Research Overview
My research explores how technology can enhance learning, assessment, and student success in education. I work at the intersection of Educational Technology, Learning Analytics, Tangible User Interfaces (TUI), Human–Computer Interaction (HCI), and Applied Machine Learning. My goal is to design intelligent and inclusive learning environments that bridge abstract concepts with real-world practice, particularly for students from diverse and underserved backgrounds.
Research Philosophy
My research is grounded in the belief that technology is not neutral—it shapes human experience and can either widen or reduce inequalities. Working in the Brazilian Amazon has shown me how computing education can transform lives, especially for students facing systemic barriers. I design research that integrates rigor, innovation, and social impact, aiming to create learning technologies that are inclusive, accessible, and meaningful.
Research Areas
Tangible User Interfaces for Learning
I investigate how physical-digital interactions can support the understanding of abstract concepts in computing and mathematics. My doctoral work introduced a method based on tangible objects for learning assessment, demonstrating gains in engagement, conceptual clarity, and retention. This line of research focuses on embodied cognition, active learning, and low-cost educational technologies.
Learning Analytics & Educational Data Mining
I develop data-driven methods to model student performance, generate personalized feedback, and support instructors in decision-making. My work includes statistical modeling of learning metrics, Item Response Theory (IRT)-based assessment, and analytics pipelines for educational platforms. I am particularly interested in how learning data can reduce dropout rates and improve equity in computing education.
Human–Computer Interaction in Education
My research in HCI examines how interface design, usability, and interaction models influence learning outcomes. This includes intelligent tutoring systems, recommendation engines for educational content, and the design of digital ecosystems that support teachers and learners. I focus on creating accessible and intuitive systems that accommodate diverse learning profiles.
Applied Machine Learning & Embedded Systems
I explore ML applications in education, health, and human activity recognition, often integrating sensors and embedded systems. Past work includes ML-based detection of human activities, mobile testing automation, and the use of microcontrollers to make programming concepts more concrete for beginners.