Litman Lab







  • Diane Litman, Professor of Computer Science, Senior Scientist at LRDC, Faculty of Intelligent Systems Program
  • PhD Students

  • Michael Lipschultz, Computer Science
  • Wencan Luo, Computer Science
  • Huy Nguyen, Computer Science
  • Zahra Rahimi, Intelligent Systems
  • Wenting Xiong, Computer Science
  • Fan Zhang, Computer Science
  • Undergraduate Students

  • TBA, Computer Science
  • University Collaborators

  • Prof. Kevin Ashley, Law, Intelligent Systems, LRDC
  • Prof. Richard Correnti, School of Education, LRDC
  • Prof. Michael Ford, School of Education
  • Prof. Amanda Godley, School of Education
  • Dr. Pamela Jordan, LRDC
  • Dr. Sandra Katz, LRDC
  • Prof. Lindsay Clare Matsumura, School of Education, LRDC
  • Prof. Christian Schunn, Psychology, Learning Sciences, Intelligent Systems, LRDC
  • Prof. Jingtao Wang, Computer Science, Intelligent Systems, LRDC

    Alumni: Research Associates

  • Dr. Kate Forbes-Riley, LRDC
  • Dr. Joel Tetreault, LRDC (now at Yahoo, previously at ETS and Nuance )
  • Alumni: PhD Students

  • Hua Ai, Intelligent Systems, PhD 2009: Dissertation (now Research Scientist, Georgia Tech, School of Interactive Computing)
  • Min Chi, Intelligent Systems, PhD 2009: Dissertation (now Assistant Professor, North Carolina State University)
  • Mihai Rotaru, Computer Science, PhD 2008: Dissertation (now at Textkernel)
  • Arthur Ward, Intelligent Systems, PhD 2010: Dissertation (now Postdoctoral Research Associate, Department of Biomedical Informatics, University of Pittsburgh)
  • Alumni: Masters Students

  • Heather Friedberg, Computer Science (MS Project: Lexical Entrainment and Success in Student Engineering Groups), 2012 (now at Aptima)
  • Beatriz Maeireizo-Tokeshi, Computer Science (MS Project: Applying Co-training for Predicting Student Emotions with Spoken Dialogue Data), 2005
  • Amruta Purandare, Intelligent Systems
  • Alumni: Computer Science Undergraduate Students
    (NSF Research Experience for Undergraduate Program)

  • Alexandra Brusilovsky, now senior, University of Pittsburgh
  • Samantha Corcoran, now graduate student, University of Pittsburgh
  • Joanna Drummond, now graduate student, University of Toronto
  • Heather Friedberg, MS 2012, University of Pittsburgh (see above)
  • Gregory Nicholas, became graduate student, Brown University
  • Nathan Ong, now graduate student, University of Pittsburgh
  • Chris Thomas, now graduate student, University of Pittsburgh
  • Jesse Thomason, now graduate student, University of Texas at Austin
  • Other Alumni

  • Alison Huettner, consultant
  • Scott Silliman, programmer, LRDC
  • Past Visitors

  • Matthew Frampton, PhD Student, University of Edinburgh (now at Stanford)
  • Reva Freedman, Professor, Northern Illinois University
  • Ryoko Tokuhisa, Toyota Central R&D Labs (also PhD Student at Nara Institute of Science and Technology)



Currently Funded Projects:

    Teaching Writing and Argumentation with AI-Supported Diagramming and Peer Review (project homepage) (Sep. 2011 - Aug. 2015)

    The PIs are investigating the design of intelligent tutoring systems (ITSs) that are aimed at learning in unstructured domains. Such systems are not able to do as much automatically as ITSs working in traditionally narrow and well-structured domains, but rather they need to share responsibilities for scaffolding learning with a teacher and/or peers. In the work proposed, the three PIs, who share expertise in automated natural language understanding, intelligent tutoring systems, machine learning, argumentation (especially in law), complex problem solving, and engineering education, are integrating intelligent tutoring, data mining, machine learning, and language processing to design a socio-technical system (people and machines working together) that helps undergraduates and law students write better argumentative essays. The work of helping learners derive an argument is shared by the computer and peers, as is the work of helping peer reviewers review the writing of others and the work of learners to turn their argument diagrams into well-written documents. Research questions address the roles computers might take on in promoting writing and the technology that enables that, how to distribute scaffolding between an intelligent machine and human agents, how to promote better writing (especially the relationship between diagramming and writing), and how to promote learning through peer review of the writing of others.

