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Past Projects

AutoTutor [PI: Art Graesser]

AutoTutor is an intelligent tutoring system that helps students learn about computer literacy or physics by holding a conversation in natural language. AutoTutor appears as an animated agent that acts as a dialog partner with the learners. The animated agent delivers AutoTutor's dialog moves with synthesized speech, intonation, facial expressions, and gestures.

AutoCommunicator [PI: Art Graesser and Xiangen Hu]

This project is developing a question answering system that allows faculty, students, and the public to learn about technology transfer and the relevant research projects at the University of Memphis. The system will be one of the Web faculties for the FedEx Institute of Technology site. The user accesses relevant information to their queries by asking a question in natural language and engaging in a brief dialogue with AutoCommunicator until an answer is found. An animated conversational agent is available to guide the dialog.


Coh-Metrix [PI: Danielle McNamara]

Coh-Metrix is a system for computing computational cohesion and coherence metrics for written and spoken texts, using advanced methods that are widely used in computational linguistics. Coh-Metrix allows readers, writers, educators, and researchers to instantly gauge the difficulty of written text for the target audience.

Deep Tutor [PI: Vasile Rus] 

The researchers are developing an innovative intelligent tutoring system that is intended to improve student outcomes in science relative to current state-of-the art tutoring systems. DeepTutor’s hallmark features are deep natural language and discourse processing, advanced tutoring strategies targeting frequent illusions in tutoring, and advanced instructional strategies in the form of learning progressions.

Guru [PI: Andrew Olney]

Guru models the strategies and dialogue of expert human tutors and is a logical progression from AutoTutor, which models novice human tutors. The Guru expert tutor, by using expert human tutor strategies, actions, and dialogue, should promote larger learning gains than previous novice computer tutors. Guru could have a big impact on Memphis City Schools because it is designed to improve educational outcomes on the Tennessee Gateway Science Test, which high school students must pass to receive a diploma.

iDrive [PI: Barry Gholson]

This project implements vicarious learning strategies wherein learners observe virtual tutoring sessions with conversational agents and multimedia learning environments. The agents ask and answer deep-level questions that facilitate constructive learning in labs and classroom instruction. Exposure to deep-level reasoning questions improves the number and quality of questions asked that are critical to establish interactive knowledge construction. Dialogs with deep-level reasoning questions and also interactive AutoTutor tutoring sessions improved learning over equivalent content presented at a monolog for middle- and high-school aged students.


iMAP [PI: Max Louwerse]

The iMAP (Intelligent MapTask Agent) project investigates multimodal communication in humans and agents, focusing on linguistic modalities (prosody and dialog structure) that reflect major communicative events, and nonlinguistic modalities (eye gaze, facial expressions, and gesture).


iSTART [PI: Danielle McNamara]

iSTART (Interactive Strategy Trainer for Active Reading and Thinking) is an automated strategy trainer designed to help students become better readers via multi-media technologies. Pedagogical agents provide students with interactive and adaptive training to use active reading strategies.

Languages Across Cultures [PI: James Pennebaker]

This project investigates the language and discourse patterns of English and Arabic texts using computerized text analysis tools. Specifically, the researchers are interested in analyzing discourse patterns in various corpora such as newspapers, speeches, and conversations to elucidate the leadership style, personality, and social status of leaders. In addition to English and Arabic, analyses will be performed on Korean, Chinese, and other languages. We will use computational tools that automatically analyze texts on hundreds of measures of language and text cohesion (using Coh-Metrix), including word characteristics, syntax complexity, lexical diversity, readability, connectives, latent semantic analysis, co-referential cohesion, mental model dimensions, and genre.

MetaTutor [PI: Roger Azevedo]

MetaTutor is a new multi-agent, hypermedia-based intelligent tutoring system that is designed to improve the effectiveness of animated pedagogical agents (APAs) as external regulatory agents in the learning of the circulatory system. A mixed-initiative intelligent tutoring system similar to AutoTutor simulates the discourse patterns and pedagogical strategies of human tutors. The underlying assumption of MetaTutor is that students should regulate key cognitive, metacognitive, motivational, social, and affective processes to learn complex science topics. The design of MetaTutor is based on extensive research by Azevedo and colleagues showing that adaptive human scaffolding that addresses both the content of the domain and the processes of self-regulated learning enhances students' learning of challenging science topics with hypermedia.

