| Belenky D, Ringenberg M, Olsen J, Aleven V and Rummel N (2014), "Using Dual Eye-Tracking to Evaluate Students’ Collaboration with an Intelligent Tutoring System for Elementary-Level Fractions", In CogSci 2014. Quebec City, Canada, July, 2014. , pp. 176-181. |
| Abstract: As learning technologies proliferate, it is important for research to address how to best align instruction to educational goals. For example, recent evidence indicates that working collaboratively may have unique benefits for facilitating the acquisition of conceptual understanding, as opposed to procedural fluency (Mullins, Rummel & Spada, 2011). To investigate this effect, we leverage and expand upon a new methodology, dual eye-tracking, to understand how collaborators’ joint attention may impact learning in a collaboration-enabled Intelligent Tutoring System for fractions. We present results from a study in which 28 pairs of 4th and 5th grade students completed a set of either conceptually- or procedurally-oriented instructional activities in a school setting. Results indicate that students collaborating exhibited learning gains for conceptual knowledge, but not for procedural knowledge, and that more joint attention was related to learning gains. These results may inform the design of future learning technologies, and illustrate the utility of using dual eye-tracking to study collaboration. |
BibTeX:
@inproceedings{Belenky2014,
author = {Belenky, Daniel and Ringenberg, Michael and Olsen, Jennifer and Aleven, Vincent and Rummel, Nikol},
editor = {Paul Bello and Marcello Guarini and Marjorie McShane and Brian Scassellati},
title = {Using Dual Eye-Tracking to Evaluate Students’ Collaboration with an Intelligent Tutoring System for Elementary-Level Fractions},
booktitle = {CogSci 2014},
year = {2014},
pages = {176-181},
url = {https://mindmodeling.org/cogsci2014/papers/041/paper041.pdf}
}
|
| Freedman R, Rosé CP, Ringenberg MA and VanLehn K (2000), "ITS tools for natural language dialogue: A domain-independent parser and planner", In Intelligent Tutoring Systems: 5th International Conference. Montréal, Canada Vol. 1839, pp. 433-442. Springer-Verlag. |
| Abstract: The goal of the Atlas project is to increase the opportunities for students to construct their own knowledge by conversing in typed form with a natural language-based ITS. In this paper we describe two components of Atlas -- APE, the integrated planning and execution system at the heart of Atlas, and CARMEL, the natural language understanding component. These components have been designed as domain-independent rule based software, with the goal of making them both extensible and reusable. We illustrate the use of CARMEL and APE by describing Atlas-Andes, a prototype ITS built with Atlas sing the Andes physics tutor as the host. |
BibTeX:
@inproceedings{Freedman2000,
author = {Freedman, Reva and Rosé, Carolyn Penstein and Ringenberg, Michael A. and VanLehn, Kurt},
editor = {Gilles Gauthier and Claude Frasson and Kurt VanLehn},
title = {ITS tools for natural language dialogue: A domain-independent parser and planner},
booktitle = {Intelligent Tutoring Systems: 5th International Conference},
publisher = {Springer-Verlag},
year = {2000},
volume = {1839},
pages = {433--442},
url = {http://www.public.asu.edu/~kvanlehn/Stringent/Abstracts/00ITS_RCF_CPR_MAR_KVL.html},
doi = {10.1007/3-540-45108-0_47}
}
|
| Jordan P, Ringenberg M and Hall B (2006), "Rapidly developing dialogue systems that support learning studies", In Proceedings of ITS06 Workshop on Teaching with Robots, Agents, and NLP. Jhongli, Taiwan , pp. 1-8. |
| Abstract: Abstract: We describe a dialogue system construction tool that supports the rapid development of dialogue systems for learning applications. Our goals in developing this tool were to provide 1) a plug-and-play type of system that facilitates the integration of new modules and experimentation with different core modules 2) configuration options that effect the behavior of the modules so that the system can be flexibly fine-tuned for a number of learning studies and 3) an authoring language for setting up the domain knowledge and resources needed by the system modules. |
BibTeX:
@inproceedings{Jordan06,
author = {Pamela Jordan and Michael Ringenberg and Brian Hall},
title = {Rapidly developing dialogue systems that support learning studies},