    This project is bringing together outstanding researchers from a variety of different disciplines -- artificial intelligence, law education, engineering and science education, and cognitive psychology -- to address an education issue of national concern -- writing, especially writing that makes and substantiates a point -- and to explore ways of extending intelligent tutoring systems beyond fact-based domains. It fulfills all aims of the Cyberlearning program -- to imagine, design, and learn how to best design and use the next generation of learning technologies, to address learning issues of national importance, and to contribute to understanding of how people learn. This NSF grant is in collaboration with Kevin Ashley and Christian Schunn.

    Intelligent Scaffolding for Peer Reviews of Writing (project homepage) (Sep. 2012 - Aug. 2015)

    This study will adapt and apply existing Artificial Intelligence techniques from Natural Language Processing and Machine Learning to automatically scaffold the peer reviewing and revising-from-peer-review process. Utilizing an iterative development plan more complex and refined versions of the system will be used with heavy testing in October and March each year. Researchers will undertake three different but partially integrated interventions: automatic detection of effective review comment features, automatic detection of thesis statements and related comments, and facilitating author revision by organizing review comments and author response planning. The pilot experiment will take place in a high school setting the last 6 months of the grant.

    Iterative development will be conducted in four classroom environments: high school science, high school English/social studies, a university physics lab, and a university psychology class. The comparison group will include students using the same web-based peer review system as the treatment group, but with all the intelligent scaffolding interventions disabled. This IES grant is in collaboration with Kevin Ashley, Amanda Godley and Christian Schunn.

    Response-to-Text Prompts to Assess Students' Writing Ability: Using Natural Language Processing for Scoring Writing at Scale (July 2013 - June 2015)

    Assessing analytic writing in response to text (RTA) is a means for understanding students’ analytic writing ability and understanding measures of effective teaching. Current writing assessments typically examine “content-free” writing (i.e., writing in response to open-ended prompts divorced from text), although prior work demonstrates that it is also possible to administer and rate student writing in response-to-text. However, there is a significant barrier to scoring writing at scale, as scoring is labor intensive and requires extensive training and expertise on the part of raters to obtain reliable scores.

    Recent advances in artificial intelligence offer a promising way forward for scoring students’ analytic writing at scale. Natural language processing (NLP) experts have been working for decades on producing ways to reliably score student writing holistically. The state-of-the-art of automated essay systems (AES) indicates that AES can produce scores as reliable as human ratings in the sense that they can be trained to score similarly to humans on holistic measures of writing, especially for short, timed student responses. To move the field forward, however, there is a need for writing assessments that are aligned with authentic writing tasks. Second, there is a need to explore whether AES algorithms can reliably score across multiple dimensions of student writing. Our assessement includes five dimensions (analysis, evidence, organization, style/vocabulary and mechanics/usage/grammar/syntax) and it will be important to see if AES designs can rate substantive dimensions such as analysis and evidence as well as they can rate more surface and structural dimensions of writing. This LRDC internal grant is in collaboration with Rip Correnti and Lindsay Clare Matsumura.

    Peer Review Search & Analytics in MOOCs via Natural Language Processing (February 2014)

    Peer assessments provided by students are widely used in massively open-access online courses (MOOCs) due to the difficulty of fully-automating assessment for many types of assignments. However, the use of student assessment provides an overwhelming amount of textual information for instructors to process. The proposed research will develop Natural Language Processing methods to support search and large scale analytics of student assessment comments in MOOCs. My liason for this Google Faculty Research Award is Daniel Russell.

