Résumé
Goal orientation, or achievement orientation, is an "individual disposition towards developing or validating one's ability in achievement settings". In general, an individual can be said to be mastery or performance oriented, based on whether one's goal is to develop one's ability or to demonstrate one's ability, respectively. A mastery orientation is also sometimes referred to as a learning orientation. Goal orientation refers to how an individual interprets and reacts to tasks, resulting in different patterns of cognition, affect and behavior. Developed within a social-cognitive framework, the orientation goal theory proposes that students' motivation and achievement-related behaviors can be understood by considering the reasons or purposes they adopt while engaged in academic work. The focus is on how students think about themselves, their tasks, and their performance. Goal orientations have been shown to be associated with individuals' academic achievement, adjustment, and well-being. Research has examined goal orientation as a motivation variable that is useful for recruitment, climate and culture, performance appraisal, and choice. It has also been used to predict sales performance, adaptive performance, goal setting, learning and adaptive behaviors in training, and leadership. Research on achievement motivation can be traced back to the 1940s following the seminal work of David McClelland and colleagues who established the link between achievement and motivations (see need for achievement). Students' goal orientations were shown to be predictive of academic performance. Specifically, students with high goal orientation tended to value competence, expect success and seek challenges, while students with low achievement motivation tended to expect failure and avoid challenges. In an effort to better understand the mechanisms underlying achievement, personality and social psychology researchers expanded McClelland's work by examining how cognitive representations shape social experiences.
À propos de ce résultat
Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.
Cours associés (32)
MATH-502: Distribution and interpolation spaces
The goal of this course is to give an introduction to the theory of distributions and cover the fundamental results of Sobolev spaces including fractional spaces that appear in the interpolation theor
MATH-351: Advanced numerical analysis
The student will learn state-of-the-art algorithms for solving differential equations. The analysis and implementation of these algorithms will be discussed in some detail.
PHYS-467: Machine learning for physicists
Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practi
Afficher plus
Publications associées (135)