BACKGROUND: The aim of the study was to analyze the influence of potentially important individual variables (motivation, satisfaction with the course, self-regulation, expectations of self-efficacy and perception of academic performance) on the intention to remain on university courses. METHOD: An ex-post-facto design was used, applying the University Life Questionnaire to a sample of 2,741 first-year students from six Chilean universities. Data were analyzed by path analysis. RESULTS: The intention to remain is higher when intrinsic motivation is higher, self-efficacy expectations are higher, the perception of performance is higher and satisfaction with the course is higher. All the variables included in the model explained 26% of the intention to remain. CONCLUSIONS: The study confirms the suitability of studying this phenomenon through complex models (e.g., structural equation models, multilevel models), since it makes little sense to try to explain dropout only through direct effects (as in most previous research). Secondly, the percentage of variance explained by dropout intention means it is important to continue this kind of research (with better controls, other types of measures, etc.).
BACKGROUND: The aim of the study was to analyze the influence of potentially important individual variables (motivation, satisfaction with the course, self-regulation, expectations of self-efficacy and perception of academic performance) on the intention to remain on university courses. METHOD: An ex-post-facto design was used, applying the University Life Questionnaire to a sample of 2,741 first-year students from six Chilean universities. Data were analyzed by path analysis. RESULTS: The intention to remain is higher when intrinsic motivation is higher, self-efficacy expectations are higher, the perception of performance is higher and satisfaction with the course is higher. All the variables included in the model explained 26% of the intention to remain. CONCLUSIONS: The study confirms the suitability of studying this phenomenon through complex models (e.g., structural equation models, multilevel models), since it makes little sense to try to explain dropout only through direct effects (as in most previous research). Secondly, the percentage of variance explained by dropout intention means it is important to continue this kind of research (with better controls, other types of measures, etc.).
Authors: Fotios S Milienos; Christos Rentzios; Leen Catrysse; David Gijbels; Sofia Mastrokoukou; Claudio Longobardi; Evangelia Karagiannopoulou Journal: Front Psychol Date: 2021-04-21