    Improving Undergraduate STEM Education by Integrating Natural Language Processing with Mobile Technologies (July 2014 - June 2016)

    The degree and quality of interaction between students and instructors are critical factors for students' engagement, retention, and learning outcomes across domains. This is especially true for the introductory STEM courses at the undergraduate level since these courses are generally taught in lecture halls due to a large number of students enrolled. Recent developments in educational technology such as MOOCs and financial troubles in universities make it safe to predict that the class size problem will only get worse both in traditional face-to-face and online classes. So, how can we modify the passive nature of lectures and increase the interaction while actively involving both students and instructors in the learning process in these circumstances?

    In order to address this problem, we propose integrating Natural Language Processing (NLP) with a mobile application that prompts students to reflect as well as provide immediate and continuous feedback to instructors about the difficulties that their students encounter. By enhancing the student reflection and instructor feedback cycle with technological tools, this project will incorporate three lines of research: 1) role of students' reflection and instructor's feedback on students' retention and learning outcomes, 2) effectiveness and reliability of NLP to summarize written responses in a meaningful way, and 3) value and design of mobile technologies to improve retention and learning in STEM domains. This LRDC internal grant is in collaboration with Muhsin Menekse and Jingtao Wang.

    Entrainment and Task Success in Team Conversations (August 2014 - July 2017)

    Teams, rather than individuals, are now the usual generators of scientific knowledge. How to optimize team interactions is a passionately pursued topic across several disciplines. This research hypothesizes that linguistic entrainment, or the convergence of linguistic properties of spoken conversation, may serve as a valid and relatively easy-to-collect measure that is predictive of team success. From the perspective of developing interventions for team innovation, organizations could unobtrusively measure team effectiveness using entrainment, and intervene with training to aid teams with low entrainment. Similar interventions would be useful for conversational agents that monitor and facilitate group interactions. The work could also support the development of browsers or data mining applications for corpora such as team meetings or classroom discussions.

    To date, most studies of entrainment have focused on conversational dyads rather than the multi-party conversations typical of teams. The technical objective of this research is to develop, validate and evaluate new measures of linguistic entrainment tailored to multi-party conversations. In particular, the first research goal is to develop multi-party entrainment measures that are computable using language technologies, and that are both motivated and validated by the literature on teams. The second goal is to demonstrate the utility of these measures in being associated with team processes and predicting team success. The supporting activities include 1) collection of an experimentally-obtained corpus where teams collaborate on a task where they converse, and where a team process intervention manipulates likely entrainment, 2) development of a set of entrainment measures for multi-party dialogue, 3) use of standard psychological teamwork measures for convergent validity and random conversations for divergent validity, 4) exploration of how the team factors of gender composition and participation equality impact group entrainment, and 5) evaluation of the utility of measuring entrainment for predicting team and dialogue success. This NSF grant is in collaboration with Susannah Paletz.

    An Intelligent Ecosystem for Science Writing Instruction (September 2014 - August 2017)

    The ability to express scientific ideas in both written and oral form is an important 21st century skill. Teachers, employers, and college faculty lament the inability of many high school graduates to write clearly. This deficit in writing is due in part because teachers do not have the time to provide appropriate, timely feedback to students on their writing. This project would help teachers help students achieve these skills through automating an effective feedback process, in ways that are customized to particular disciplines and local classroom needs, particularly in high needs districts. The project will contribute to knowledge about how students learn to write and how computer assisted systems can support this learning.

    This project will develop and test three tools: 1) Teaching resources organized as developmental trajectories for teachers to use (e.g. from more simple to more complex; with diagnostics and strategies for addressing particular challenges); 2) A teacher dashboard that uses Artificial Intelligence tools to provide timely formative assessment to teachers by highlighting problem areas in their students' writing and peer reviews; and 3) An online teacher resource exchange to rapidly grow the set of appropriate assignments that can be used with this approach, critically filtered by student performance metrics. The project builds on a current system called SWoRD, which supports student peer reviewing in many disciplines within and beyond science. Working with six lead teachers and larger set of pilot teachers, the project will develop a trajectory of effective writing assignments in Biology, Chemistry, and Physics. In year three, there will be a summative evaluation with 90 teachers. This NSF grant is in collaboration with Amanda Godley and Christian Schunn.

