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Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: towards emotionally-adaptive agent-based learning environments

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Abstract

The current study examined the relationships between learners’ (\(N = 123\)) personality traits, the emotions they typically experience while studying (trait studying emotions), and the emotions they reported experiencing as a result of interacting with four pedagogical agents (agent-directed emotions) in MetaTutor, an advanced multi-agent learning environment. Overall, significant relationships between a subset of trait emotions (trait anger, trait anxiety) and personality traits (agreeableness, conscientiousness, and neuroticism) were found for four agent-directed emotions (enjoyment, pride, boredom, and neutral) though the relationships differed between pedagogical agents. These results demonstrate that some trait emotions and personality traits can be used to predict learners’ emotions directed toward specific pedagogical agents (with different roles). Results provide suggestions for adapting pedagogical agents to support learners’ (with certain characteristics; e.g., high in neuroticism or agreeableness) experience of adaptive emotions (e.g., enjoyment) and minimize their experience on non-adaptive emotions (e.g., boredom). Such an approach presents a scalable and easily implementable method for creating emotionally-adaptive, agent-based learning environments, and improving learner-pedagogical agent interactions in order to support learning.

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Notes

  1. A drastically shortened version of this paper appeared in Harley et al. (2015a). This manuscript extends our work from the conference proceeding in a number of critical ways: First, the conference paper only examined the relationship between personality traits and trait emotions, and two of the four pedagogical agents that interacted with learners in MetaTutor. Second, research questions examining learning were not discussed at all. Third, the regression tables were not included in the conference paper, but are valuable to examine. Fourth, this journal article provides a more contextually-rich description of the theories guiding the paper, its methodology, results, and discussion of.

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Acknowledgments

The research presented in this paper has been supported by a doctoral and postdoctoral fellowship from the Fonds Québécois de recherche—Société et culture (FQRSC) and a Joseph-Armand Bombardier Canada Graduate Scholarship for Doctoral research from the Social Science and Humanities Research Council (SSHRC) awarded to the first author. This research has also been supported by funding awarded from the National Science Foundation (DRL 1008282), the Social Science and Humanities Research Council of Canada, and the Canada Research Chairs program. The authors would like to thank Lauren Agnew, Kelsey Anderson, Valérie Bélanger-Cantara, Reza Feyzi-Behnagh, Sophie Griscom, Nicholas Mudrick, Nicole Pacampara, Alejandra Segura, Victoria Stead, Gregory Trevors, Grace Wang, and Wook Yang for assisting in running participants, and Nathan C. Hall and Rebecca Maymon for their feedback on the paper.

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Authors and Affiliations

  1. Department of Educational Psychology, University of Alberta, 6-102 Education North, Edmonton, Alberta, T6G 2G5, Canada

    Jason M. Harley

  2. Department of Educational and Counselling Psychology, McGill University, 3700 McTavish Street 614, Montréal, QC, H3A 1Y2, Canada

    Jason M. Harley & Lana Karabachian

  3. Computer Science and Operations Research, Université de Montréal, 2920 Chemin de la Tour, Pavillon André-Aisenstadt 2194, Montréal, QC, H3C 3J7, Canada

    Jason M. Harley

  4. Department of Psychology, Illinois Institute of Technology, 3105 S. Dearborn St., Chicago, IL, 60616, USA

    Cassia K. Carter, Niki Papaionnou & Ronald S. Landis

  5. CNRS, LIP6 UMR 7606, Sorbonne Universités, UPMC Univ Paris 06, 4 place Jussieu, 75005, Paris, France

    François Bouchet

  6. Department of Psychology, North Carolina State University, 2310 Stinson Drive, Poe Hall 640, Raleigh, NC, 2765-7650, USA

    Roger Azevedo

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Appendices

Appendix 1

See Table 10.

Table 10 Agent directed emotions: Sam descriptive statistics
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Appendix 2

See Table 11.

Table 11 Agent directed emotions: Pam descriptive statistics
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Appendix 3

See Table 12.

Table 12 Agent directed emotions: Mary descriptive statistics
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Appendix 4

See Table 13.

Table 13 Agent directed emotions: Gavin descriptive statistics
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Harley, J.M., Carter, C.K., Papaionnou, N. et al. Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: towards emotionally-adaptive agent-based learning environments. User Model User-Adap Inter 26, 177–219 (2016). https://doi.org/10.1007/s11257-016-9169-7

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  • DOI: https://doi.org/10.1007/s11257-016-9169-7

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