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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.

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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.

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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.

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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.

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Our lab examines relationships between metacognitive, motivational and affective aspects of SRL and their influence on student learning and performance in OELEs.

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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.

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