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

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.

The Personalized and Inclusive MOOC: Using Learning Characteristics and Quality Principles in Instructional Design

“…Figure 3 presents the relationship between perspectives and components, and how they are deployed through the application of the criteria and quality principles obtained as a result of the instructional design. To respond to the unique learning needs and characteristics to create a quality and personalized instructional design [21,45], the learning needs, and demands of the operators were identified in previous work [43], and the following profile was determined: Profile Target Learners: Operators have quality internet access at home, as well as access to computers and smartphones. Their educational level is high school degree, and they have between 1 and 5 years of experience as operators.…”

“…Applying a stratified sampling by city, we determined a total sample size of 286 workers, which corresponds to the cities which participated in the project, and 100% of the sample was surveyed. With the collected data, an analysis of principal components and cluster analysis was carried out and the following profile was obtained [43].…”