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Cross-Cutting Support of Making and Explaining Decisions in Intelligent Tutoring Systems Using Cognitive Maps of Knowledge Diagnosis. / Uglev, Viktor; Sychev, Oleg; Gavrilova, Tatiana.

Intelligent Tutoring Systems - 18th International Conference, ITS 2022, Proceedings. ed. / Scott Crossley; Elvira Popescu. Springer Nature, 2022. p. 51-64 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13284 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Uglev, V, Sychev, O & Gavrilova, T 2022, Cross-Cutting Support of Making and Explaining Decisions in Intelligent Tutoring Systems Using Cognitive Maps of Knowledge Diagnosis. in S Crossley & E Popescu (eds), Intelligent Tutoring Systems - 18th International Conference, ITS 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13284 LNCS, Springer Nature, pp. 51-64, 18th International Conference on Intelligent Tutoring Systems, ITS 2022, Virtual, Online, 29/06/22. https://doi.org/10.1007/978-3-031-09680-8_5

APA

Uglev, V., Sychev, O., & Gavrilova, T. (2022). Cross-Cutting Support of Making and Explaining Decisions in Intelligent Tutoring Systems Using Cognitive Maps of Knowledge Diagnosis. In S. Crossley, & E. Popescu (Eds.), Intelligent Tutoring Systems - 18th International Conference, ITS 2022, Proceedings (pp. 51-64). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13284 LNCS). Springer Nature. https://doi.org/10.1007/978-3-031-09680-8_5

Vancouver

Uglev V, Sychev O, Gavrilova T. Cross-Cutting Support of Making and Explaining Decisions in Intelligent Tutoring Systems Using Cognitive Maps of Knowledge Diagnosis. In Crossley S, Popescu E, editors, Intelligent Tutoring Systems - 18th International Conference, ITS 2022, Proceedings. Springer Nature. 2022. p. 51-64. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-031-09680-8_5

Author

Uglev, Viktor ; Sychev, Oleg ; Gavrilova, Tatiana. / Cross-Cutting Support of Making and Explaining Decisions in Intelligent Tutoring Systems Using Cognitive Maps of Knowledge Diagnosis. Intelligent Tutoring Systems - 18th International Conference, ITS 2022, Proceedings. editor / Scott Crossley ; Elvira Popescu. Springer Nature, 2022. pp. 51-64 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{a54f63103dbf4398982443cb63cd55fa,
title = "Cross-Cutting Support of Making and Explaining Decisions in Intelligent Tutoring Systems Using Cognitive Maps of Knowledge Diagnosis",
abstract = "The paper concerns the problem of visual support of decision-making in intelligent tutoring systems and generating natural-language explanations of these decisions. A model of interpreting a learning situation and decision making about further learning on operational, tactical, and strategic levels considered from topic, competency, and learning-goal points of view. We show a case study of learning-situation visualization of analysis and synthesis of explanatory feedback for making operational, tactical, and strategic decisions. The method was evaluated with 3 groups of graduate students; the groups that received simplified Cognitive Maps of Knowledge Diagnosis along with textual explanations demonstrated higher trust in the system{\textquoteright}s decisions and were more likely to follow the recommendations. We conclude with recommendations for using cognitive maps of knowledge diagnosis for cross-cutting analysis of learning situations.",
keywords = "Cognitive maps of knowledge diagnosis, Cognitive visualization, Decision making, Digital learning footprint, Explanation of decisions, Intelligent tutoring systems",
author = "Viktor Uglev and Oleg Sychev and Tatiana Gavrilova",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 18th International Conference on Intelligent Tutoring Systems, ITS 2022 ; Conference date: 29-06-2022 Through 01-07-2022",
year = "2022",
doi = "10.1007/978-3-031-09680-8_5",
language = "English",
isbn = "9783031096792",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "51--64",
editor = "Scott Crossley and Elvira Popescu",
booktitle = "Intelligent Tutoring Systems - 18th International Conference, ITS 2022, Proceedings",
address = "Germany",

}

RIS

TY - GEN

T1 - Cross-Cutting Support of Making and Explaining Decisions in Intelligent Tutoring Systems Using Cognitive Maps of Knowledge Diagnosis

AU - Uglev, Viktor

AU - Sychev, Oleg

AU - Gavrilova, Tatiana

N1 - Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - The paper concerns the problem of visual support of decision-making in intelligent tutoring systems and generating natural-language explanations of these decisions. A model of interpreting a learning situation and decision making about further learning on operational, tactical, and strategic levels considered from topic, competency, and learning-goal points of view. We show a case study of learning-situation visualization of analysis and synthesis of explanatory feedback for making operational, tactical, and strategic decisions. The method was evaluated with 3 groups of graduate students; the groups that received simplified Cognitive Maps of Knowledge Diagnosis along with textual explanations demonstrated higher trust in the system’s decisions and were more likely to follow the recommendations. We conclude with recommendations for using cognitive maps of knowledge diagnosis for cross-cutting analysis of learning situations.

AB - The paper concerns the problem of visual support of decision-making in intelligent tutoring systems and generating natural-language explanations of these decisions. A model of interpreting a learning situation and decision making about further learning on operational, tactical, and strategic levels considered from topic, competency, and learning-goal points of view. We show a case study of learning-situation visualization of analysis and synthesis of explanatory feedback for making operational, tactical, and strategic decisions. The method was evaluated with 3 groups of graduate students; the groups that received simplified Cognitive Maps of Knowledge Diagnosis along with textual explanations demonstrated higher trust in the system’s decisions and were more likely to follow the recommendations. We conclude with recommendations for using cognitive maps of knowledge diagnosis for cross-cutting analysis of learning situations.

KW - Cognitive maps of knowledge diagnosis

KW - Cognitive visualization

KW - Decision making

KW - Digital learning footprint

KW - Explanation of decisions

KW - Intelligent tutoring systems

UR - http://www.scopus.com/inward/record.url?scp=85134160011&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/f5a1cdfc-85c7-3a82-ba64-0e9b43288ffb/

U2 - 10.1007/978-3-031-09680-8_5

DO - 10.1007/978-3-031-09680-8_5

M3 - Conference contribution

AN - SCOPUS:85134160011

SN - 9783031096792

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 51

EP - 64

BT - Intelligent Tutoring Systems - 18th International Conference, ITS 2022, Proceedings

A2 - Crossley, Scott

A2 - Popescu, Elvira

PB - Springer Nature

T2 - 18th International Conference on Intelligent Tutoring Systems, ITS 2022

Y2 - 29 June 2022 through 1 July 2022

ER -

ID: 98159477