Trajectory-based motivational profiles and performance: evidence from the academic context
[eng] The purpose of the present doctoral thesis is to contribute to the research on motivation quality by analyzing longitudinal profiles of different motivational variables and their relationship with performance in higher education setting in Spain. Specifically, this thesis targets the following...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/203171 |
| Acceso en línea: | https://hdl.handle.net/2445/203171 http://hdl.handle.net/10803/689221 |
| Access Level: | acceso abierto |
| Palabra clave: | Motivació en l'educació Educació superior Mètode longitudinal Motivation in education Higher education Longitudinal method |
| Sumario: | [eng] The purpose of the present doctoral thesis is to contribute to the research on motivation quality by analyzing longitudinal profiles of different motivational variables and their relationship with performance in higher education setting in Spain. Specifically, this thesis targets the following objectives: (1) To study the trajectories of different forms of motivation in samples of university students in Spain during a prescribed period of time; (2) to test whether respondents can be grouped based on different configurations of motivation that they experience over time; (3) to explore the qualitative characteristics of the motivational profiles; (4) to analyze the predictive validity of the profiles regarding academic performance. Two empirical studies were conducted in order to address these objectives. The first study (Study 1) was grounded in flow and self-determination (SDT) theories, and applied latent class mixed models analysis in a sample of 291 undergraduate students in order to study (1) whether different patterns of dynamics in academic motivation can be distinguished, and (2) whether the observed patterns are related to students’ performance. Two obtained latent classes were characterized as strong increase and modest increase observed in the studied flow- and SDT-related variables. The comparison of these two groups of students confirmed that those whose motivation increased more sharply over the semester were likely to achieve better performance, compared to the participants whose motivation increased modestly. The second study (Study 2) focused on the continuum of motivation proposed by SDT. In this investigation, data was collected in five waves from a sample of 979 undergraduate students, applying the Multidimensional Work Motivation Scale (MWMS, Gagné et al., 2015) adapted to the academic context. A non-parametric clustering procedure was implemented to investigate whether the respondents could be grouped according their trajectories in MWMS adapted to the academic context. Two profiles of students were observed: Highly motivated (average to high levels of all motivational forms over time, except social external regulation; low amotivation), and Reward oriented (high but slightly decreasing external-material regulation; moderate and decreasing identified and introjected regulation and intrinsic motivation; low and increasing external-social regulation and amotivation). Students in the Highly motivated profile achieved better performance. They were also characterized by higher levels of perceived competence and perceived challenge over a course of the semester, compared to the respondents in the Reward oriented profile. Furthermore, Study 2 was the first to consider both facets of external regulation, material and social, in the educational setting, and provided evidence of different evolution of these two forms of motivation in a sample of undergraduate students. The results of the aforementioned studies stress the importance of person- centered longitudinal research to detect different patterns of motivational evolution in the academic context which, in turn, are useful to predict academic outcomes. |
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