Affect sequences and learning in Betty's brain

Juliana Ma Alexandra L. Andres, Luc Paquette, Jaclyn Ocumpaugh, Yang Jiang, Ryan S. Baker, Shamya Karumbaiah, Stefan Slater, Nigel Bosch, Anabil Munshi, Allison Moore, Gautam Biswas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Education research has explored the role of students' affective states in learning, but some evidence suggests that existing models may not fully capture the meaning or frequency of how students transition between different states. In this study we examine the patterns of educationally-relevant affective states within the context of Betty's Brain, an open-ended, computer-based learning system used to teach complex scientific processes. We examine three types of affective transitions based on similarity with the theorized D'Mello and Graesser model, transition between two affective states, and the sustained instances of certain states. We correlate of the frequency of these patterns with learning outcomes and our findings suggest that boredom is a powerful indicator of students' knowledge, but not necessarily indicative of learning. We discuss our findings within the context of both research and theory on affect dynamics and the implications for pedagogical and system design.

Original languageEnglish (US)
Title of host publicationProceedings of the 9th International Conference on Learning Analytics and Knowledge
Subtitle of host publicationLearning Analytics to Promote Inclusion and Success, LAK 2019
PublisherAssociation for Computing Machinery,
Pages383-390
Number of pages8
ISBN (Electronic)9781450362566
DOIs
StatePublished - Mar 4 2019
Event9th International Conference on Learning Analytics and Knowledge, LAK 2019 - Tempe, United States
Duration: Mar 4 2019Mar 8 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference9th International Conference on Learning Analytics and Knowledge, LAK 2019
CountryUnited States
CityTempe
Period3/4/193/8/19

Fingerprint

Brain
Students
Learning systems
Education
Systems analysis

Keywords

  • Affect
  • Affect dynamics
  • Learning analytics

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Andres, J. M. A. L., Paquette, L., Ocumpaugh, J., Jiang, Y., Baker, R. S., Karumbaiah, S., ... Biswas, G. (2019). Affect sequences and learning in Betty's brain. In Proceedings of the 9th International Conference on Learning Analytics and Knowledge: Learning Analytics to Promote Inclusion and Success, LAK 2019 (pp. 383-390). (ACM International Conference Proceeding Series). Association for Computing Machinery,. https://doi.org/10.1145/3303772.3303807

Affect sequences and learning in Betty's brain. / Andres, Juliana Ma Alexandra L.; Paquette, Luc; Ocumpaugh, Jaclyn; Jiang, Yang; Baker, Ryan S.; Karumbaiah, Shamya; Slater, Stefan; Bosch, Nigel; Munshi, Anabil; Moore, Allison; Biswas, Gautam.