Plate Tectonics [PI: Art Graesser]

This project investigates the impact of a Web tutor on helping college students' identify true versus false bodies of knowledge while exploring Web pages to research the causes of the eruption of Mt. St. Helens. The Web tutor (called SEEK, an acronym for Source, Evidence, Explanation, and Knowledge) was designed to improve a critical stance through several facilities in a computer environment: spoken hints on a mock Google search page, on-line ratings on the reliability of particular Web sites, and a structured note-taking facility that prompted them to reflect on the quality of particular Web sites.

Quaid Tool [PI: Art Graesser]

A computer model of human question understanding (called QUEST) helps survey designers identify problems with questions on a Web-based tool called QUAID (Question Understanding Aid). QUAID is a software tool that assists survey methodologists, social scientists, and designers of questionnaires in improving the wording, syntax, and semantics of questions. QUAID is being used by six government agencies.

Sandia Labs [PI: Sidney D'Mello]

Working in collaboration with researchers at Sandia National Laboratories and the University of Notre Dame, UM researchers will identify skills that may differentially affect performance of individual humans in cognitive tasks relevant to flying airplanes and communicating with team members. The project will either identify or develop measures to quantify individual ability with respect to each identified skill. A battery of tests will be administered to experimental test participants to assess their relative abilities to predict task performance.

Writing Pal [PI: Danielle McNamara]

This project develops a new automated intelligent tutoring system that provides interactive and adaptive strategy training that encourages students to use independent writing techniques. The W-Pal will be evaluated with high school students and English teachers from urban and suburban schools in Memphis. The goal is to provide a tool that provides writing strategy instruction via automated technologies, which offer tutoring that mimics human one-on-one tutoring. The W-Pal allows teachers to provide adaptive one-on-one tutoring, not to a few students in the classroom, but to all of the students in the classroom. As such, this research will significantly impact the educational community by providing an automated instructional writing tool that can potentially benefit students across the nation.

ALEKS [PI: Xiangen Hu]

This study examines the efficacy of using the ALEKS (Assessment and LEarning in Knowledge Spaces) system as a method of strategic intervention in after-school settings to improve the mathematical skills of struggling 6th grade students in the Jackson-Madison County School System in Jackson, TN. ALEKS is a web-based, artificial intelligent assessment and learning system that uses adaptive questioning to quickly and accurately determine exactly what a student knows and does not know in a course. To ensure that topics learned are retained in long-term memory, ALEKS periodically reassesses the student, using the results to adjust the student’s lesson plan. Because students must demonstrate mastery through mixed question assessments that cannot be predicted, mastery on ALEKS represents true content mastery. This study will focus on sixth grade students who are in the bottom 40th percentile in math achievement.

ARIES [PI: Art Graesser]

ARIES (Acquiring Research Investigative and Evaluative Skills) is a learning environment with conversational agents that hold trialogs with the human learner who is acquiring critical reasoning skills on science. The learner holds conversations with two animated pedagogical agents while solving a number of engaging problems in the social and physical sciences. ARIES is embedded in a game environment with an electronic textbook, multiple choice questions, and the trialogs. Science teachers in both high school and higher institutions could assign ARIES as homework, if they decide not to devote class time to it.

GIFT [PI: Xiangen Hu]

The ultimate goal of this multiyear collaborative effort between the ARL Team and the Memphis team is to build a science-based suite of documents, guidelines, prototypes, and architectures that exhibit advanced learning technologies embodying the features of the Generalized Intelligent Framework for Tutors (GIFT) and conversational agents. These deliverables are informed by a community of researchers who have relevant expertise to bring these advanced technologies to fruition. The hope of this project is that the next generation of personnel in the DoD will be fortified with the optimal learning environments for a broad landscape of subject matters, personnel, and practical challenges in the military.

