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This study utilizes a sociocultural interpretation of Self-Determination Theory to better understand the role that learning contexts play in generating student motivation, engagement, and identity. By drawing on previous motivation research we develop a model that describes how student senses of belonging in social settings can transform their goals and experiences. We use the extremes of fear and integrity to model student motivations to engage in activities. Student senses of connection and belonging (or not) in social settings drives whether they feel integrated with or alienated from their environments. Our model is based on three studies and suggests that a sense of belonging emerges through the alignment of goals and practices of the individual and an activity. This model is applied to two examples to illustrate how social connection or isolation can be exhibited in a physics classroom setting. We conclude by discussing the role of the teacher in designing classroom environments that support students engaging.
Students who serve as Learning Assistants (LAs) and have the opportunity to teach the content they are learning, while also studying effective teaching pedagogy, have demonstrated achievement gains in advanced content courses and positive shifts in a
In this study, we investigated the employment status of recent University of Ottawa physics MSc and PhD graduates, finding that 94% of graduates are either employed or pursuing further physics education one year post-graduation. Our database was popu
We report results from a study designed to identify links between undergraduate students views about experimental physics and their engagement in multiweek projects in lab courses. Using surveys and interviews, we explored whether students perceived
This study investigates how an urban, high school physics class responded to the inclusion of a classroom set of iPads and associated applications, such as screencasting. The participatory roles of students and the expressions of their relationships
Physics education research has used quantitative modeling techniques to explore learning, affect, and other aspects of physics education. However, these studies have rarely examined the predictive output of the models, instead focusing on the inferen