropout in undergraduate courses using EDM

Published: 24 March 2014 Publication History

Abstract

Predicting the academic progress of student is an issue faced by many public universities in emerging countries. Although, those institutions stores large amounts of educational data, they fail to recognize the students that are in danger to leave the system. This paper presents a novel architecture that uses EDM techniques to predict and to identify those who are at dropout risk. This approach allows academic managers to monitor the progress of the students in each academic semester, identifying the ones in difficult to fulfill their academic requirements. This paper shows initial experimental results using real world data about of three undergraduate engineering courses of one the largest Brazilian public university. According to the experiments, the classifier Naïve Bayes presented the highest true positive rate for all datasets used in the experiments.

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  • (2024)Systematic Review and Analysis of EDM for Predicting the Academic Performance of StudentsJournal of The Institution of Engineers (India): Series B10.1007/s40031-024-00998-0105:4(1021-1071)Online publication date: 4-Feb-2024
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  1. WAVE: an architecture for predicting dropout in undergraduate courses using EDM

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    cover image ACM Conferences
    SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing
    March 2014
    1890 pages
    ISBN:9781450324694
    DOI:10.1145/2554850
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    Published: 24 March 2014

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    Author Tags

    1. academic management system
    2. dropout
    3. educational data mining

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    March 24 - 28, 2014
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    • (2024)Investigação da Evasão Estudantil por meio da Mineração de Dados e Aprendizagem de Máquina: Um Mapeamento SistemáticoRevista Brasileira de Informática na Educação10.5753/rbie.2024.346632(807-841)Online publication date: 10-Mar-2024
    • (2024)Systematic Review and Analysis of EDM for Predicting the Academic Performance of StudentsJournal of The Institution of Engineers (India): Series B10.1007/s40031-024-00998-0105:4(1021-1071)Online publication date: 4-Feb-2024
    • (2023)Educational Data Mining: A Systematic Review on the Applications of Classical Methods and Deep Learning Until 20222023 IEEE Symposium on Industrial Electronics & Applications (ISIEA)10.1109/ISIEA58478.2023.10212273(1-15)Online publication date: 15-Jul-2023
    • (2023)Classification Technique and its Combination with Clustering and Association Rule Mining in Educational Data Mining — A surveyEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106071122(106071)Online publication date: Jun-2023
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    • (2021)Knowledge Discovery for Higher Education Student Retention Based on Data Mining: Machine Learning Algorithms and Case Study in ChileEntropy10.3390/e2304048523:4(485)Online publication date: 20-Apr-2021
    • (2021)Hidden space deep sequential risk prediction on student trajectoriesFuture Generation Computer Systems10.1016/j.future.2021.07.002125:C(532-543)Online publication date: 1-Dec-2021
    • (2020)Analysis of Learning Behavior in an Automated Programming Assessment Environment: A Code Quality PerspectiveIEEE Access10.1109/ACCESS.2020.30241028(167341-167354)Online publication date: 2020
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