Peter W. Foltz - US grants

This project combines relevant, recent findings from psychology, education, artificial intelligence, and computational linguistics in learning and knowledge representation and applies them to the development of intelligent tutoring systems (ITSs). Learning theory can be extended through the study of the evolution of different types of knowledge representations together with procedural representations. Thus, this research will examine learning, the change in students' knowledge representations, as they integrate multiple sources of information.

Although ITSs have been developed to examine learning for well-defined problems requiring a limited set of possi-ble responses, ITSs have had problems evaluating open-ended questions that require the student to provide natural language responses. However, asking students to provide written answers to essay questions can provide a much richer representation of student knowledge and also improving students' writing abilities. Thus, this research will demonstrate that ITSs can be developed which extend beyond simple responses and that these ITSs can adapt more readily to provide individualized instruction to students. In order to develop these ITSs, this research uses the computational linguistic technique Latent Semantic Analysis (LSA) to model student knowledge representations from essay-based answers within tutoring systems.

As students' knowledge representations change, so too should their essay responses, including more knowledge and improving in quality. Therefore such an ITS can provide the necessary feedback for student improvement (both in domain knowledge and in writing skill). In addition, the ITS can initially assess the students' ability and provide extra help to the students with help gradually fading as the student gains more knowledge (e.g., scaffold-ing). Thus, by incorporating LSA into an ITS, individualized instruction is possible for adjusting both the level of feedback provided and the content presented. The goal of this research is thus to study the acquisition of complex skills through incorporating LSA, a method for representing students' knowledge representations through their essays, into intelligent tutoring systems.

Because this research focuses on complex skills from knowledge-rich domains, results from this project extend theories of learning in psychology by providing information about how multiple sources of knowledge are integrated into students' knowledge representations as they are learning . The research also has theoretical implications for educational psychology including the systematic examination of scaffolding for two content areas. In addition, because LSA is an automatic method for deriving knowledge representations, this research has broad implications for developing systems for training in any area of content knowledge.

This proposal, from a MSI institution in an EPCoR state, develops a face-to-face interaction room to enable improving the degree of analytical precision available in studies of naturalistic sociolinguistic interaction. The research aims at better quantifying the cognitive, physiological, and behavioral correlates of both deception, and the ability to detect deception. Addressing an understudied Homeland Security need (the ability of interviews to detect deception in one-on-one and group interviews), the work specifically seeks to design
Training methods, to train humans to better detect deception in interviews, and
Automatic quantitative measures that may aid an interviewer in detecting deception.
Studying visual attention, PIs will conduct experimental studies of cognitive and neural correlates of deception, and of deception detection, in the context of interviews and conversations. They will study EEG correlates of executive function and rapport development, coded video to detect nonverbal language associated with deception, and language-use correlates of deception using latent semantic analysis.
Broader Impact: Consistent with current national priorities in today's climate of social uncertainty, this minority serving institution addresses issues in homeland security. The lab will also be used to train students to conduct research into multi-modal language understanding.