This research will advance a novel technological approach that relies on machine learning techniques in general and Natural Language Processing (NLP) in particular to develop models and support for creativity during collaborative science, technology, engineering, and mathematics (STEM) educational activities. We will extend existing educational software with NLP capabilities to automatically assess and subsequently support creativity during collaborative tasks. The research questions are: (1) Which factors influence moment-by-moment creativity during collaborative problem solving activities? (2) How can NLP be used to build student models that detect those factors? (3) How can an ITS use this information to create personalized interventions to support creativity?
The first phase in this research will collect data from students solving problems in pairs with an educational application to identify factors that are relevant to creativity processes and outcomes. These data will be used to derive computational student models for automatically assessing student creativity in terms of both moment-to-moment processes and outcomes through machine learning methodologies focusing on an NLP approach. In addition to providing automatic assessment, the models will also inform factors that influence creativity during collaboration through educational data mining techniques. The final phase of the work will design and test a set of interventions to foster creativity during collaborative activities.
Using data corresponding to pairs of students solving open-ended STEM-based problems, this research will develop a rich and nuanced understanding of creativity processes and outcomes in collaborative contexts, and how these relate to knowledge, affect and creative thinking styles. Relying on that understanding, it will develop and evaluate novel student models that recognize salient, creativity-related events through NLP techniques, as well as personalized support for creativity during collaborative activities and evaluating that support through an experiment with university students. This project will pave the way for a new class of collaborative cyberlearning technologies to both assess and foster creativity, through just-in-time personalized support based on easily deployed NLP-based student models.
