This paper expands on knowledge of computing identity by building on what is known about prior identity models in science and mathematics education. The model theorizes three primary sub-constructs that contribute to the development of a computing identity: belief in one's performance/competence, interest, and recognition in computing. Drawing on data from a nationally representative survey of more than 1,700 college students at 22 colleges and universities, the study tested the alignment of the theorized model to the measures on the survey. Confirmatory Factor Analysis was used to validate whether the appropriate measures loaded on the three separate sub-constructs. Criterion-related validity was also established by testing whether the computing identity measures predicted the choice of a computer science career. The results reveal that a computing identity proxy based on the theorized measures was a highly significant predictor of students' computer science and information technology career choice (p < 0.0001). In addition, this work also established criterion-related validity by showing gender differences that had been found by prior work in computing. Finally, the theorized measures were found to be reliable and internally consistent. The educational understanding of computing identities may provide an important tool to help researchers and practitioners improve student persistence in computer science.
It is well known that women are underrepresented in science.technology engineering and mathematics (STEM) and that their interest declines more steeply over the schooling years. As such, this study uses a STEM identity theoretical framework to
Abstract. Previous analysis of common exam questions in introductory physics at Florida International University has revealed differences in the number and type of epistemic games played by students in their solutions. Separated by course format (lecture/lab, lecture/lab/recitation, or inquiry-based), student work also shows varying use of multiple representational tools. Here we examine representation use in more detail to establish a descriptive picture of representation use across multiple instructors and course formats. We then compare these profiles with the epistemic games played by students, asking whether the same epistemic game shows the same pattern of representational tools across course types. We find that patterns of representation use vary by course format, but there are generally not clear representational "signatures" to uniquely identify epistemic games.
This study investigates differences in problem-solving performance between three different introductory physics course formats at Florida International University. The course formats-lecture+laboratory (LL), inquiry-based (IQB), and lecture+laboratory+recitation (LLR)-all incorporated two Advanced Placement (AP) questions into their final exams. Students' written responses were evaluated via an AP scoring rubric, and during this scoring, we observed marked differences in solution behavior between the three course formats. To further investigate these differences, we used the framework of epistemic games to analyze student responses. To apply this framework to written work, an epistemic game rubric was created. Application of this rubric yielded game profiles for each of the course formats, allowing us to highlight and compare course characteristics. These profiles of epistemic game distributions were then examined via chi-squared tests to quantify differences in the tools and strategies students used in their solutions.
High School Modeling Workshops are designed to improve high school physics teachers' understanding of physics and how to teach using the Modeling method. The basic assumption is that the teacher plays a critical role in their students' physics education. This study investigated teacher impacts on students' Force Concept Inventory scores, (FCI), with the hopes of identifying quantitative differences between teachers. This study examined student FCI scores from 18 teachers with at least a year of teaching high school physics. This data was then evaluated using a General Linear Model (GLM), which allowed for a regression equation to be fitted to the data. This regression equation was used to predict student post FCI scores, based on: teacher ID, student pre FCI score, gender, and representation. The results show 12 out of 18 teachers significantly impact their student post FCI scores. The GLM further revealed that of the 12 teachers only five have a positive impact on student post FCI scores. Given these differences among teachers it is our intention to extend our analysis to investigate pedagogical differences between them.
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