We conduct research in artificial intelligence (particularly natural language processing and machine learning) and in human-computer interaction. Our researchers build learning technologies and analyze educational data from such technologies, e.g., intelligent tutoring systems, computer-supported collaborative learning systems, automated writing evaluation systems, and robotic learning environments. In addition, we implement principles of learning and teaching to understand when and why educational technology is effective and rectify it when it isn't.
Additionally, we study the use of mobile applications in large STEM lectures to monitor and enhance student reflection, in medical settings to provide paramedic students with access to practice when not in the field, and in classrooms to support the teaching of statistics.
Our research has created Web-based writing environments that use computational linguistics to score student essays and to provide formative feedback to guide essay revision. We have also used computational linguistics to create analytics to improve the teaching of collaborative argumentation, and to succinctly summarize legal cases for lay users.
We investigate the use of social robots to support collaborative learning and study how the relationship between learners and robots influences knowledge acquisition and use. We are part of the emerging discipline of data mining, the use of educational datasets to better understand students and settings in which they learn.
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Researchers Associated with this Area
Lindsay Clare Matsumura
Luis Perez Cortes
LRDC Research AreasCognitive & Neural Foundations of Learning Developmental Processes & Outcomes Educational Opportunities, Equity, & Attainment Improvement Research in Education Informal & Life-long Learning Learning Technology Motivation & Engagement Reading & Language Reasoning, Decision Making, & Argumentation STEM
- Cognitive Science Learning Laboratory
- Pitt EducaTional And Language Technology lab (PETAL)
- Schneider Lab
- Schunn Lab
- Technology-enhanced Language Interactions for Learning (TELL)
- Walker Lab