Learning Analytics Lab

Learning Analytics (LA) is an interdisciplinary field at the intersection of education, computer science, and data science. It involves the measurement, collection, analysis, and reporting of data about learners and their contexts to understand and optimize learning, as well as the environments in which it occurs. Rather than focusing solely on final grades or standardized tests, Learning Analytics examines the process of learning—using data generated during student interactions to uncover patterns, identify challenges, and enhance instructional design in real time.

Our Scope of Research

The Learning Analytics Lab conducts research across physical, digital, and hybrid learning environments. Our work spans four key domains:

• Computational & Multimodal Analytics: Tracking cognitive, behavioral, and creative learning trajectories—especially within K–12 STEAM and Computational Thinking (CT) contexts.

• Human-Centered Instructional Design: Developing actionable analytics tools, dashboards, and feedback systems that support educators without replacing human intuition.

• Algorithmic Equity & Ethics: Investigating fairness, transparency, and data privacy to ensure learning analytics models serve diverse and underserved student populations equitably.

• Process-Based Assessment: Moving beyond traditional testing by analyzing student problem-solving steps, debugging behaviors, and collaborative engagement.

Mission Statement

To advance educational research by developing human-centered learning analytics and computational tools that uncover deep insights into learning processes, promote equitable educational practices, and empower educators with actionable, data-informed intelligence.

Vision Statement

To lead the future of evidence-based education—where transparent, ethically designed learning analytics transform educational ecosystems into adaptive, joyful, and inclusive spaces for every learner.

Articles

• Huang, Z., Yang, Y. & Gulbahar, Y. (2026). Understanding the interconnected drivers of mathematics test performance: a longitudinal study, Studies in Educational Evaluation, Volume 88, 101539, ISSN 0191-491X. https://doi.org/10.1016/j.stueduc.2025.101539.
• Gulbahar, Y., Öztürk, T., Dagiene, V., Parviainen, M., Güven, I., Bilbao, J. (2025). Evaluating Interactive Tasks through the Lens of Computational and Algebraic Thinking, Interactivity Types, and Multimedia Design Principles. Olympiads in Informatics, Vol. 19, p. 63–86.  https://doi.org/10.15388/ioi.2025.05  

Conference Papers