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Help Seekers vs. Help Accepters: Understanding Student Engagement with a Mentor Agent

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Artificial Intelligence in Education (AIED 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13916))

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Abstract

Help from virtual pedagogical agents has the potential to improve student learning. Yet students often do not seek help when they need it, do not use help effectively, or ignore the agent’s help altogether. This paper seeks to better understand students’ patterns of accepting and seeking help in a computer-based science program called Betty’s Brain. Focusing on student interactions with the mentor agent, Mr. Davis, we examine the factors associated with patterns of help acceptance and help seeking; the relationship between help acceptance and help seeking; and how each behavior is related to learning outcomes. First, we examine whether students accepted help from Mr. Davis, operationalized as whether they followed his suggestions to read specific textbook pages. We find a significant positive relationship between help acceptance and student post-test scores. Despite this, help accepters made fewer positive statements about Mr. Davis in the interviews. Second, we identify how many times students proactively sought help from Mr. Davis. Students who most frequently sought help demonstrated more confusion while learning (measured using an interaction-based ML-based detector); tended to have higher science anxiety; and made more negative statements about Mr. Davis, compared to those who made few or no requests. However, help seeking was not significantly related to post-test scores. Finally, we draw from the qualitative interviews to consider how students understand and articulate their experiences with help from Mr. Davis.

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Acknowledgments

This work was supported by NSF #DRL-1561567. Elena G. van Stee was supported by a fellowship from the Institute of Education Sciences under Award #3505B200035 to the University of Pennsylvania during her work on this project. Taylor Heath was supported by a NIH T32 Grant under award #5T32HD007242-40. The opinions expressed are those of the authors and do not represent views of the funding agencies.

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Authors and Affiliations

  1. University of Pennsylvania, Philadelphia, USA

    Elena G. van Stee, Taylor Heath, Ryan S. Baker, J. M. Alexandra L. Andres & Jaclyn Ocumpaugh

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  1. Elena G. van Stee
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  2. Taylor Heath
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  3. Ryan S. Baker
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  4. J. M. Alexandra L. Andres
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  5. Jaclyn Ocumpaugh
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Corresponding author

Correspondence to Elena G. van Stee .

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Editors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    Ning Wang

  2. University of British Columbia, Vancouver, BC, Canada

    Genaro Rebolledo-Mendez

  3. North Carolina State University, Raleigh, NC, USA

    Noboru Matsuda

  4. Despacho 3.01, UNED-Grupo de Investigación aDeNu, Madrid, Spain

    Olga C. Santos

  5. University of Leeds, Leeds, UK

    Vania Dimitrova

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van Stee, E.G., Heath, T., Baker, R.S., Andres, J.M.A.L., Ocumpaugh, J. (2023). Help Seekers vs. Help Accepters: Understanding Student Engagement with a Mentor Agent. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2023. Lecture Notes in Computer Science(), vol 13916. Springer, Cham. https://doi.org/10.1007/978-3-031-36272-9_12

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  • DOI: https://doi.org/10.1007/978-3-031-36272-9_12

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