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Register a new userFrom Fear to Self-Expression: The Contextual Nature of Physics Student Motivations
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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.
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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 attitudes about learning science [V. Otero, S. Pollock & N. Finkelstein, Amer J Physics 78, 11 (2010)]. Although the LA experience is also valuable for high school students, the tight schedule and credit requirements of advanced high school students limit opportunities for implementing traditional LA programs at the high school level. In order to provide high school physics students with an LA-like experience, iPads were used as tools for students to synthesize screencast video tutorials for students to access, review and evaluate. The iPads were utilized in a one-to-one tablet-to-student environment throughout the course of an entire school year. This research investigates the impact of a one-to-one iPad environment and the use of iPads to create teaching-to-learn (TtL) experiences on student agency and attitudes toward learning science. Project funded by NSF grant # DUE 934921.
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 populated from the public online repository of MSc and PhD theses submitted between the academic years of 2011 to 2019, with employment information collected in 2020 from the professional social media platform LinkedIn. Our results highlight that graduates primarily find employment quickly and in their field of study, with most graduates employed in either academia or physics-related industries. We also found that a significant portion of employed graduates, 20%, find employment in non-traditional physics careers, such as business management and healthcare. Graduates with careers in academia tend to have lower online connectivity compared to graduates with careers in industry or non-traditional fields, suggesting a greater importance for online networking for students interested in non-academic careers.
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 particular classroom activities to be features of experimental physics practice. We focused on 18 activities, including maintaining lab notebooks, fabricating parts, and asking others for help. Interviewees identified activities related to project execution as intrinsic to experimental physics practice based on high prevalence of those activities in interviewees own projects. Fabrication-oriented activities were identified as conditional features of experimentation based on differences between projects, which interviewees attributed to variations in project resources. Interpersonal activities were also viewed as conditional features of experimentation, dependent upon ones status as novice or expert. Our findings suggest that students views about experimental physics are shaped by firsthand experiences of their own projects and secondhand experiences of those of others.
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 to physics were examined. Findings suggest that iPad technology altered classroom norms and student relationships to include increased student agency and use of evidence. Findings also suggest that the iPad provided a connection between physics, social status, and play. Videos, observations, interviews, and survey responses were analyzed to provide insight into the nature of these changes.
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 inferences or causal relationships observed in various data sets. This research introduces a modern predictive modeling approach to the PER community using transcript data for students declaring physics majors at Michigan State University (MSU). Using a machine learning model, this analysis demonstrates that students who switch from a physics degree program to an engineering degree program do not take the third semester course in thermodynamics and modern physics, and may take engineering courses while registered as a physics major. Performance in introductory physics and calculus courses, measured by grade as well as a students declared gender and ethnicity play a much smaller role relative to the other features included the model. These results are used to compare traditional statistical analysis to a more modern modeling approach.
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