Research
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.
We leverage multiple data modalities to better understand how students co-construct knowledge and problem-solving skills in OELEs.
Our lab works closely with edtech stakeholders, including teachers and students, to design AI-backed technologies that best support them in the classroom.
We aim to advance research on how learning and cognition is improved when a student teaches an agent concepts within a given domain.
Our lab examines relationships between metacognitive, motivational and affective aspects of SRL and their influence on student learning and performance in OELEs.
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.