booktitle = {Proceedings of ITS06 Workshop on Teaching with Robots, Agents, and NLP},
year = {2006},
pages = {1-8},
url = {http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.75.652}
}
|
| Jordan PW, Hall B, Ringenberg M, Cue Y and Rosé C (2007), "Tools for authoring a dialogue agent that participates in learning studies", Artificial Intelligence in Education: Building Technology Rich Learning Contexts that Work., In Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work. Marina Del Rey, CA Vol. 158, pp. 43-50. IOS Press. |
| Abstract: TuTalk supports the rapid development of dialogue agents for learning applications. It enables an experimenter to create a dialogue agent with either minimal or no programming and provides the infrastructure needed for testing hypotheses about dialogue. Our main goals in developing this tool were to provide 1) an authoring interface and language for setting up the domain knowledge and resources needed to support the agent and 2) a plug-and-play type of system that facilitates the integration of new modules and experimentation with different core modules. In this paper we describe the authoring tool and the usability studies that have shaped its design, the dialogue that is supported and features of the authoring language and their usage history. |
BibTeX:
@inproceedings{Jordan2007,
author = {Pamela W Jordan and Brian Hall and Michael Ringenberg and Yui Cue and Carolyn Rosé},
editor = {Rosemary Luckin and Kenneth R. Koedinger and Jim Greer},
title = {Tools for authoring a dialogue agent that participates in learning studies},
booktitle = {Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work},
journal = {Artificial Intelligence in Education: Building Technology Rich Learning Contexts that Work},
publisher = {IOS Press},
year = {2007},
volume = {158},
pages = {43-50},
url = {http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.77.824}
}
|
| Jordan PW, Makatchev M, Ringenberg M and VanLehn K (2002), "Engineering the Tacitus-lite weighted abductive inference engine for use in the Why-Atlas qualitative physics tutoring system"
[BibTeX] |
BibTeX:
@techreport{Jordan2002,
author = {Pamela W Jordan and Maxim Makatchev and Michael Ringenberg and Kurt VanLehn},
title = {Engineering the Tacitus-lite weighted abductive inference engine for use in the Why-Atlas qualitative physics tutoring system},
year = {2002}
}
|
| Olsen JK, Belenky DM, Aleven V, Rummel N and Ringenberg M (2013), "Authoring collaborative intelligent tutoring systems", In Proceedings 2nd Workshop on Intelligent Support for Learning in Groups at the 16th International Conference on Artificial Intelligent in Education. Memphis, TM, July, 2013. Vol. 1009(111484), pp. 1-10. |
| Abstract: Authoring tools for Intelligent Tutoring System (ITS) have been shown to decrease the amount of time that it takes to develop an ITS. However, most of these tools currently do not extend to collaborative ITSs. In this paper, we illustrate an extension to the Cognitive Tutor Authoring Tools (CTAT) to allow for development of collaborative ITSs that can support a range of collaboration scripts. Authoring tools for collaborative ITSs must be flexible enough to allow for different learning goals and different collaboration scripts. We discuss how two collaboration scripts that we are using in our research on fractions learning are implemented in CTAT. The examples illustrate how CTAT flexibly supports collaborative tutors by running synchronized tutor engines for each student, and how it supports the development of collaborative tutors through the use of multiple behavior graphs that use no programming to develop. |
BibTeX:
@inproceedings{Olsen2013,
author = {Olsen, Jennifer K. and Belenky, Daniel M. and Aleven, Vincent and Rummel, Nikol and Ringenberg, Michael},
editor = {Walker E. and Looi C.-K.},
title = {Authoring collaborative intelligent tutoring systems},
booktitle = {Proceedings 2nd Workshop on Intelligent Support for Learning in Groups at the 16th International Conference on Artificial Intelligent in Education},
year = {2013},
volume = {1009},
number = {111484},
pages = {1--10},
url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84924981304&partnerID=MN8TOARS}
}
|
