Article

Choosing when to interact with learners

Authors:

Lei Qu,

Ning Wang,

W. Lewis JohnsonAuthors Info & Claims

IUI '04: Proceedings of the 9th international conference on Intelligent user interfaces

Pages 307 - 309

https://doi.org/10.1145/964442.964514

Published: 13 January 2004 Publication History

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Abstract

In this paper, we describe a method for pedagogical agents to choose when to interact with learners in interactive learning environments. This method is based on observations of human tutors coaching students in on-line learning tasks. It takes into account the focus of attention of the learner, the learner's current task, and expected time required to perform the task. A Bayesian network model combines evidence from eye gaze and interface actions to infer learner focus of attention. The attention model is combined with a plan recognizer to detect different types of learner difficulties such as confusion and indecision which warrant intervention. We plan to incorporate this capability into a pedagogical agent able to interact with learners in socially appropriate ways.

References

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Pearl, J., Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: organ-Kaufmann, 1998.

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W. L. Johnson. Using Agent Technology to Improve the Quality of Web-Based Education. In N. Zhong and J. Liu (Eds.), Web Intelligence. Springer, Berlin, 2002.

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W. Lewis Johnson. Interaction Tactics for Socially Intelligent Pedagogical Agents. In Proceedings of the Intelligent User Interfaces, 2003.

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Dessouky, M.M., Verma, S., Bailey, D., & Richel, J. A methodology for developing a Web-based factory simulator for manufacturing education. IEEE Transactions, 33, 167--180, 2001.

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T. del Soldato and B. Du Boulay. Implementation of motivational tactics in tutoring systems. Journal of Artificial Intelligence in Education, 6(4), 337--378, 199.

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Ceneda DArleo AGschwandtner TMiksch S(2022)Show Me Your Face: Towards an Automated Method to Provide Timely Guidance in Visual AnalyticsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.309487028:12(4570-4581)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TVCG.2021.3094870
Völkel SSchneegass CEiband MBuschek DPaternò FOliver NConati CSpano LTintarev N(2020)What is "intelligent" in intelligent user interfaces?Proceedings of the 25th International Conference on Intelligent User Interfaces10.1145/3377325.3377500(477-487)Online publication date: 17-Mar-2020
https://dl.acm.org/doi/10.1145/3377325.3377500
Malysheva YKelleher C(2020)Using Bugs in Student Code to Predict Need for Help2020 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VL/HCC50065.2020.9127252(1-6)Online publication date: Aug-2020
https://doi.org/10.1109/VL/HCC50065.2020.9127252
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Index Terms

Choosing when to interact with learners
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Intelligent agents
2. Human-centered computing
  1. Human computer interaction (HCI)

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Information & Contributors

Information

Published In

IUI '04: Proceedings of the 9th international conference on Intelligent user interfaces

January 2004

396 pages

ISBN:1581138156

DOI:10.1145/964442

Conference Chair:
Jean Vanderdonckt
University Louvain (BE)
,
Program Chairs:
Nuno Jardim Nunes
University of Madeira (PT)
,
Charles Rich
Mitsubishi Electric Research Laboratories (US)

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 January 2004

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IUI-CADUI04

Sponsor:

IUI-CADUI04: Intelligent User Interface

January 13 - 16, 2004

Funchal, Madeira, Portugal

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IUI '04 Paper Acceptance Rate 72 of 140 submissions, 51%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Ceneda DArleo AGschwandtner TMiksch S(2022)Show Me Your Face: Towards an Automated Method to Provide Timely Guidance in Visual AnalyticsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.309487028:12(4570-4581)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TVCG.2021.3094870
Völkel SSchneegass CEiband MBuschek DPaternò FOliver NConati CSpano LTintarev N(2020)What is "intelligent" in intelligent user interfaces?Proceedings of the 25th International Conference on Intelligent User Interfaces10.1145/3377325.3377500(477-487)Online publication date: 17-Mar-2020
https://dl.acm.org/doi/10.1145/3377325.3377500
Malysheva YKelleher C(2020)Using Bugs in Student Code to Predict Need for Help2020 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VL/HCC50065.2020.9127252(1-6)Online publication date: Aug-2020
https://doi.org/10.1109/VL/HCC50065.2020.9127252
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Mutahi JBent OKinai AWeldemariam KSengupta BContractor D(2015)Seamless blended learning using the cognitive learning companionIBM Journal of Research and Development10.1147/JRD.2015.246359159:6(8:1-8:13)Online publication date: 1-Nov-2015
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Clarkes-Nias JMutahi JKinai ABent OWeldemariam KSrivastava SKiczales GRussell DWoolf B(2015)Towards Capturing Learners Sentiment and ContextProceedings of the Second (2015) ACM Conference on Learning @ Scale10.1145/2724660.2728662(217-222)Online publication date: 14-Mar-2015
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Becker-Asano CStahl PRagni MCourgeon MMartin JNebel B(2013)An Affective Virtual Agent Providing Embodied Feedback in the Paired Associate Task: System Design and EvaluationIntelligent Virtual Agents10.1007/978-3-642-40415-3_36(406-415)Online publication date: 2013
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