Data Mining and Learning Analytics
From Hidden Markov Models and Differential Sequence Mining to Multi-Modal Learning Analytics, we aim to advance the state-of-the-field in educational data analysis.
Collaborative, Problem-Based Learning in STEM
We leverage multiple data modalities to better understand how students co-construct knowledge and problem-solving skills in OELEs.
Co-Design of AI in Educational Technology
Our lab works closely with edtech stakeholders, including teachers and students, to design AI-backed technologies that best support them in the classroom.
Teachable Agents to Support STEM Learning
We aim to advance research on how learning and cognition is improved when a student teaches an agent concepts within a given domain.
Metacognition
Our lab examines relationships between metacognitive, motivational and affective aspects of SRL and their influence on student learning and performance in OELEs.
Gender Diversity in CS
It’s no secret – we need more female computer scientists. To support this, we examine HCI and STEM engagement to better design more inclusive software.