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Sustainable e-Learning by Data Mining—Successful Results in a Chilean University

Author

Listed:
  • Aurora Sánchez

    (Department of Administration, Universidad Católica del Norte, Angamos 0610, Antofagasta 1270709, Chile)

  • Cristian Vidal-Silva

    (Faculty of Engineering, School of Videogame Development and Virtual Reality Engineering, University of Talca, Talca 3460000, Chile)

  • Gabriela Mancilla

    (Department of Administration, Universidad Católica del Norte, Angamos 0610, Antofagasta 1270709, Chile)

  • Miguel Tupac-Yupanqui

    (EAP, Ingeniería de Sistemas e Informática, Universidad Continental, Huancayo 12000, Peru)

  • José M. Rubio

    (Escuela de Computación e Informática, Facultad de Ingeniería, Ciencia y Tecnología, Universidad Bernardo O’Higgins, Santiago 8370993, Chile)

Abstract

People are increasingly open to using online education mainly to break the distance and time barriers of presential education. This type of education is sustainable at all levels, and its relevance has increased even more during the pandemic. Consequently, educational institutions are saving large volumes of data containing relevant information about their operations, but they do not know why students succeed or fail. The Knowledge Discovery in Databases (KDD) process could support this challenge by extracting innovative models to identify the main patterns and factors that could affect the success of their students in online education programs. This work uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to analyze data from the Distance Education Center of the Universidad Católica del Norte (DEC-UCN) from 2000 to 2018. CRISP-DM was chosen because it represents a proven process that integrates multiple methodologies to provide an effective meta-process for data knowledge projects. DEC-UCN is one of the first centers to implement online learning in Chile, and this study analyses 18,610 records in this period. The study applies data mining, the most critical KDD phase, to find hidden data patterns to identify the variables associated with students’ success in online learning (e-learning) programs. This study found that the main variables explaining student success in e-learning programs are age, gender, degree study, educational level, and locality.

Suggested Citation

  • Aurora Sánchez & Cristian Vidal-Silva & Gabriela Mancilla & Miguel Tupac-Yupanqui & José M. Rubio, 2023. "Sustainable e-Learning by Data Mining—Successful Results in a Chilean University," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:895-:d:1024430
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    References listed on IDEAS

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    1. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, April.
    2. Jesús Valverde-Berrocoso & María del Carmen Garrido-Arroyo & Carmen Burgos-Videla & María Belén Morales-Cevallos, 2020. "Trends in Educational Research about e-Learning: A Systematic Literature Review (2009–2018)," Sustainability, MDPI, vol. 12(12), pages 1-23, June.
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    Cited by:

    1. Lihong Zhao & Jiaolong Ren & Lin Zhang & Hongbo Zhao, 2023. "Quantitative Analysis and Prediction of Academic Performance of Students Using Machine Learning," Sustainability, MDPI, vol. 15(16), pages 1-18, August.
    2. Munaf Salim Najim Al-Din, 2024. "Students’ Academic Performance Prediction Using Educational Data Mining and Machine Learning: A Systematic Review," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(8), pages 1264-1291, August.

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