IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i21p14206-d959590.html
   My bibliography  Save this article

Toward a More Personalized MOOC: Data Analysis to Identify Drinking Water Production Operators’ Learning Characteristics—An Ecuador Case

Author

Listed:
  • Martín Bustamante-León

    (Facultad de Ciencias Sociales y Humanísticas, Escuela Superior Politécnica del Litoral, Campus Gustavo Galindo, Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil 090902, Ecuador)

  • Paúl Herrera

    (Facultad de Ciencias de la Vida, Escuela Superior Politécnica del Litoral, Guayaquil 090902, Ecuador)

  • Luis Domínguez-Granda

    (Department of Sustainable Water Management, Escuela Superior Politécnica del Litoral, P.O. Box 09-01-5863, Guayaquil 090902, Ecuador)

  • Tammy Schellens

    (Department of Educational Sciences, Ghent University, 9000 Ghent, Belgium)

  • Peter L. M. Goethals

    (Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium)

  • Otilia Alejandro

    (Facultad de Ingeniería en Electricidad y Computación, Escuela Superior Politécnica del Litoral, P.O. Box 09-01-5863, Guayaquil 090902, Ecuador)

  • Martin Valcke

    (Department of Educational Sciences, Ghent University, 9000 Ghent, Belgium)

Abstract

Only 35% of the Ecuadorian population consumes drinking water of “assured quality”. One of the causes is related to the deficiencies in the technical ability of the operators due to their lack of education, technical training, and experience. Massive open online courses (MOOCs) responsive to characteristics and learning needs are an option to strengthen the skills of operators. The goal of the present study is therefore to describe a methodology that includes the application of a survey and the use of statistical methods such as categorical principal component analysis (CATPCA) and cluster analysis to identify and assess learning characteristics. The results present the most frequent variables in the personal, academic, emotional, social, and cognitive aspects. They also show the preferences and learning needs of the operators. Finally, it is concluded that this study identifies common learning characteristics, needs, and preferences that are relevant for the creation of a quality personalized instructional design in MOOCs.

Suggested Citation

  • Martín Bustamante-León & Paúl Herrera & Luis Domínguez-Granda & Tammy Schellens & Peter L. M. Goethals & Otilia Alejandro & Martin Valcke, 2022. "Toward a More Personalized MOOC: Data Analysis to Identify Drinking Water Production Operators’ Learning Characteristics—An Ecuador Case," Sustainability, MDPI, vol. 14(21), pages 1-31, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14206-:d:959590
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/21/14206/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/21/14206/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. José A. Ruipérez-Valiente & Sergio Martin & Justin Reich & Manuel Castro, 2020. "The UnMOOCing Process: Extending the Impact of MOOC Educational Resources as OERs," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    2. Chao Li & Hong Zhou, 2018. "Enhancing the Efficiency of Massive Online Learning by Integrating Intelligent Analysis into MOOCs with an Application to Education of Sustainability," Sustainability, MDPI, vol. 10(2), pages 1-16, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jesús Maya & Juan F. Luesia & Javier Pérez-Padilla, 2021. "The Relationship between Learning Styles and Academic Performance: Consistency among Multiple Assessment Methods in Psychology and Education Students," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    2. Bernardo Tabuenca & Marco Kalz & Ansje Löhr, 2019. "Massive Open Online Education for Environmental Activism: The Worldwide Problem of Marine Litter," Sustainability, MDPI, vol. 11(10), pages 1-16, May.
    3. Sergiu Cosmin Nistor & Tudor Alexandru Ileni & Adrian Sergiu Dărăbant, 2020. "Automatic Development of Deep Learning Architectures for Image Segmentation," Sustainability, MDPI, vol. 12(22), pages 1-18, November.
    4. Serdar Türkeli & Martine Schophuizen, 2019. "Decomposing the Complexity of Value: Integration of Digital Transformation of Education with Circular Economy Transition," Social Sciences, MDPI, vol. 8(8), pages 1-22, August.
    5. Eva Gómez-Llanos & Pablo Durán-Barroso, 2020. "Learning Design Decisions in Massive Open Online Courses (MOOC) Applied to Higher Education in Civil-Engineering Topics," Sustainability, MDPI, vol. 12(20), pages 1-13, October.
    6. Ling Wang & Gongliang Hu & Tiehua Zhou, 2018. "Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education," Sustainability, MDPI, vol. 10(6), pages 1-19, June.
    7. Chiou-Jye Huang & Ping-Huan Kuo, 2018. "A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems," Energies, MDPI, vol. 11(10), pages 1-20, October.
    8. Muhammad Imran & Saman Hina & Mirza Mahmood Baig, 2022. "Analysis of Learner’s Sentiments to Evaluate Sustainability of Online Education System during COVID-19 Pandemic," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    9. Cecilia Temilola Olugbara & Moeketsi Letseka & Oludayo O. Olugbara, 2021. "Multiple Correspondence Analysis of Factors Influencing Student Acceptance of Massive Open Online Courses," Sustainability, MDPI, vol. 13(23), pages 1-21, December.
    10. María Consuelo Sáiz-Manzanares & Raúl Marticorena-Sánchez & Javier Ochoa-Orihuel, 2020. "Effectiveness of Using Voice Assistants in Learning: A Study at the Time of COVID-19," IJERPH, MDPI, vol. 17(15), pages 1-20, August.
    11. Chi-Cheng Chang & Chin-Guo Kuo & Yu-Hsuan Chang, 2018. "An Assessment Tool Predicts Learning Effectiveness for Project-Based Learning in Enhancing Education of Sustainability," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    12. María José Sosa-Díaz & María Rosa Fernández-Sánchez, 2020. "Massive Open Online Courses (MOOC) within the Framework of International Developmental Cooperation as a Strategy to Achieve Sustainable Development Goals," Sustainability, MDPI, vol. 12(23), pages 1-23, December.
    13. Walter Leal Filho & Elizabeth Price & Tony Wall & Chris Shiel & Ulisses M. Azeiteiro & Mark Mifsud & Luciana Brandli & Carla Sofia Farinha & Sandra Caeiro & Amanda Lange Salvia & Claudio Ruy Vasconcel, 2021. "COVID-19: the impact of a global crisis on sustainable development teaching," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 11257-11278, August.
    14. Ishteyaaq Ahmad & Sonal Sharma & Rajesh Singh & Anita Gehlot & Neeraj Priyadarshi & Bhekisipho Twala, 2022. "MOOC 5.0: A Roadmap to the Future of Learning," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    15. Jacqmin, Julien, 2022. "Why are some Massive Open Online Courses more open than others?," Technovation, Elsevier, vol. 112(C).
    16. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks," Sustainability, MDPI, vol. 10(4), pages 1-17, April.
    17. Martín Bustamante-León & Paúl Herrera & Luis Domínguez-Granda & Tammy Schellens & Peter L. M. Goethals & Otilia Alejandro & Martin Valcke, 2022. "The Personalized and Inclusive MOOC: Using Learning Characteristics and Quality Principles in Instructional Design," Sustainability, MDPI, vol. 14(22), pages 1-17, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14206-:d:959590. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.