AutoMentor [PI: David Shaffer]

This project will develop a system for producing automated professional mentoring (called AutoMentor) as critical piece of technological infrastructure for a new, more motivating, and more inclusive approach to STEM education.  Students are motivated to learn STEM concepts because they play computer games based on STEM professions.  The project will add two important components to prior work on NSF-funded STEM computer games, such as Urban Science. First, we will develop automated mentoring technology with AutoMentor, building on previous research on automated tutoring systems (specifically on AutoTutor, a computer tutor that helps students learn about science and technology topics by holding a conversation in natural language with the learner).  Second, we will implement Evidence Centered Assessment Design and Epistemic Network Analysis, a methodology developed with NSF funding to assess students’ ability to think and act like STEM professionals through game play. The project will use a Wizard of Oz methodology, in which data will be collected about player/mentor interactions over multiple instances of game play, whereas the resulting database will be used to develop and validate a system for automatically coding interactions. The coded database will then be used to generate automated responses to player actions in the game through AutoMentor. The resulting system will be tested to see whether players’ STEM learning with automated mentoring are comparable to outcomes with live mentors. This project is a collaboration between University of Wisconsin, University of Memphis, University of Maryland, and Massachusetts Audubon Society.

AutoTutor Emotions [PI: Art Graesser]

AutoTutor simulates human tutorial dialog with an animated conversational agent that helps students learn qualitative physics or computer literary by holding conversations in natural language. This project tracks the emotions and knowledge of the learner by dialogue patterns, speech intonation, facial expressions, and body movements. It integrates advances in discourse processes, education, multimedia, psycholinguistics, computational linguistics, and artificial intelligence. The project investigates strategies, processes, practices, and environments that are likely to assist the learners in interactive knowledge construction, particularly at deeper levels of comprehension and problem solving.

Language Across Cultures [PI: James Pennebaker]

This project investigates the language and discourse patterns of English and Arabic texts using computerized text analysis tools. Specifically, the researchers are interested in analyzing discourse patterns in various corpora such as newspapers, speeches, and conversations to elucidate the leadership style, personality, and social status of leaders. In addition to English and Arabic, analyses will be performed on Korean, Chinese, and other languages. We will use computational tools that automatically analyze texts on hundreds of measures of language and text cohesion (using Coh-Metrix), including word characteristics, syntax complexity, lexical diversity, readability, connectives, latent semantic analysis, co-referential cohesion, mental model dimensions, and genre.

Shareable Knowledge Object Portable Environment for Intelligent Tutoring (SKOPE-IT) [PI: Xiangen Hu]

Researchers from the Institute for Intelligent Systems at the University of Memphis (UM) plan to develop Shareable Knowledge Object (SKO) Modules Using a Portable Intelligent Tutoring System (ITS) to teach mathematics to high school students. The proposed project targets intelligent tutoring systems that use animated conversational agents to hold conversations with learners in natural language. The proposed development focus on high school Algebra addresses a gap in agent-based learning systems in mathematics with applicability to addition STEM content and Navy training topics that require deep conceptual learning and/or precise analytical reasoning. It also addresses a key area of achievement deficiency among the nation’s high school student that negatively impacts pipeline development for future STEM fields important to the Navy.

Personal Assistant for Life Long Learning (PAL3) [PI: Bill Swartout]

As the Navy seeks to reduce manning on ships, fewer senior sailors will be available to mentor sailors newly assigned to a ship and guide them as they seek to translate schoolhouse knowledge acquired on land into operational knowledge that can be used at sea.  In collaboration with Arizona State University and the University of Memphis, the Institute for Creative Technologies at the University of Southern California proposes to create a prototype on-the-job (OJT) Personal Assistant for Life Long Learning (PAL3).  This system will guide new sailors in performing their mission essential shipboard duties.  The PAL3 will have knowledge of the ship as well as knowledge of the sailor’s background and education, and it will use this knowledge to create tailored educational experiences that reflect the sailor’s experience, interests and abilities.   The effectiveness of the resulting system will be evaluated experimentally.


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