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About IES

Sandra Katz

Associated IES Content

Grant

Linking Dialogue and Student Modeling to Create an Enhanced Micro-adaptive Tutoring System

In this project, the research team will continue to develop and test Rimac, a tutorial dialogue system for physics developed with prior IES funding (Improving a Natural-Language Tutoring System that Engages Students in Deep Reasoning Dialogues about Physics) and currently serving as the test bed for a current exploration project (Exploring Studies to Derive Policies for Adaptive Natural-Language Tutoring in Physics). The researchers will create and incorporate a student modeling engine into ...
Federal funding program:
Education Research Grants
Award number:
R305A150155
Grant

Exploring Studies to Derive Policies for Adaptive Natural-Language Tutoring in Physics

Students' failure to grasp basic scientific concepts and apply these concepts to problem solving has been a persistent challenge, especially in physics education. Many educators and education policymakers have looked to intelligent tutoring systems (ITSs) as a means of providing cost-effective, individualized instruction to students with the potential to improve their conceptual understanding of and problem-solving skills in math and science. The key to developing highly effective, adaptive ...
Federal funding program:
Education Research Grants
Award number:
R305A130441
Grant

Improving a Natural-Language Tutoring System that Engages Students in Deep Reasoning Dialogues about Physics

Recent studies show that U.S. students lag behind students in other developed countries in math and science. Because one-on-one tutoring has been shown to be a highly effective form of instruction, many educators and education policy makers have looked to intelligent tutoring systems (ITSs) as a means of providing cost-effective, individualized instruction to students that can improve their conceptual understanding of and problem-solving skills in math and science. However, even though many ...
Federal funding program:
Education Research Grants
Award number:
R305A100163
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