| Olsen JK, Belenky DM, Aleven V, Rummel N, Sewall J and Ringenberg M (2014), "Authoring tools for collaborative intelligent tutoring system environments", In 12th International Conference on Intelligent Tutoring Systems, ITS 2014. Honolulu, HI; United States, June, 2014. Vol. 8474(107059), pp. 523-528. Springer International Publishing. |
| Abstract: Authoring tools have been shown to decrease the amount of time and resources needed for the development of Intelligent Tutoring Systems (ITSs). Although collaborative learning has been shown to be beneficial to learning, most of the current authoring tools do not support the development of collaborative ITSs. In this paper, we discuss an extension to the Cognitive Tutor Authoring Tools to allow for development of collaborative ITSs through multiple synchronized tutor engines. Using this tool, an author can combine collaboration with the type of problem solving support typically offered by an ITS. Different phases of collaboration scripts can be tied to particular problem states in a flexible, problem-specific way. We illustrate the tool's capabilities by presenting examples of collaborative tutors used in recent studies that showed learning gains. The work is a step forward in blending computer-supported collaborative learning and ITS technologies in an effort to combine their strengths. |
BibTeX:
@inproceedings{Olsen2014,
author = {Olsen, Jennifer K. and Belenky, Daniel M. and Aleven, Vincent and Rummel, Nikol and Sewall, Jonathan and Ringenberg, Michael},
editor = {Stefan Trausan-Matu and Kristy Elizabeth Boyer and Martha Crosby and Kitty Panourgia},
title = {Authoring tools for collaborative intelligent tutoring system environments},
booktitle = {12th International Conference on Intelligent Tutoring Systems, ITS 2014},
publisher = {Springer International Publishing},
year = {2014},
volume = {8474},
number = {107059},
pages = {523--528},
url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84906498671&partnerID=MN8TOARS},
doi = {10.1007/978-3-319-07221-0_66}
}
|
| Ringenberg M (2007), "A Student Model Based on Item Response Theory for TuTalk, a Tutorial Dialogue Agent", In Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work. Marina Del Rey, CA Vol. 158, pp. 699-700. IOS Press. |
BibTeX:
@inproceedings{Ringenberg2007a,
author = {Ringenberg, Michael},
editor = {Rosemary Luckin and Kenneth R. Koedinger and Jim Greer},
title = {A Student Model Based on Item Response Theory for TuTalk, a Tutorial Dialogue Agent},
booktitle = {Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work},
publisher = {IOS Press},
year = {2007},
volume = {158},
pages = {699--700},
url = {http://dl.acm.org/citation.cfm?id=1563746}
}
|
| Ringenberg MA (2007), "Scaffolding Problem Solving with Embedded Examples to Promote Deep Learning". Thesis at: University of Pittsburgh., August, 2007. |
| Abstract: This study compared the relative utility of an intelligent tutoring system that uses procedure-based hints to a version that uses worked-out examples. The system, Andes, taught college level physics. In order to test which strategy produced better gains in competence, two versions of Andes were used: one offered participants graded hints and the other offered annotated, worked-out examples in response to their help requests. We found that providing examples was at least as effective as the hintsequences and was more efficient in terms of the number of problems it took to obtain the same level of mastery. |
BibTeX:
@phdthesis{Ringenberg2007,
author = {Ringenberg, Michael Aleksandr},
title = {Scaffolding Problem Solving with Embedded Examples to Promote Deep Learning},
school = {University of Pittsburgh},
year = {2007},
note = {Date of defense: August 10, 2006},
url = {http://etd.library.pitt.edu/ETD/available/etd-08092006-163545/}
}
|
| Ringenberg MA and VanLehn K (2006), "Scaffolding problem solving with annotated, worked-out examples to promote deep learning", In Intelligent tutoring Systems: 8th International Conference, ITS2006. Jhongli, Taiwan, June, 2006. Vol. 4053, pp. 625-634. Springer. |
| Abstract: This study compares the relative utility of an intelligent tutoring system that uses procedure-based hints to a version that uses worked-out examples for learning college level physics. In order to test which strategy produced better gains in competence, two versions of Andes were used: one offered participants graded hints and the other offered annotated, worked-out examples in response to their help requests. We found that providing examples was at least as effective as the hint sequences and was more efficient in terms of the number of problems it took to obtain the same level of mastery. |
