Xin Xia

Research Projects

Skills

• Qualitative Research

• Semi-structured Interviews

• Thematic analysis

• Deductive coding

• IRB writing

• Dedoose

LSAME Students Conference Experience

The LSAMP (Louis Stokes Alliances for Minority Participation) is a program funded by the National Science Foundation (NSF) aimed at increasing the number of underrepresented minority students who earn degrees in science, technology, engineering, and mathematics (STEM) fields. The program focuses on academic enrichment, research opportunities, and mentorship to support students from undergraduate through to graduate levels, enhancing their academic and professional development. The LSAMP conference provides opportunities for academic presentation, communication, and development. 

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• Objective: This study aims to explore the experience of groups underrepresented in the conference and understand their research journey. Through semi-structured interviews, we examined how the students' science identities formed and how their experience engaged students in independent scientific research. The research provides valuable insights for developing informal STEM programs. 

• Methodology:​

  • Semi-structured Interviews

  • Thematic analysis

  • Deductive coding

Work in Progress

Skills

• Qualitative Research

• Semi-structured Interview

• Thematic analysis

• Deductive coding

• IRB writing

• Dedoose

High School Students' Science Fair Experience
Science fairs are platforms where students present their science projects and experiments. These fairs provide opportunities for students to engage in scientific research, present their findings, and communicate with peers and professionals.

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• Objective: The study aims to explore underrepresented minority students' experiences in science fairs, focusing on their engagement in independent scientific research and the impact of these experiences on their science identity. We conducted semi-structured interviews with high school students, gathering current and retrospective insights into their participation in science fairs. Our goal is to understand what supports underrepresented minority students in conducting independent STEM research and how these experiences influence their perspectives on pursuing long-term careers in STEM fields. The research provides information for information STEM educational practices and policies that can enhance student engagement in STEM; foster a sustained interest in scientific research, and ultimately contribute to the development of a skilled and passionate STEM workforce.

• Methodology:​

  • Semi-structured Interviews

  • Thematic analysis

  • Deductive coding

Work in Progress

Skills

• Quantitative Research

• Survey Analysis

• Multilevel Modeling 

• R

FOCIS Survey Analysis

• Objective: The framework for observing and categorizing instructional strategies (FOCIS) survey is an instrument to measure students’ preferences in different kinds of activities in science learning (Tai et al, 2012). The instrument captures students' perceptions of science instruction. This study aims to understand the correlation between students' perceptions of science instruction and their engagement in science learning.

• Data: The study employed multilevel modeling to analyze survey data collected from 6,465 students across 25 schools.

• Methodology:

  • Multilevel Modeling ​

Work in Progress

Skills

• Quantitative Research

• Literature Review

• Random Effects Model

• R

Informal STEM Education Meta-Analysis
Informal STEM education refers to learning experiences that occur outside the traditional classroom setting, such as in museums, science centers, after-school programs, clubs, camps, and other community-based activities. 

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• Objective: To better understand the field, we conducted a comprehensive meta-analysis examining the impact of informal science education on students' attitudes toward STEM learning. By synthesizing data from numerous studies, our research highlights the significant role that out-of-school experiences play in fostering a positive perception and enthusiasm for STEM subjects among students. Our findings advocate more quantitative research with the duration and dosage information of informal STEM learning. 

• Methodology:

  • Random Effects Model

Work in Progress

Skills

• Qualitative Research

• Methodology

• Thematic analysis

• Deductive coding

• Dedoose

Large Language Modeling Qualitative Methodology Development

• Objective: The increasing use of machine learning and Large Language Models (LLMs) opens up opportunities to use these artificially intelligent algorithms in novel ways. Our latest research proposes a methodology leveraging LLMs to enhance traditional deductive coding in qualitative research.

• Methodology:

  • Analyzed three different sample texts from existing interviews.

  • Utilized a codebook to input sample texts into an LLM, instructing it to identify codes and provide supporting evidence.

  • Iterated this process 160 times to simulate multiple coders analyzing the text deductively.

• Findings:

  • Compared LLM's coding with human coder evaluations.

  • Demonstrated the potential of LLMs to aid qualitative researchers by providing systematic and reliable code identification.

  • Highlighted the ability of LLMs to minimize analysis misalignment.