Completed Projects:

    Adding Spoken Language to a Text-Based Dialogue Tutor (Nov. 2003 - Sep. 2006)

    The goal of this research is to generate an empirically-based understanding of the ramifications of adding spoken language capabilities to text-based dialogue tutors, and to understand how these implications might differ in human-human and human-computer spoken interactions. This research will explore the relative effectiveness of speech versus text-based tutoring in the context of ITSPOKE, a speech-based dialogue system that uses a text-based system for tutoring conceptual physics (VanLehn et al, 2002) as its ``back-end.'' The results of this work will demonstrate whether spoken dialogues yield increased performance compared to text with respect to a variety of evaluation measures, whether the same or different student and tutor behaviors correlate with learning gains in speech and text, and how such findings generalize both across and within human and computer tutoring conditions. These results will impact the development of future dialogue tutoring systems incorporating speech, by highlighting the performance gains that can be expected, and the requirements for achieving such gains.

    TuTalk: Infrastructure for authoring and experimenting with natural language dialogue in tutoring systems and learning research (project homepage) (Dec. 2004- Nov. 2006)

    The focus of our proposed work is to provide an infrastructure that will allow learning researchers to study dialogue in new ways and for educational technology researchers to quickly build dialogue based help systems for their tutoring systems. Most tutorial dialogue systems that to date have undergone successful evaluations (CIRCSIM, AutoTutor, WHY-Atlas, the Geometry Explanation Tutor) represent development efforts of many man-years. These systems were instrumental in pushing the technology forward and in proving that tutorial dialogue systems are feasible and useful in realistic educational contexts, although not always provably better on a pedagogical level than the more challenging alternatives to which they have been compared. We are now entering a new phase in which we as a research community must not only continue to improve the effectiveness of basic tutorial dialogue technology but also provide tools that support investigating the effective use of dialogue as a learning intervention as well as application of tutorial dialogue systems by those who are not dialogue system researchers. We propose to develop a community resource to address all three of these problems on a grand scale, building upon our prior work developing both basic dialogue technology and tools for rapid development of running dialogue systems. This grant is led by Pamela Jordan at the University of Pittsburgh and Carolyn Rose at Carnegie Mellon University.

    Does Treating Student Uncertainty as a Learning Impass Improve Learning in Spoken Dialogue Tutoring (Oct. 2006 - May 2007)

    Most existing tutoring systems respond based only on the correctness of student answers. Although the tutoring community has shown that incorrectness and uncertainty both represent learning impasses (and thus opportunities to learn), and has also shown correlations between uncertainty and learning, to date there have been very few controlled experiments investigating whether system responses to student uncertainty improve learning. We thus propose a small controlled study to test whether this hypothesis holds true, under "ideal" system conditions. The study uses a Wizard of Oz (WOZ) version of a qualitative physics spoken dialogue tutoring system, where the human Wizard performs speech recognition, natural language understanding, and recognition of uncertainty, for each student answer. In the experimental condition, the Wizard then tells the system that correct but uncertain answers are incorrect, causing the system to respond to both uncertain and incorrect student answers in the same way, namely with further dialogue, thereby reinforcing the student's understanding of the principle(s) under discussion. In the first control condition, the system responds only to incorrect student answers in this way. In the second control condition, the system responds to a percentage of correct answers in this way, to control for the additional tutoring in the experimental condition.