BibTeX:
@inproceedings{Ringenberg2006,
author = {Ringenberg, Michael A. and VanLehn, Kurt},
editor = {Mitsuru Ikeda and Kevin D. Ashley and Tak-Wai Chan},
title = {Scaffolding problem solving with annotated, worked-out examples to promote deep learning},
booktitle = {Intelligent tutoring Systems: 8th International Conference, ITS2006},
publisher = {Springer},
year = {2006},
volume = {4053},
pages = {625--634},
note = {Winner of Best Paper First Authored by a Student Award},
doi = {10.1007/11774303_62}
}
|
| Ringenberg MA and VanLehn K (2008), "Does solving ill-defined physics problems elicit more learning than conventional problem solving?", In Doctoral Consortium, Intelligent Tutoring Systems: 9th International Conference, ITS2008. Montréal, Canada |
| Abstract: Students who complete an introductory physics course often do not have a good conceptual understanding of the principles taught. There have been various attempts at increasing conceptual learning, often with only modest improvements. One promising avenue is the use of ill-defined problems. However, it can be very difficult for students to solve these problems without proper support. If ill-defined problem solving can be supported using intelligent tutoring systems, then it will be possible to investigate the potential of ill-defined problems and their influence on conceptual learning. |
BibTeX:
@inproceedings{Ringenberg2008,
author = {Ringenberg, Michael A and VanLehn, Kurt},
editor = {B. P. Woolf and E. Aimeur and R. Nkambou and S. Lajoie},
title = {Does solving ill-defined physics problems elicit more learning than conventional problem solving?},
booktitle = {Doctoral Consortium, Intelligent Tutoring Systems: 9th International Conference, ITS2008},
year = {2008},
url = {http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.7436}
}
|
| Rosé C, Freedman R, Ringenberg M, Roque A, Schulze K, Shelby R, Siler S, Treacy D, VanLehn K, Weinstein A and Wintersgill M (2000), "Conceptual Tutoring in Atlas-Andes", In Building Dialogue Systems for Tutorial Applications: Papers from the 2000 Fall Symposium. North Falmouth, MA
[BibTeX] |
BibTeX:
@conference{Rose2000,
author = {Rosé, C. and Freedman, R. and Ringenberg, Michael and Roque, A. and Schulze, K. and Shelby, R. and Siler, S. and Treacy, D. and VanLehn, K. and Weinstein, A. and Wintersgill, M.},
title = {Conceptual Tutoring in Atlas-Andes},
booktitle = {Building Dialogue Systems for Tutorial Applications: Papers from the 2000 Fall Symposium},
year = {2000}
}
|
| Rosé CP, Jordan P, Ringenberg M, Siler S, VanLehn K and Weinstein A (2001), "Interactive conceptual tutoring in Atlas-Andes", In Artificial Intelligence in Education: AI-ED in the Wired and Wireless Future. Amsterdam , pp. 256-266. IOS Press. |
| Abstract: The goal of the Atlas project is to increase the opportunities for students to construct their own knowledge by conversing (in typed form) with a natural language based ITS. In this paper we present the results of a comparative evaluation between a model tracing tutor, the Andes system [9], with the otherwise equivalent dialogue enhanced Atlas-Andes [6]. Andes is a model tracing tutor (MTT) that presents quantitative physics problems to students. The focus of Andes is to help students develop good physics problem solving skills. While Andes has been successful at this task, nevertheless, there is ample evidence to suggest that teaching students to solve physics problems is not all that is required to provide them with a solid grounding in physics. While students in elementary mechanics courses have demonstrated an ability to master the skills required to solve quantitative physics problems, a number of studies have revealed that the same students perform very poorly when faced with qualitative physics problems [13, 12, 11]. Atlas provides Andes with the capability of leading students through directed lines of reasoning that teach basic physics conceptual knowledge, such as Newton's Laws. The purpose of these directed lines of reasoning is to provide a solid foundation in conceptual physics to promote meaningful learning and to enable students to develop meaningful problem solving strategies. In this study students using the dialogue enhanced version performed significantly better on a conceptual post-test than students using the standard version of Andes. |
