arXiv subjects

Remy Dou

Publications and source records attributed to Remy Dou.

8 recordsLinked to original sources

arXiv2026

Concurrent Phenomenological Analysis of STEM Career Aspirations in Underrepresented Youth: Role of Experiences and Identity

Despite numerous initiatives aimed at enhancing diversity and achieving equity in the STEM workforce, racially and ethnically minoritized individuals remain underrepresented in STEM disciplines and the STEM workforce. While many factors influence STEM identity (i.e., seeing oneself as a STEM person), it strongly correlates with individuals' future STEM career choices. This phenomenological qualitative study explores the impact of formal or informal STEM-related recognition that influences minority youths' aspirations to pursue a STEM career. Results indicate that though misrecognition negatively impacts one's motivation to study STEM areas, lack of recognition in formal schooling contexts was sometimes mitigated by recognition and support from family. The study suggests providing targeted interventions to facilitate underrepresented youths' achievements in STEM to foster a strong STEM identity and STEM career aspirations.

physics.ed-ph↗

arXiv2026

Associations Between Event-Based (Mis)Recognition by STEM Authorities with STEM Identity and STEM Career Aspirations

Several programs for marginalized young people have been intentionally designed to increase the diversity in STEM careers; however, the participation of racially and ethnically minoritized individuals continued to be underrepresented in STEM disciplines. Using discipline-based STEM identity theories and drawing data from an NSF-funded survey of 1,134 participants, these regression analyses examine the impact of prior educational experiences on STEM identity (i.e., seeing oneself as a STEM person) and career aspirations among individuals attending Minority Serving Institutions (MSIs). The study's findings indicate that although positive reinforcement positively correlates with the STEM identity construct, the efforts are not always supportive enough to predict STEM career aspirations for minoritized individuals. It underscores the importance of explicitly designing appropriate interventions to support STEM identity formation and STEM career pursuit.

physics.ed-ph↗

arXiv2022

Understanding the development of interest and self-efficacy in active-learning undergraduate physics courses

Modeling Instruction (MI), an active-learning introductory physics curriculum, has been shown to improve student academic success. Peer-to-peer interactions play a salient role in the MI classroom. Their impact on student interest and self-efficacy -- preeminent constructs of various career theories -- has not been thoroughly explored. Our examination of three undergraduate MI courses (N=221) revealed a decrease in students' physics self-efficacy, physics interest, and general science interest. We found a positive link from physics interest to self-efficacy, and a negative relationship between science interest and self-efficacy. We tested structural equation models confirming that student interactions make positive contributions to self-efficacy. This study frames students' classroom interactions within broader career theory frameworks and suggests nuanced considerations regarding interest and self-efficacy constructs in the context of undergraduate active-learning science courses.

physics.ed-ph↗

arXiv2018

Beyond performance metrics: Examining a decrease in students' physics self-efficacy through a social network lens

The Modeling Instruction (MI) approach to introductory physics manifests significant increases in student conceptual understanding and attitudes toward physics. In light of these findings, we investigated changes in student self-efficacy while considering the construct's contribution to the career-decision making process. Students in the Fall 2014 and 2015 MI courses at Florida International University exhibited a decrease on each of the sources of self-efficacy and overall self-efficacy (N = 147) as measured by the Sources of Self-Efficacy in Science Courses-Physics (SOSESC-P) survey. This held true regardless of student gender or ethnic group. Given the highly interactive nature of the MI course and the drops observed on the SOSESC-P, we chose to further explore students' changes in self-efficacy as a function of three centrality measures (i.e., relational positions in the classroom social network): inDegree, outDegree, and PageRank. We collected social network data by periodically asking students to list the names of peers with whom they had meaningful interactions. While controlling for PRE scores on the SOSESC-P, bootstrapped linear regressions revealed post-self-efficacy scores to be predicted by PageRank centrality. When disaggregated by the sources of self-efficacy, PageRank centrality was shown to be directly related to students' sense of mastery experiences. InDegree was associated with verbal persuasion experiences, and outDegree with both verbal persuasion and vicarious learning experiences. We posit that analysis of social networks in active learning classrooms helps to reveal nuances in self-efficacy development.

