Enhancing Engagement Modeling in Game-Based Learning Environments with Student-Agent Discourse Analysis

Alex Goslen; Nathan Henderson; Jonathan Rowe; Jiayi Zhang; Stephen Hutt; Jaclyn Ocumpaugh; Eric Wiebe; Kristy Elizabeth Boyer; Bradford Mott; James Lester

doi:10.1007/978-3-031-36336-8_105

Enhancing Engagement Modeling in Game-Based Learning Environments with Student-Agent Discourse Analysis

Alex Goslen, Nathan Henderson, Jonathan Rowe, Jiayi Zhang, Stephen Hutt, Jaclyn Ocumpaugh, Eric Wiebe, Kristy Elizabeth Boyer, Bradford Mott, James Lester

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

2 Scopus citations

Abstract

Pedagogical agents offer significant promise for engaging students in learning. In this paper, we investigate students’ conversational interactions with a pedagogical agent in a game-based learning environment for middle school science education. We utilize word embeddings of student-agent conversations along with features distilled from students’ in-game actions to induce predictive models of student engagement. An evaluation of the models’ accuracy and early prediction performance indicates that features derived from students’ conversations with the pedagogical agent yield the highest accuracy for predicting student engagement. Results also show that combining student problem-solving features and conversation features yields higher performance than a problem solving-only feature set. Overall, the findings suggest that student-agent conversations can greatly enhance student models for game-based learning environments.

Original language	English (US)
Title of host publication	Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky - 24th International Conference, AIED 2023, Proceedings
Editors	Ning Wang, Genaro Rebolledo-Mendez, Vania Dimitrova, Noboru Matsuda, Olga C. Santos
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	681-687
Number of pages	7
ISBN (Print)	9783031363351
DOIs	https://doi.org/10.1007/978-3-031-36336-8_105
State	Published - 2023
Externally published	Yes
Event	24th International Conference on Artificial Intelligence in Education , AIED 2023 - Tokyo, Japan Duration: Jul 3 2023 → Jul 7 2023

Publication series

Name	Communications in Computer and Information Science
Volume	1831 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	24th International Conference on Artificial Intelligence in Education , AIED 2023
Country/Territory	Japan
City	Tokyo
Period	7/3/23 → 7/7/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Discourse analysis
Game-based learning
Student engagement

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Publisher link

10.1007/978-3-031-36336-8_105

Find It

Find It at U of M Libraries

Cite this

Goslen, A., Henderson, N., Rowe, J., Zhang, J., Hutt, S., Ocumpaugh, J., Wiebe, E., Boyer, K. E., Mott, B., & Lester, J. (2023). Enhancing Engagement Modeling in Game-Based Learning Environments with Student-Agent Discourse Analysis. In N. Wang, G. Rebolledo-Mendez, V. Dimitrova, N. Matsuda, & O. C. Santos (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky - 24th International Conference, AIED 2023, Proceedings (pp. 681-687). (Communications in Computer and Information Science; Vol. 1831 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-36336-8_105

Enhancing Engagement Modeling in Game-Based Learning Environments with Student-Agent Discourse Analysis. / Goslen, Alex; Henderson, Nathan; Rowe, Jonathan et al.
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky - 24th International Conference, AIED 2023, Proceedings. ed. / Ning Wang; Genaro Rebolledo-Mendez; Vania Dimitrova; Noboru Matsuda; Olga C. Santos. Springer Science and Business Media Deutschland GmbH, 2023. p. 681-687 (Communications in Computer and Information Science; Vol. 1831 CCIS).

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

Goslen, A, Henderson, N, Rowe, J, Zhang, J, Hutt, S, Ocumpaugh, J, Wiebe, E, Boyer, KE, Mott, B & Lester, J 2023, Enhancing Engagement Modeling in Game-Based Learning Environments with Student-Agent Discourse Analysis. in N Wang, G Rebolledo-Mendez, V Dimitrova, N Matsuda & OC Santos (eds), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky - 24th International Conference, AIED 2023, Proceedings. Communications in Computer and Information Science, vol. 1831 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 681-687, 24th International Conference on Artificial Intelligence in Education , AIED 2023, Tokyo, Japan, 7/3/23. https://doi.org/10.1007/978-3-031-36336-8_105

Goslen A, Henderson N, Rowe J, Zhang J, Hutt S, Ocumpaugh J et al. Enhancing Engagement Modeling in Game-Based Learning Environments with Student-Agent Discourse Analysis. In Wang N, Rebolledo-Mendez G, Dimitrova V, Matsuda N, Santos OC, editors, Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky - 24th International Conference, AIED 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 681-687. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-36336-8_105

Goslen, Alex ; Henderson, Nathan ; Rowe, Jonathan et al. / Enhancing Engagement Modeling in Game-Based Learning Environments with Student-Agent Discourse Analysis. Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky - 24th International Conference, AIED 2023, Proceedings. editor / Ning Wang ; Genaro Rebolledo-Mendez ; Vania Dimitrova ; Noboru Matsuda ; Olga C. Santos. Springer Science and Business Media Deutschland GmbH, 2023. pp. 681-687 (Communications in Computer and Information Science).

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abstract = "Pedagogical agents offer significant promise for engaging students in learning. In this paper, we investigate students{\textquoteright} conversational interactions with a pedagogical agent in a game-based learning environment for middle school science education. We utilize word embeddings of student-agent conversations along with features distilled from students{\textquoteright} in-game actions to induce predictive models of student engagement. An evaluation of the models{\textquoteright} accuracy and early prediction performance indicates that features derived from students{\textquoteright} conversations with the pedagogical agent yield the highest accuracy for predicting student engagement. Results also show that combining student problem-solving features and conversation features yields higher performance than a problem solving-only feature set. Overall, the findings suggest that student-agent conversations can greatly enhance student models for game-based learning environments.",

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AB - Pedagogical agents offer significant promise for engaging students in learning. In this paper, we investigate students’ conversational interactions with a pedagogical agent in a game-based learning environment for middle school science education. We utilize word embeddings of student-agent conversations along with features distilled from students’ in-game actions to induce predictive models of student engagement. An evaluation of the models’ accuracy and early prediction performance indicates that features derived from students’ conversations with the pedagogical agent yield the highest accuracy for predicting student engagement. Results also show that combining student problem-solving features and conversation features yields higher performance than a problem solving-only feature set. Overall, the findings suggest that student-agent conversations can greatly enhance student models for game-based learning environments.

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ER -

Enhancing Engagement Modeling in Game-Based Learning Environments with Student-Agent Discourse Analysis

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Publisher link

Find It

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Cite this