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629 results on '"Sottilare, Robert A."'

56. Passively Classifying Student Mood and Performance within Intelligent Tutors

Author

Sottilare, Robert A. and Proctor, Michael

Abstract

It has been long recognized that successful human tutors are capable of adapting instruction to mitigate barriers (e.g., withdrawal or frustration) to learning during the one-to-one tutoring process. A significant part of the success of human tutors is based on their perception of student affect (e.g., mood or emotions). To at least match the capabilities of human tutors, computer-based intelligent tutoring system (ITS) will need to "perceive" student affect and improve performance by selecting more effective instructional strategies (e.g., feedback). To date, ITS have fallen short in realizing this capability. Much of the existing research models the emotions of virtual characters rather than assessing the affective state of the student. Our goal was to determine the context and importance of student mood in an adaptable ITS model. To enhance our existing model, we evaluated procedural reasoning systems used in virtual characters, and reviewed behavioral and physiological sensing methods and predictive models of affect. Our experiment focused on passive capture of behaviors (e.g., mouse movement) during training to predict the student's mood. The idea of mood as a constant during training and predictors of performance are also discussed. (Contains 3 tables and 6 figures.)

Published
2012

57. Semi-Supervised Classification of Realtime Physiological Sensor Datastreams for Student Affect Assessment in Intelligent Tutoring

Author

Brawner, Keith W., Sottilare, Robert, Gonzalez, Avelino, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cerri, Stefano A., editor, Clancey, William J., editor, Papadourakis, Giorgos, editor, and Panourgia, Kitty, editor

Published
2012
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58. Toward the Development of an Intelligent Tutoring System for Distributed Team Training through Passive Sensing

Author

Sottilare, Robert A., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Aleven, Vincent, editor, Kay, Judy, editor, and Mostow, Jack, editor

Published
2010
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74. The mental machine

Author

Hillege, Roderic H.L., Lo, J.C., Janssen, Christian P., Romeijn, Nico, Sottilare, Robert A., and Schwarz, Jessica

Subjects
Computer science, business.industry, 0206 medical engineering, 05 social sciences, Training level, Work (physics), Workload, 02 engineering and technology, Machine learning, computer.software_genre, 020601 biomedical engineering, Mental workload classification, Remote photoplethysmography, 0501 psychology and cognitive sciences, Adaptive Instructional Systems, Artificial intelligence, State (computer science), business, computer, 050107 human factors
Abstract

This paper investigates whether mental workload can be classified in an operator setting using unobtrusive psychophysiological measures. Having reliable predictions of workload using unobtrusive sensors can be useful for adaptive instructional systems, as knowledge of a trainee’s workload can then be used to provide appropriate training level (not too hard, not too easy). Previous work has investigated automatic mental workload prediction using biophysical measures and machine learning, however less attention has been given to the level of physical obtrusiveness of the used measures. We therefore explore the use of color-, and infrared-spectrum cameras for remote photoplethysmography (rPPG) as physically unobtrusive measures. Sixteen expert train traffic operators participated in a railway human-in-the-loop simulator. We used two machine learning models (AdaBoost and Random Forests) to predict low-, medium- and high-mental workload levels based on heart rate features in a leave-one-out cross-validated design. Results show above chance classification for low- and high-mental workload states. Based on infrared-spectrum rPPG derived features, the AdaBoost machine learning model yielded the highest classification performance.

Published
2020

79. Diagnostic Requirements for Efficient, Adaptive Robotic Surgery Training

Author

Witte, Thomas E.F., Schmettow, Martin, Groenier, Marleen, Sottilare, Robert A., Schwarz, Jessica, and Technical Medicine

Subjects
Real-time assessment, Computer science, ComputerApplications_COMPUTERSINOTHERSYSTEMS, Robotic surgery, Action selection, Domain (software engineering), Adaptive training, Formative assessment, 03 medical and health sciences, Engineering management, 0302 clinical medicine, Operating theater, Summative assessment, Robotic surgical training, 030220 oncology & carcinogenesis, Adaptive system, 030211 gastroenterology & hepatology, State (computer science)
Abstract

Robot-assisted surgery training is shifting towards simulation-based training. Challenges that accompany this shift are high costs, working hour regulations and the high stakes aspects of the surgery domain. Adaptive training could be a possible solution to reduce the problems. First, an adaptive system needs diagnostic data with which the system can make an action selection. A scoping literature search was performed to give an overview of the state of the research regarding diagnostic requirements. Diagnostic metrics should be (a) useful for formative and not only summative assessment of trainee progress, (b) valid and reliable, (c) as nonintrusive as possible for the trainee, (d) predictive of future performance in the operating theater (e) explanatory, and (f) suitable for real-time assessment of trainee’s learning state. For a more in-depth understanding, further research is needed into which simulator parameters can be used as diagnostic metrics that can be assessed in real-time. A possible framework for adaptive training systems is discussed, and future research topics are presented.

Published
2019

99. The Fourth International Defense and Homeland Security Simulation Workshop 2014

Author

Bruzzone, Agostino, Frydman, Claudia, Longo, Francesco, SOKOLOWSKI, John, SOTTILARE, Robert, Dipartimento di Ingegneria della produzione, termoenergetica e modelli matematici (DIPTEM), Università degli studi di Genova = University of Genoa (UniGe), Laboratoire des Sciences de l'Information et des Systèmes (LSIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Arts et Métiers Paristech ENSAM Aix-en-Provence-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Messina = University of Messina (UniMe), Universita degli studi di Genova, Centre National de la Recherche Scientifique (CNRS)-Arts et Métiers Paristech ENSAM Aix-en-Provence-Université de Toulon (UTLN)-Aix Marseille Université (AMU), and Università degli studi di Messina

Subjects
[INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, [INFO]Computer Science [cs], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS
Abstract

National audience; no abstract

Published
2014

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