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Moran P. Lee; Abubakir Siedahmed; Neil T. Heffernan – Grantee Submission, 2024
Contextual multi-armed bandits have previously been used to personalize student support messages given to learners by supplying a model with relevant context about the user, problem, and available student supports. In this work, we propose using careful feature selection with relevant domain knowledge to improve the quality of student support…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Reinforcement
Janice D. Gobert; Michael A. Sao Pedro; Haiying Li; Christine Lott – Grantee Submission, 2023
In this entry, we define Intelligent Tutoring Systems (ITSs) and present a description of their core components. We outline a history of the development of ITSs with a focus on key issues that have driven change and innovation in ITSs from their inception to present day. We also present a brief case study on a specific ITS, Inq-ITS (Inquiry…
Descriptors: Intelligent Tutoring Systems, Student Evaluation, Evaluation Methods, Natural Language Processing
Amy Adair; Ellie Segan; Janice Gobert; Michael Sao Pedro – Grantee Submission, 2023
Developing models and using mathematics are two key practices in internationally recognized science education standards, such as the Next Generation Science Standards (NGSS). However, students often struggle with these two intersecting practices, particularly when developing mathematical models about scientific phenomena. Formative…
Descriptors: Artificial Intelligence, Mathematical Models, Science Process Skills, Inquiry
Schussler, Deborah; Frank, Jennifer; Lee, Tsan-Kuang; Mahfouz, Julia – Grantee Submission, 2017
Nearly one in three students in the United States today is negatively impacted by bullying. Teachers can play a critical role in stopping bullying-related violence, but many struggle with how to engage students in difficult conversations. Traditional classroom-based pedagogy used to teach communication skills (e.g., modeling & role-play) is…
Descriptors: Computer Simulation, Artificial Intelligence, Role Playing, Teaching Methods
Benjamin D. Nye; Arthur C. Graesser; Xiangen Hu – Grantee Submission, 2014
AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Computer Software, Artificial Intelligence