Research Portfolio

These projects reflect my approach to translating data into actionable insights that support leaders, systems, and policy decisions.

Featured Project

Evaluating the Impact of AI-Supported Literacy and Teacher Coaching Models

The Problem

School systems are increasingly investing in AI tools and professional learning models, but often lack clear, actionable evidence to determine their impact on student outcomes and instructional practice.

Approach

Led a districtwide program evaluation examining the impact of an AI-supported literacy tool (Microsoft Reading Progress) and an ESE teacher support model by:

  • Using a quasi-experimental matched-group design with Propensity Score Matching (PSM)

  • Comparing student performance outcomes using standardized assessment data

  • Incorporating Student Growth Percentiles (SGPs) to capture changes in learning over time

  • Analyzing multiple implementation models (AI only, ESE support only, and combined approach)

Key Findings

  • No statistically significant differences were observed in achievement outcomes between treatment and comparison groups across most analyses

  • However, students participating in the interventions demonstrated typical or above-typical growth relative to academic peers

  • Findings highlighted the importance of examining growth alongside traditional outcome measures when evaluating program effectiveness

Implications for Practice

  • Program evaluations should incorporate both achievement and growth measures to provide a more complete picture of impact

  • “No significant difference” findings should be interpreted cautiously, particularly in real-world educational settings

  • Leaders can use evaluation results to guide decisions related to program continuation, scaling, and refinement

  • Implementation factors (e.g., duration, participation, teacher training) are critical to understanding program outcomes

Additional Research & Evaluation Work

Additional projects focused on supporting decision-making, program evaluation, and system-level improvement.

  • Teach graduate-level courses in statistics and research methods, supporting students in designing and executing applied research projects.
    → Strengthened practitioner capacity to use data for decision-making and school improvement.

  • Developed and validated machine learning and regression-based models to predict student performance across grades 3–10.
    → Improved early identification of at-risk students and supported data-informed instructional decision-making.

  • Led a quasi-experimental evaluation using hierarchical regression to assess the impact of tutoring services on student outcomes.
    → Informed district-level funding decisions and program refinement.

  • Designed and maintain a Power BI dashboard tracking 600+ metrics across 200+ schools.
    → Accelerated decision-making for district leaders through real-time performance monitoring.

  • Developed a clustering methodology to group schools based on performance profiles.
    → Enabled more targeted interventions and contributed to improved outcomes in identified schools.

Research Focus

My work focuses on using research and evaluation to support decision-making, improve programs, and strengthen outcomes across educational systems.

I am particularly interested in:

  • Program evaluation and research-practice partnerships

  • Data-informed leadership and decision-making

  • Instructional improvement and student outcomes

  • Leadership development and applied research