Two-stage cluster analysis in distance learning: A way to reduce gaps in the scientific literature on open and distance education

• Autor(a) principal: Desjardins, Guillaume
• Data de Publicação: 2021
• Outros Autores: Papi, Cathia, Gérin-Lajoie, Serge, Sauvé, Louise
• Tipo de documento: Artigo
• Idioma: eng; por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://doi.org/10.31211/rpics.2021.7.2.230
• topic: Curso online; Ensino à distância; Ensino superior; Perseverança; Análise cluster; online course; distance education; higher education; perseverance; cluster analysis

Background: Dropout rates are often very high in distance education. A plethora of research has been conducted to identify the contributing factors; however, the majority of the findings are inconclusive and point to the fact that it is difficult to isolate a single explanatory factor. While frequently examined factors are personal and environmental, there is less research on the relationship between course design and retention or dropout. Method: This paper presents a study involving two-stage cluster analysis of 623 variables from 19 university courses at one open and distance education (ODE) institution. To this end, the current study grouped the courses into five types based on 22 variables. Results: The results indicate that certain sociodemographic variables become a risk factor for course dropout depending on their distribution in the standard courses. Conclusions: This result highlights the importance of instructional design in the ODE retention and dropout equation and helps explain, in part, why previous studies have not reached a consensus on which variables should be considered to explain dropout rates.