The Cushman School — Miami, FL
Students design and conduct original, independent research — producing a 4,000–5,000 word academic paper (75%) and a 15–20 minute oral defense (25%). Willie co-teaches students whose work involves quantitative methods and computational approaches.
Overview
AP Research is the second course in the College Board AP Capstone program. Organized around five Big Ideas, students identify a research question, survey the literature, design a methodology, collect and analyze data, and produce a formal academic paper and oral defense — work equivalent to an undergraduate thesis seminar. There is no May AP Exam; the course is assessed entirely through performance tasks.
Willie co-teaches sections of the course where students are pursuing quantitative, computational, or mixed-methods research. He supports literature review in technical domains, methodology design for data-driven studies, statistical analysis, and computational tools for data collection and processing. The academic paper accounts for 75% of the final score; the oral defense accounts for 25%.
Curriculum
Big Idea 1
Developing a meaningful research question, surveying the existing literature, and identifying a gap in scholarship.
Big Idea 2
Critically evaluating sources; examining methodologies; synthesizing what is known about the research topic.
Big Idea 3
Considering diverse viewpoints, competing theories, and alternative interpretations to strengthen the research argument.
Big Idea 4
Integrating findings and perspectives into a cohesive, evidence-based argument in the academic paper.
Big Idea 5
Collaborating, reflecting on scholarly growth, and communicating research through the academic paper and oral defense.
Based on the AP Research Course and Exam Description.
Resources
Student Work
NLP pipeline measuring political polarity in news text using TF-IDF features, logistic regression, and fine-tuned DistilBERT. Research question: how do algorithmic systems encode political bias?
View on GitHubBrowser-based ML forecasting tool using LSTM, GBM Monte Carlo, and Decision Tree models. Research question: what are the quantitative limits of ML-based prediction in efficient financial markets?
View on GitHub