Motivation:
My passion for understanding the quantitative aspects of education has driven me to explore the relationship between data and effective teaching strategies. My research interests lie in the use of statistical techniques to improve the measurement, evaluation, and application of educational technologies. By testing, evaluating, and applying quantitative analysis to educational data, I aim to address theoretical issues related to the development of measurement techniques and provide practical insights that improve educational outcomes.
My journey from undergraduate to graduate studies has been marked by a consistent growth in my expertise in quantitative analysis. During my undergraduate years, my research led to a publication titled 'Analysis of Sentiment Optimization on Social Networks Based on Statistical Data,' presented at the 2021 IEEE International Conference on Computer Science, Artificial Intelligence, and Electronic Engineering. This work demonstrated my foundational skills in statistical analysis, data cleaning, and modeling. It also allows me to apply data analysis into the real-world solutions.
Building on these skills, my graduate research at Cornell University involved using more advanced statistical methodologies to identify, e.g. trends in the healthcare workforce, providing me with valuable experience in complex data analysis and visualization. I am motivated to transfer these skills to the educational sector to evaluate and enhance educational interventions.
Relevance:
Quantitative Analysis: Throughout my academic and professional career, I have been deeply involved in applying quantitative methods into different real-world problems. At Cornell University, I conducted extensive research using linear regression and machine learning to identify significant trends in large datasets, including healthcare workforce data, which gave me experience in handling complex data and developing effective statistical models.
Advanced Data Analysis Techniques: My research projects have consistently utilized advanced statistical techniques, including multivariate analysis, regression modeling, and machine learning. I have a strong background in using statistical software like R, SAS for data analysis, which has helped me derive insights into educational effectiveness and develop predictive models. These experiences have helped me develop a keen understanding of how quantitative methods can be used to measure the effectiveness of educational tools and interventions.
Professional Experience: In my professional role at Looper Developments, I focused on utilizing quantitative methods to analyze the impact of educational technologies on students' career advising experiences. I led surveys involving students and career advisors, using statistical analysis to derive actionable insights. My work included developing interactive dashboards that employed data visualization techniques to help students better understand their career trajectories and make informed decisions. These experiences allowed me to see firsthand the impact of data-driven decision-making in an educational setting.