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

Martín Bustamante-León (), Paúl Herrera, Luis Domínguez-Granda, Tammy Schellens, Peter L. M. Goethals, Otilia Alejandro and Martin Valcke
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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

Sustainability, 2022, vol. 14, issue 21, 1-31

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.

Keywords: learner’s characteristics; massive open online courses; PCA analysis; hierarchical analysis cluster; engineering education (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
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