Computation and big data can be used to revolutionize education. They enable studying the behavior of online learners in ways that were not possible before, and using that to inform the design of better content and learning environments, and to develop tools and mechanisms for personalized teaching and learning. That’s the focus of my research, which combines the learning sciences, artificial intelligence, data science, and human-computer interaction. Main keywords that are associated with this type of research are educational data mining, learning analytics, and artificial-intelligence in education. In addition, I am interested in computer science education, and in the design and analysis of algorithms.
Quality and Accessibility.Online learning has also the potential of scaling education while reducing its costs. Thus, by improving the pedagogy of online-learning environments, I hope to contribute to making quality science education more accessible and affordable.
Key topics that my research centers on are i) data-driven, scientifically-based research for better pedagogy in online science education environments; ii) recommending educational content to learners in real-time, using algorithms and data-driven methods; iii) designing formal representations of subject-matter knowledge that are amenable for algorithmization; iv) developing methods for online tracing of learners’ knowledge and other relevant factors (e.g., engagement).
Application domains. The application domains on which I focus are digital learning environments for K12 science education, ranging from Intelligent Tutoring Systems designed to teach specific concepts, to Massive Open Online Courses (MOOCs) that offer a complete course environment.
Multi-disciplinary research. My research spans across science education research, the learning sciences, computational approaches, statistical modeling, AI, and programming. I conduct applied research that is directly applicable to online learning tools, and collaborate with developers of digital science education content to ensure that this research brings measurable pedagogic value to local and global learners.
Join my team! I am always looking for bright students, with a Background in Computer Science (Algorithms, Machine Learning, AI), Cognitive and Behavioral Sciences, and hands-on programming experience (preferably in R/Python).
Community involvement. In addition to my research activity, I work with local school systems, NGOs, and initiatives that aim to promote under-represented communities.