    Monitoring Student State in Tutorial Spoken Dialogue (Sep. 2003 - Aug. 2007)

    This research investigates the feasibility and utility of monitoring student emotions in spoken dialogue tutorial systems. While human tutors respond to both the content of student utterances and underlying perceived emotions, most tutorial dialogue systems cannot detect student emotions, and furthermore are text-based, which may limit their success at emotion prediction. While there has been increasing interest in identifying problematic emotions (e.g. frustration, anger) in spoken dialogue applications such as call centers, little work has addressed the tutorial domain. The PIs are investigating the use of lexical, syntactic, dialogue, prosodic and acoustic cues to enable a computer tutor to automatically predict and respond to student emotions. The research is being performed in the context of ITSPOKE, a speech-based tutoring dialogue system for conceptual physics. The PIs are recording students interacting with ITSPOKE, manually annotating student emotions in these as well as in human-human dialogues, identifying linguistic and paralinguistic cues to the annotations, and using machine learning to predict emotions from potential cues. The PIs are then deriving strategies for adapting the system's tutoring based upon emotion identification. The major scientific contribution will be an understanding of whether cues available to spoken dialogue systems can be used to predict emotion, and ultimately to improve tutoring performance. The results will be of value to other applications that can benefit from monitoring emotional speech. Progress towards closing the performance gap between human tutors and current machine tutors will also expand the usefulness of current computer tutors. This grant is in collaboration with Julia Hirschberg and her group at Columbia University.

    Tutoring Scientific Explanations via Natural Language Dialogue (project homepage) (Jan. 2004 - Dec. 2007)

    It is widely acknowledged, both in academic studies and the marketplace, that the most effective form of education is the professional human tutor. A major difference between human tutors and computer tutors is that only human tutors understand unconstrained natural language input. Recently, a few tutoring systems have been developed that carry on a natural language (NL) dialogue with students. Our research problem is to find ways to make NL-based tutoring systems more effective. Our basic approach is to derive new dialogue strategies from studies of human tutorial dialogues, incorporate them in an NL-based tutoring system, and determine if they make the tutoring system more effective. For instance, some studies are determining if learning increases when human tutors are constrained to follow certain strategies. In order to incorporate the new dialogue strategies into our existing text and spoken NL-based tutoring systems, two completely new modules are being developed. One new module will interpret student utterances using a large directed graph of propositions called an explanation network, which is halfway between the shallow and deep representations of knowledge that are currently used. The second new module uses machine learning to improve the selection of dialogue management strategies. The research is thus a multidisciplinary effort whose intellectual merit lies in new results in the cognitive psychology of human tutoring, in the technology of NL processing, and in the design of effective tutoring systems. Improved NL-based tutoring systems could have a broad impact on education and society. This grant is in collaboration with Kurt VanLehn, Micheline Chi, and Pamela Jordan at the Learning Research and Development Center, University of Pittsburgh, and with Carolyn Rose (now at CMU).

    Cohesion in Tutorial Dialogue and its Impact on Learning (Oct. 2006 - June 2009)

    Research on the factors that make one-on-one tutoring a very effective mode of instruction has converged on an important finding: that the critical term in "tutorial interaction" is "interaction." That is, what the tutor says or does during tutoring, and what the student says or does are less important than the dynamic, coordinated interplay between their dialogue turns. It is now important to identify the discourse mechanisms that drive highly interactive human tutoring, so that these mechanisms can be simulated by natural-language dialogue engines in intelligent tutoring systems (ITSs). In the first stage of this project, we will analyze a corpus of naturalistic tutorial dialogues to accomplish this goal. Specifically, we will identify the mechanisms that achieve cohesion in tutorial dialogues, since highly interactive tutorial dialogue is intrinsically highly cohesive. In the second stage, we will run a series of controlled studies to test the hypothesis that more highly cohesive tutorial dialogue is more effective for promoting learning than less cohesive dialogue, and to assess the effectiveness of a few selected mechanisms of cohesion. Finally, in the third stage of the project, we will explore the extent to which database tools developed by the computational linguistics community (e.g., WordNet and FrameNet) can automatically tag cohesion in tutorial dialogue. We will also extend these tools and develop algorithms that will allow them to be used to automatically generate cohesive tutor turns for a small sample of student turns, as a first step towards developing a natural-language dialogue engine that can use these tools to generate highly cohesive tutorial dialogue. This grant is in collaboration with Sandra Katz.