physics.ed-ph↗

arXiv2018

Practitioner's guide to social network analysis: Examining physics anxiety in an active-learning setting

The application of social network analysis (SNA) has recently grown prevalent in science, technology, engineering, and mathematics education research. Research on classroom networks has led to greater understandings of student persistence in physics majors, changes in their career-related beliefs (e.g., physics interest), and their academic success. In this paper, we aim to provide a practitioner's guide to carrying out research using SNA, including how to develop data collection instruments, set up protocols for gathering data, as well as identify network methodologies relevant to a wide range of research questions beyond what one might find in a typical primer. We illustrate these techniques using student anxiety data from active-learning physics classrooms. We explore the relationship between students' physics anxiety and the social networks they participate in throughout the course of a semester. We find that students' with greater numbers of outgoing interactions are more likely to experience negative anxiety shifts even while we control for {\it pre} anxiety, gender, and final course grade. We also explore the evolution of student networks and find that the second half of the semester is a critical period for participating in interactions associated with decreased physics anxiety. Our study further supports the benefits of dynamic group formation strategies that give students an opportunity to interact with as many peers as possible throughout a semester. To complement our guide to SNA in education research, we also provide a set of tools for letting other researchers use this approach in their work -- the {\it SNA toolbox} -- that can be accessed on GitHub.

physics.ed-ph↗

arXiv2017

Student perceptions of the value of out-of-class interactions: Attitudes vs. Practice

From industry to government to academia, attracting and retaining science, technology, engineering, and mathematics majors is recognized as a key element of the 21$^{\text{st}}$ century knowledge economy. The ability to retain students seems to be intimately tied with understanding their immersion into the academic and social system of an institution. For instance, it has been noted that insufficient interactions with peers can lead to a low commitment to the university and, ultimately, affect one's decision about whether to drop out. Since nearly half of first-time students who leave a university by the end of the freshman year never come back to college, the importance of understanding experiences in introductory courses as a means for improving students' persistence is particularly pronounced. We investigate students' experiences in an introductory physics courses, focusing on their self-reported perception of the values of out-of-class collaborations. We find that, even though students consider the out-of-class collaborations to be important for success, it takes a relatively long time before they start practicing collaborative learning.

physics.ed-ph↗

arXiv2017

Linking engagement and performance: The social network analysis perspective

Theories developed by Tinto and Nora identify academic performance, learning gains, and involvement in learning communities as significant facets of student engagement that, in turn, support student persistence. Collaborative learning environments, such as those employed in the Modeling Instruction introductory physics course, provide structure for student engagement by encouraging peer-to-peer interactions. Because of the inherently social nature of collaborative learning, we examine student interactions in the classroom using network analysis. We use centrality---a family of measures that quantify how connected or "central" a particular student is within the classroom network---to study student engagement longitudinally. Bootstrapped linear regression modeling shows that students' centrality predicts future academic performance over and above prior GPA for three out of four centrality measures tested. In particular, we find that closeness centrality explains 28 % more of the variance than prior GPA alone. These results confirm that student engagement in the classroom is critical to supporting academic performance. Furthermore, we find that this relationship for social interactions does not emerge until the second half of the semester, suggesting that classroom community develops over time in a meaningful way.

physics.ed-ph↗

arXiv2017

Students' network integration as a predictor of persistence in introductory physics courses

Increasing student retention (successfully finishing a particular course) and persistence (continuing through a sequence of courses or the major area of study) is currently a major challenge for universities. While students' academic and social integration into an institution seems to be vital for student retention, research into the effect of interpersonal interactions is rare. We use network analysis as an approach to investigate academic and social experiences of students in the classroom. In particular, centrality measures identify patterns of interaction that contribute to integration into the university. Using these measures, we analyze how position within a social network in a Modeling Instruction (MI) course -- an introductory physics course that strongly emphasizes interactive learning -- predicts their persistence in taking a subsequent physics course. Students with higher centrality at the end of the first semester of MI are more likely to enroll in a second semester of MI. Moreover, we found that chances of successfully predicting individual student's persistence based on centrality measures are fairly high -- up to $75\%$, making the centrality a good predictor of persistence. These findings suggest that increasing student social integration may help in improving persistence in science, technology, engineering, and mathematics fields.

physics.ed-ph↗