DATE: Thu, Mar 16, 2017
TIME: 1 pm
TITLE: Applications of Models Learned from Data in Instructional Contexts
PRESENTER: Kasia Muldner
Carleton University, Institute for Cognitive Science

Tutoring systems are educational technologies that support students through personalized instruction. To this end, these technologies rely on models that recognize student states relevant to instructional activities, such as creativity, affect, and effort. In this talk, I will describe our work on building models from data collected in studies involving students engaged in various instructional activities. I will focus on a series of analyses involving the automatic extraction of linguistic features from collaborative problem-solving activities to build models that predict student creativity. I will also touch on work modelling other student characteristics, including affect and effort-related outcomes.