    Improving Learning from Peer Review with NLP and ITS Techniques (July 2009 - June 2011)

    SWoRD is a web-based system to support peer reviewing in a wide variety of disciplinary classroom settings. One result of prior research with SWoRD is an enormous database of written materials that are ripe for analysis and exploitation in support of research on natural language processing (NLP), intelligent tutoring systems (ITS), cognitive science, educational data mining, and improving learning from peer review. In this project we will both analyze existing SWoRD-generated data, and develop an improved version of SWoRD for use in further experimentation. In particular, we will explore using SWoRD to teach substantive skills in domains involving ill-defined problems, and will explore techniques for automatically identifying key concepts and flagging issue understanding. Second, given a SWoRD toolkit of what can be accomplished robustly with peer interactions, we will explore the use of natural language processing to automatically support and improve those interactions. Finally, we will develop a new version of the SWoRD program that incorporates improved features and control facilities, and that incorporates Artificial Intelligence techniques to improve learning in a variety of ways. This is an internal LRDC grant, and is in collaboration with Christian Schunn and Kevin Ashley.

    Adapting to Student Uncertainty over and above Correctness in A Spoken Tutoring Dialogue System (Sep. 2006 - Aug. 2011)

    This research investigates whether responding to student uncertainty over and above correctness improves learning during computer tutoring. The investigation is performed in the context of a spoken dialogue tutoring system, where student speech provides many linguistic cues (e.g. intonation, pausing, word usage) that computational linguistics research suggests can be used to detect uncertainty. Intelligent tutoring systems research suggests that uncertainty is part of the learning process, and has hypothesized that to increase system effectiveness, it is critical to respond to more than correctness. However, most existing tutoring systems respond only to student correctness, and few controlled experiments have yet investigated whether also responding to uncertainty can improve learning. This research designs and implements two different enhancements to the spoken dialogue tutoring system, to test two hypotheses in the tutoring literature concerning how tutors can effectively respond to uncertainty over and above correctness. The first hypothesis is that student uncertainty and incorrectness both represent learning impasses, i.e., opportunities to improve understanding. This hypothesis is addressed with an enhanced system version that treats uncertainty in the same way that incorrectness is currently treated (i.e., with additional subdialogue to increase understanding). The second hypothesis is that more optimal responses can be developed by modeling how human tutor responses to correctness change when the student is uncertain. This hypothesis is addressed by analyzing human tutor dialogue act responses (i.e. content and presentation) to student uncertainty over and above correctness in an existing tutoring corpus, then implementing these responses in a second enhanced system version. Two controlled experiments are then performed. The first tests the relative impact of the two adaptations on learning using a Wizard of Oz version of the system, with a human (Wizard) detecting uncertainty and performing speech recognition and language understanding. The second experiment tests the impact of the best-performing adaptation from the first experiment in the context of the real system, with the system processing the speech and language and detecting uncertainty in a fully automated manner. The major intellectual contribution of the research is to demonstrate whether significant improvements in learning are achieved by adapting to student uncertainty over and above correctness during tutoring, to advance the state of the art by fully automating and evaluating user uncertainty detection and adaptation in a working spoken dialogue system, and to investigate any different effects of this adaptation under ideal versus actual system conditions. This NSF grant is in collaboration with Kate Forbes-Riley.

    Improving a Natural-language Tutoring System That Engages Students in Deep Reasoning Dialogues About Physics (project homepage) (June 2010-May 2013)

    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 ITSs have been shown to be effective, they are still not as effective as human tutors.

