Adapting learning activities selection in an intelligent tutoring system to affect

Chinasa Odo

doi:10.1007/978-3-319-93846-2_98

Adapting learning activities selection in an intelligent tutoring system to affect

Chinasa Odo^*

^*Corresponding author for this work

Computing Science

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

1 Citation (Scopus)

Abstract

My PhD focuses on adapting learning activities selection to learner affect in an intelligent tutoring system. The research aims to investigate the affective states considered for adapting learning activity selection, and how to adapt to these. It also seeks to know how learner’s affective state can be obtained through tutor-learner interaction rather than via sensors or questionnaires. The research will use of a mixture of qualitative and quantitative methods to achieve these aims. This research will significantly contribute to the area of intelligent tutoring technology by providing more insights into how to adapt to affective states, and improve the delivery of learning. The result will lead to an algorithm for learning activity selection based on affect, which also incorporates other relevant learner characteristics, such as personalty, that moderate affect.

Original language	English
Title of host publication	Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings
Editors	Rose Luckin, Kaska Porayska-Pomsta, Benedict du Boulay, Manolis Mavrikis, Carolyn Penstein Rosé, Bruce McLaren, Roberto Martinez-Maldonado, H. Ulrich Hoppe
Publisher	Springer Verlag
Pages	521-525
Number of pages	5
ISBN (Print)	9783319938455
DOIs	https://doi.org/10.1007/978-3-319-93846-2_98
Publication status	Published - 20 Jun 2018
Event	19th International Conference on Artificial Intelligence in Education, AIED 2018 - London, United Kingdom Duration: 27 Jun 2018 → 30 Jun 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10948 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Artificial Intelligence in Education, AIED 2018
Country/Territory	United Kingdom
City	London
Period	27/06/18 → 30/06/18

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.

Keywords

Affective state
Learning activity selection
Personalization

Access to Document

10.1007/978-3-319-93846-2_98

Cite this

Odo, C. (2018). Adapting learning activities selection in an intelligent tutoring system to affect. In R. Luckin, K. Porayska-Pomsta, B. du Boulay, M. Mavrikis, C. Penstein Rosé, B. McLaren, R. Martinez-Maldonado, & H. U. Hoppe (Eds.), Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings (pp. 521-525). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10948 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-93846-2_98

Adapting learning activities selection in an intelligent tutoring system to affect. / Odo, Chinasa.
Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings. ed. / Rose Luckin; Kaska Porayska-Pomsta; Benedict du Boulay; Manolis Mavrikis; Carolyn Penstein Rosé; Bruce McLaren; Roberto Martinez-Maldonado; H. Ulrich Hoppe. Springer Verlag, 2018. p. 521-525 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10948 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Odo, C 2018, Adapting learning activities selection in an intelligent tutoring system to affect. in R Luckin, K Porayska-Pomsta, B du Boulay, M Mavrikis, C Penstein Rosé, B McLaren, R Martinez-Maldonado & HU Hoppe (eds), Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10948 LNAI, Springer Verlag, pp. 521-525, 19th International Conference on Artificial Intelligence in Education, AIED 2018, London, United Kingdom, 27/06/18. https://doi.org/10.1007/978-3-319-93846-2_98

Odo C. Adapting learning activities selection in an intelligent tutoring system to affect. In Luckin R, Porayska-Pomsta K, du Boulay B, Mavrikis M, Penstein Rosé C, McLaren B, Martinez-Maldonado R, Hoppe HU, editors, Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings. Springer Verlag. 2018. p. 521-525. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-93846-2_98

Odo, Chinasa. / Adapting learning activities selection in an intelligent tutoring system to affect. Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings. editor / Rose Luckin ; Kaska Porayska-Pomsta ; Benedict du Boulay ; Manolis Mavrikis ; Carolyn Penstein Rosé ; Bruce McLaren ; Roberto Martinez-Maldonado ; H. Ulrich Hoppe. Springer Verlag, 2018. pp. 521-525 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "My PhD focuses on adapting learning activities selection to learner affect in an intelligent tutoring system. The research aims to investigate the affective states considered for adapting learning activity selection, and how to adapt to these. It also seeks to know how learner{\textquoteright}s affective state can be obtained through tutor-learner interaction rather than via sensors or questionnaires. The research will use of a mixture of qualitative and quantitative methods to achieve these aims. This research will significantly contribute to the area of intelligent tutoring technology by providing more insights into how to adapt to affective states, and improve the delivery of learning. The result will lead to an algorithm for learning activity selection based on affect, which also incorporates other relevant learner characteristics, such as personalty, that moderate affect.",

keywords = "Affective state, Learning activity selection, Personalization",

author = "Chinasa Odo",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 19th International Conference on Artificial Intelligence in Education, AIED 2018 ; Conference date: 27-06-2018 Through 30-06-2018",

year = "2018",

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Adapting learning activities selection in an intelligent tutoring system to affect

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