BibTeX:
@inproceedings{Rose2001,
author = {Rosé, Carolyn P and Jordan, Pamela and Ringenberg, Michael and Siler, Stephanie and VanLehn, Kurt and Weinstein, Anders},
editor = {J. D. Moore and C. L. Redfield and W. L. Johnson},
title = {Interactive conceptual tutoring in Atlas-Andes},
booktitle = {Artificial Intelligence in Education: AI-ED in the Wired and Wireless Future},
publisher = {IOS Press},
year = {2001},
pages = {256-266},
note = {Best Paper Award Nomination},
url = {http://www.public.asu.edu/~kvanlehn/Stringent/Abstracts/01AIED_CR_PJ_MR_SS_KVL_AW.html}
}
|
| VanLehn K, Freedman R, Jordan P, Murray C, Osan R, Ringenberg M, Rosé C, Schulze K, Shelby R, Treacy D, Weinstein A and Wintersgill M (2000), "Fading and deepening: The next steps for Andes and other model-tracing tutors", In 5th International Conference on Intelligent Tutoring Systems. Montréal, Canada, June, 2000. Vol. 1839, pp. 474-483. Springer Berlin Heidelberg. |
| Abstract: Model tracing tutors have been quite successful in teaching cognitive skills; however, they still are not as competent as expert human tutors. We propose two ways to improve model tracing tutors and in particular the Andes physics tutor. First, tutors should fade their scaffolding. Although most model tracing tutors have scaffolding that needs to be gradually removed (faded), Andes' scaffolding is already “faded,” and that causes student modeling difficulties that adversely impact its tutoring. A proposed solution to this problem ... |
BibTeX:
@inproceedings{VanLehn2000,
author = {VanLehn, Kurt and Freedman, Reva and Jordan, Pamela and Murray, Charles and Osan, Remus and Ringenberg, Michael and Rosé, Carolyn and Schulze, Kay and Shelby, Robert and Treacy, Donald and Anders Weinstein and Mary Wintersgill},
editor = {Gilles Gauthier and Claude Frasson and Kurt VanLehn},
title = {Fading and deepening: The next steps for Andes and other model-tracing tutors},
booktitle = {5th International Conference on Intelligent Tutoring Systems},
publisher = {Springer Berlin Heidelberg},
year = {2000},
volume = {1839},
pages = {474--483},
doi = {10.1007/3-540-45108-0_51}
}
|
| VanLehn K, Jordan PW, Rosé CP, Bhembe D, Böttner M, Gaydos A, Makatchev M, Pappuswamy U, Ringenberg M, Roque A and others (2002), "The architecture of Why2-Atlas: A coach for qualitative physics essay writing", In Intelligent tutoring systems. Biarritz, France and San Sebastian, Spain, June, 2002. Vol. 2363, pp. 158-167. Springer-Verlag Berlin Heidelberg. |
| Abstract: The Why2-Atlas system teaches qualitative physics by having students write paragraph-long explanations of simple mechanical phenomena. The tutor uses deep syntactic analysis and abductive theorem proving to convert the student’s essay to a proof. The proof formalizes not only what was said, but the likely beliefs behind what was said. This allows the tutor to uncover misconceptions as well as to detect missing correct parts of the explanation. If the tutor finds such a flaw in the essay, it conducts a dialogue intended to remedy the missing or misconceived beliefs, then asks the student to correct the essay. It often takes several iterations of essay correction and dialogue to get the student to produce an acceptable explanation. Pilot subjects have been run, and an evaluation is in progress. After explaining the research questions that the system addresses, the bulk of the paper describes the system’s architecture and operation. |
BibTeX:
@inproceedings{VanLehn2002,
author = {VanLehn, Kurt and Jordan, Pamela W and Rosé, Carolyn P and Bhembe, Dumisizwe and Böttner, Michael and Gaydos, Andy and Makatchev, Maxim and Pappuswamy, Umarani and Ringenberg, Michael and Roque, Antonio and others},
editor = {Stefano A. Cerri and Guy Gouardères and Fàbio Paraguaçu},
title = {The architecture of Why2-Atlas: A coach for qualitative physics essay writing},
booktitle = {Intelligent tutoring systems},
publisher = {Springer-Verlag Berlin Heidelberg},
year = {2002},
volume = {2363},
pages = {158--167},
url = {http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.20.3537},
doi = {10.1007/3-540-47987-2_20}
}
|