    The goal of this Cognition and Student Learning development project is to take a step towards meeting President Obama's challenge to produce "learning software as effective as a personal tutor." We will do this by building an enhanced version of a natural-language dialogue system that engages students in deep-reasoning, reflective dialogues after they solve quantitative problems in Andes, an intelligent tutoring system for physics. Improvements to this system will focus on addressing a key limitation of natural-language (NL) tutoring systems: although these systems are "interactive" in the sense that they try to elicit explanations from students instead of lecturing to them, automated tutors do not align their dialogue turns with those of the student to the same degree, and in the same ways, that human tutors do. In particular, automated tutors often fail to reuse parts of the student's dialogue turns in their own turns, to adjust the level of abstraction that the student is working from when the student is over-generalizing or missing important distinctions between concepts, and to abstract or specialize correct student input when doing so might enhance the student's understanding. Empirical research shows that these forms of lexical and semantic alignment in human tutoring predict learning. The main outcome of this development effort will be a fully working, prototype reflective dialogue version of Andes that can carry out these functions and serve as a research platform for a future study that compares the effectiveness of the enhanced NL tutoring system with the current system, which lacks these alignment capabilities--thereby allowing us to test the hypothesis that it is not interaction per se that explains the effectiveness of human tutoring, but how it is carried out.

    The enhanced version of this reflective dialogue system will be developed through an iterative process of preparing a prototype for experienced physics teachers and students to try out using the "Wizard of Oz" paradigm, identifying cases in which the system does not work as intended (e.g., the tutor prompts the student to generalize or make distinctions when this is not warranted by the discourse context), refining the software to correct these problems, and testing the revised software in a subsequent field trial. The subject pool for these trials will be students enrolled in a first-year physics course at the University of Pittsburgh and high school students taking physics in Pittsburgh urban and suburban schools. During the third (final) year of the project, we will collect pilot data that addresses the feasibility of implementing the system in authentic high school physics classes, and the promise of the system to increase students' conceptual understanding of physics and ability to solve physics problems. The latter will be determined by comparing students' pre- and post-test performance on measures of conceptual understanding and problem-solving ability in physics, and by comparing the performance of students who use the current and enhanced version of the system on these measures. This IES grant is in collaboration with Sandra Katz, Pamela Jordan, and Michael Ford.

    Keeping Instructors Well-Informed in Computer-Supported Peer Review (project homepage) (June 2011-June 2013)

    From the instructor's viewpoint, a class writing assignment is a black box. Until instructors actually read the first or final drafts, they do not have much information about how well the assignment has succeeded as a pedagogical activity, and even then, it is hard to get a complete picture. Computer-supported peer review systems such as SWoRD, a scaffolded peer review system can help students to write higher quality compositions in classroom assignments, can help in this regard. The goal of this project is to develop and evaluate methods to provide instructors with a comprehensive overview of the progress of a class writing assignment in terms of how well students understand the issues based on structured reviewing rubrics, feedback students provide and receive in the peer review process, and machine learning computational lingustics analysis of the resulting texts. The SWoRD-based peer-review system will present the instructor's overview via a kind of "Teacher-side Dashboard" that will summarize salient information for the class as a whole, cluster students based on common features of their texts, and enable instructors to delve into particular student's writings more effectively in a guided manner. This is an internal LRDC grant, and is in collaboration with Kevin Ashley, Christian Schunn and Jingtao Wang.

    RI: Small: An Affect-Adaptive Spoken Dialogue System that Responds Based on User Model and Multiple Affective States (September 2009 - August 2013)

    There has been increasing interest in affective dialogue systems, motivated by the belief that in human-human dialogues, participants seem to be (at least to some degree) detecting and responding to the emotions, attitudes and metacognitive states of other participants. The goal of the proposed research is to improve the state of the art in affective spoken dialogue systems along three dimensions, by drawing on the results of prior research in the wider spoken dialogue and affective system communities. First, prior research has shown that not all users interact with a system in the same way; the proposed research hypothesizes that employing different affect adaptations for users with different domain aptitude levels will yield further performance improvement in affective spoken dialogue systems. Second, prior research has shown that users display a range of affective states and attitudes while interacting with a system; the proposed research hypothesizes that adapting to multiple user states will yield further performance improvement in affective spoken dialogue systems. Third, while prior research has shown preliminary performance gains for affect adaptation in semi-automated dialogue systems, similar gains have not yet been realized in fully automated systems. The proposed research will use state of the art empirical methods to build fully automated affect detectors. It is hypothesized that both fully and semi-automated versions of a dialogue systemthat either adapts to affect differently depending on user class, or that adapts to multiple user affective states, can improve performance compared to non-adaptive counterparts, with semi-automation generating the most improvement. The three hypotheses will be investigated in the context of an existing spoken dialogue tutoring system that adapts to the user state of uncertainty. The task domain is conceptual physics typically covered in a first-year physics course (e.g., Newtons Laws, gravity, etc.). To investigate the first hypothesis, a first enhanced system version will be developed; it will use the existing uncertainty adaptation for lower aptitude users with respect to domain knowledge, and a new uncertainty adaptation will be developed and implemented to be employed for higher aptitude users. To investigate the second hypothesis, a second enhanced systemversion will be developed; it will use the existing uncertainty adaptation for all turns displaying uncertainty, and a new disengagement adaptation will be developed and implemented to be employed for all student turns displaying a second state of disengagement. A controlled experiment with the two enhanced systems will then be conducted in a Wizard-of-Oz (WOZ) setup, with a human Wizard detecting affect and performing speech recognition and language understanding. To investigate the third hypothesis, a second controlled experiment will be conducted, which replaces the WOZ system versions with fully-automated systems.