Proceedings of the 9th International Conference on Learning Analytics and Knowledge: Learning Analytics to Promote Inclusion and Success, LAK 2019. Association for Computing Machinery, 2019. p. 383-390 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Andres, JMAL, Paquette, L, Ocumpaugh, J, Jiang, Y, Baker, RS, Karumbaiah, S, Slater, S, Bosch, N, Munshi, A, Moore, A & Biswas, G 2019, Affect sequences and learning in Betty's brain. in Proceedings of the 9th International Conference on Learning Analytics and Knowledge: Learning Analytics to Promote Inclusion and Success, LAK 2019. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 383-390, 9th International Conference on Learning Analytics and Knowledge, LAK 2019, Tempe, United States, 3/4/19. https://doi.org/10.1145/3303772.3303807
Andres JMAL, Paquette L, Ocumpaugh J, Jiang Y, Baker RS, Karumbaiah S et al. Affect sequences and learning in Betty's brain. In Proceedings of the 9th International Conference on Learning Analytics and Knowledge: Learning Analytics to Promote Inclusion and Success, LAK 2019. Association for Computing Machinery,. 2019. p. 383-390. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3303772.3303807
Andres, Juliana Ma Alexandra L. ; Paquette, Luc ; Ocumpaugh, Jaclyn ; Jiang, Yang ; Baker, Ryan S. ; Karumbaiah, Shamya ; Slater, Stefan ; Bosch, Nigel ; Munshi, Anabil ; Moore, Allison ; Biswas, Gautam. / Affect sequences and learning in Betty's brain. Proceedings of the 9th International Conference on Learning Analytics and Knowledge: Learning Analytics to Promote Inclusion and Success, LAK 2019. Association for Computing Machinery, 2019. pp. 383-390 (ACM International Conference Proceeding Series).
@inproceedings{c6d8e6447ae147b992d8a7b0ae399dc3,
title = "Affect sequences and learning in Betty's brain",
abstract = "Education research has explored the role of students' affective states in learning, but some evidence suggests that existing models may not fully capture the meaning or frequency of how students transition between different states. In this study we examine the patterns of educationally-relevant affective states within the context of Betty's Brain, an open-ended, computer-based learning system used to teach complex scientific processes. We examine three types of affective transitions based on similarity with the theorized D'Mello and Graesser model, transition between two affective states, and the sustained instances of certain states. We correlate of the frequency of these patterns with learning outcomes and our findings suggest that boredom is a powerful indicator of students' knowledge, but not necessarily indicative of learning. We discuss our findings within the context of both research and theory on affect dynamics and the implications for pedagogical and system design.",
keywords = "Affect, Affect dynamics, Learning analytics",
author = "Andres, {Juliana Ma Alexandra L.} and Luc Paquette and Jaclyn Ocumpaugh and Yang Jiang and Baker, {Ryan S.} and Shamya Karumbaiah and Stefan Slater and Nigel Bosch and Anabil Munshi and Allison Moore and Gautam Biswas",
year = "2019",
month = "3",
day = "4",
doi = "10.1145/3303772.3303807",
language = "English (US)",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery,",
pages = "383--390",
booktitle = "Proceedings of the 9th International Conference on Learning Analytics and Knowledge",

}

TY - GEN

T1 - Affect sequences and learning in Betty's brain

AU - Andres, Juliana Ma Alexandra L.

AU - Paquette, Luc

AU - Ocumpaugh, Jaclyn

AU - Jiang, Yang

AU - Baker, Ryan S.

AU - Karumbaiah, Shamya

AU - Slater, Stefan

AU - Bosch, Nigel

AU - Munshi, Anabil

AU - Moore, Allison

AU - Biswas, Gautam

PY - 2019/3/4

Y1 - 2019/3/4

N2 - Education research has explored the role of students' affective states in learning, but some evidence suggests that existing models may not fully capture the meaning or frequency of how students transition between different states. In this study we examine the patterns of educationally-relevant affective states within the context of Betty's Brain, an open-ended, computer-based learning system used to teach complex scientific processes. We examine three types of affective transitions based on similarity with the theorized D'Mello and Graesser model, transition between two affective states, and the sustained instances of certain states. We correlate of the frequency of these patterns with learning outcomes and our findings suggest that boredom is a powerful indicator of students' knowledge, but not necessarily indicative of learning. We discuss our findings within the context of both research and theory on affect dynamics and the implications for pedagogical and system design.

AB - Education research has explored the role of students' affective states in learning, but some evidence suggests that existing models may not fully capture the meaning or frequency of how students transition between different states. In this study we examine the patterns of educationally-relevant affective states within the context of Betty's Brain, an open-ended, computer-based learning system used to teach complex scientific processes. We examine three types of affective transitions based on similarity with the theorized D'Mello and Graesser model, transition between two affective states, and the sustained instances of certain states. We correlate of the frequency of these patterns with learning outcomes and our findings suggest that boredom is a powerful indicator of students' knowledge, but not necessarily indicative of learning. We discuss our findings within the context of both research and theory on affect dynamics and the implications for pedagogical and system design.

KW - Affect

KW - Affect dynamics

KW - Learning analytics

UR - http://www.scopus.com/inward/record.url?scp=85062792861&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062792861&partnerID=8YFLogxK

U2 - 10.1145/3303772.3303807

DO - 10.1145/3303772.3303807

M3 - Conference contribution

T3 - ACM International Conference Proceeding Series

SP - 383

EP - 390

BT - Proceedings of the 9th International Conference on Learning Analytics and Knowledge

PB - Association for Computing Machinery,

ER -