Big Data Management

Data management enables treating data as a strategic asset—critical in Big Data.

Data Quality Assessment and Data Integration

These areas collaborate to align data with business goals using advanced techniques.

DQA detects defects across diverse data types, while Data Integration identifies shared entities in heterogeneous datasets; both rely on semantic data, machine learning, and statistical methods to ensure consistency and interoperability. Current interests also include applications involving environmental and geospatial datasets, particularly in heterogeneous monitoring, sustainability, and decision-support contexts.

Computing Education

Innovative educational approaches and technologies to support learning.

Learning Analytics and Educational Data Science

Improving education through data.

This research area investigates the collection, analysis, and interpretation of educational data to better understand learning processes and support evidence-based educational decision-making. It involves the development and evaluation of learning analytics tools capable of monitoring student performance, identifying learning patterns, detecting potential learning difficulties, and providing actionable insights for teachers, schools, and educational managers.

Computing Thinking and AI for Kids

Promoting CT and IA learning in pre-tertiary education.

This research area focuses on the design, implementation, and evaluation of pedagogical interventions aimed at fostering Computational Thinking and foundational Artificial Intelligence literacy among K–12 students. Grounded in diverse learning theories and integrating both unplugged and plugged approaches, these interventions seek to promote meaningful and engaging learning experiences. The research also incorporates Learning Analytics and Educational Data Mining techniques to assess learning outcomes and generate evidence that supports the continuous improvement of educational interventions.