    The major intellectual contribution of this research will be to demonstrate whether significant performance gains can be achieved in both partially and fully-automated affective spoken dialogue tutoring systems 1) by adapting to user uncertainty based on user aptitude levels, and 2) by adapting to multiple user states hypothesized to be of primary importance within the tutoring domain, namely uncertainty and disengagement. The research project will thus advance the state of the art in both spoken dialogue and computer tutoring technologies, while at the same time demonstrating any differing effects of affect-adaptive systems under ideal versus realistic conditions. More broadly, the research and resulting technology will lead to more natural and effective spoken dialogue-based systems, both for tutoring as well as for more traditional information-seeking domains. In addition, improving the performance of computer tutors will expand their usefulness and thus have substantial benefits for education and society. This NSF grant is in collaboration with Kate Forbes-Riley.

Current and Past Sponsors:

  • The National Science Foundation through a grant to the Center for Interdisciplinary Research on Constructive Learning Environments (CIRCLE) at the University of Pittsburgh and Carnegie Mellon University. (NSF Award Abstract #9720359)
  • The National Science Foundation through a grant to Kurt A. VanLehn, Carolyn P. Rose, Diane J. Litman, Michelene Chi, and Pamela W. Jordan (NSF Award Abstract #0325054) at the University of Pittsburgh
  • The Office of Naval Research through a grant to Diane J. Litman (Adding Spoken Language to a Text-Based Dialogue Tutor)
  • The Office of Naval Research through a grant to Sandra Katz and Diane Litman (Cohesion in Tutorial Dialogue and its Impact on Learning)
  • The Learning Research and Development Center, University of Pittsburgh, through a grant to Diane Litman, Christian Schunn, and Kevin Ashley (Improving Learning from Peer Review with NLP and ITS Techniques)
  • United States Department of Education Institute of Education Sciences through a grant to S. Katz (PI), P. Jordan (Co-PI), D. Litman (Co-PI) and M. Ford (Co-PI) (Cognition and Student Learning Award)
  • The Learning Research and Development Center, University of Pittsburgh, through a grant to Kevin Ashley, Diane Litman, Chris Schunn, and Jingtao Wang (Keeping Instructors Well-Informed in Computer-Supported Peer Review)
  • United States Department of Education Institute of Education Sciences through a grant to Diane Litman (PI), Kevin Ashley, Amanda Godley and Christian Schunn (Co-PIs) (Educational Technology Award)
  • The Learning Research and Development Center, University of Pittsburgh, The Learning Research and Development Center, University of Pittsburgh, through a grant to Richard Correnti, Diane Litman, and Lindsay Clare Matsumara (Response-to-Text Prompts to Assess students' Writing Ability: Using Natural Language Processing for Scoring Writing At-Scale)
  • The Learning Research and Development Center, University of Pittsburgh, through a grant to Muhsin Menekse, Diane Litman, and Jingtao Wang (Improving Undergraduate STEM Education by Integrating Natural Language Processing with Mobile Technologies)