The Mars and Venus Effect: The Influence of User Gender on the Effectiveness of Adaptive Task Support

  • Alexandria Katarina VailEmail author
  • Kristy Elizabeth Boyer
  • Eric N. Wiebe
  • James C. Lester
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9146)


Providing adaptive support to users engaged in learning tasks is the central focus of intelligent tutoring systems. There is evidence that female and male users may benefit differently from adaptive support, yet it is not understood how to most effectively adapt task support to gender. This paper reports on a study with four versions of an intelligent tutoring system for introductory computer programming offering different levels of cognitive (conceptual and problem-solving) and affective (motivational and engagement) support. The results show that female users reported significantly more engagement and less frustration with the affective support system than with other versions. In a human tutorial dialogue condition used for comparison, a consistent difference was observed between females and males. These results suggest the presence of the Mars and Venus Effect, a systematic difference in how female and male users benefit from cognitive and affective adaptive support. The findings point toward design principles to guide the development of gender-adaptive intelligent tutoring systems.


Gender effects Adaptive support Intelligent tutoring systems Affect Engagement Frustration 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arroyo, I., Burleson, W., Minghui, T., Muldner, K., Woolf, B.P.: Gender Differences in the Use and Benefit of Advanced Learning Technologies for Mathematics. Journal of Educational Psychology 105(4), 957–969 (2013)CrossRefGoogle Scholar
  2. 2.
    Arroyo, I., Woolf, B.P., Cooper, D.G., Burleson, W., Muldner, K.: The impact of animated pedagogical agents on girls’ and boys’ emotions, attitudes, behaviors and learning. In: Proceedings of the 11th International Conference on Advanced Learning Technologies, pp. 506–510. IEEE, Athens (2011)Google Scholar
  3. 3.
    Baker, R.S.J., D’Mello, S.K., Rodrigo, M.T., Graesser, A.C.: Better to Be Frustrated Than Bored: The Incidence, Persistence, and Impact of Learners’ Cognitive-affective States During Interactions with Three Different Computer-based Learning Environments. International Journal of Human-Computer Studies 68(4), 223–241 (2010)CrossRefGoogle Scholar
  4. 4.
    Belk, M., Germanakos, P., Fidas, C., Samaras, G.: A personalization method based on human factors for improving usability of user authentication tasks. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 13–24. Springer, Heidelberg (2014) Google Scholar
  5. 5.
    Bloom, B.S.: The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring. Educational Researcher 13(6), 4–16 (1984)CrossRefGoogle Scholar
  6. 6.
    Bousbia, N., Rebaï, I., Labat, J.M., Balla, A.: Learners’ navigation behavior identification based on trace analysis. User Modelling and User-Adapted Interaction 20(5), 455–494 (2010)CrossRefGoogle Scholar
  7. 7.
    Bouvier, P., Sehaba, K., Lavoué, E.: A trace-based approach to identifying users engagement and qualifying their engaged-behaviours in interactive systems: application to a social game. User Modeling and User-Adapted Interaction 24(5), 413–451 (2014)CrossRefGoogle Scholar
  8. 8.
    Boyer, K.E., Phillips, R., Wallis, M., Vouk, M.A., Lester, J.C.: Balancing cognitive and motivational scaffolding in tutorial dialogue. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 239–249. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  9. 9.
    Boyer, K.E., Vouk, M.A., Lester, J.C.: The influence of learner characteristics on task-oriented tutorial dialogue. In: Proceedings of the 13th International Conference on Artificial Intelligence in Education, pp. 365–372. IOS Press, Marina Del Rey (2007)Google Scholar
  10. 10.
    Burleson, W., Picard, R.W.: Gender-specific approaches to developing emotionally intelligent learning companions. IEEE Intelligent Systems 22(4), 62–69 (2007)CrossRefGoogle Scholar
  11. 11.
    Chi, M., VanLehn, K., Litman, D.J., Jordan, P.W.: Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies. User Modelling and User-Adapted Interaction 21(1–2), 137–180 (2011)CrossRefGoogle Scholar
  12. 12.
    Cohen, P.R., Perrault, C.R., Allen, J.F.: Beyond question answering. In: Strategies for Natural Language Processing, chap. 9, pp. 245–274. Psychology Press, New York (1982)Google Scholar
  13. 13.
    Cordick, A., McCuaig, J.: Adaptive tips for helping domain experts. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 397–402. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  14. 14.
    Craig, S.D., Graesser, A.C., Sullins, J., Gholson, B.: Affect and learning: An exploratory look into the role of affect in learning with AutoTutor. Journal of Educational Media 29(3), 241–250 (2004)CrossRefGoogle Scholar
  15. 15.
    Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Cambridge University Press, New York (1990) Google Scholar
  16. 16.
    Dennis, M., Masthoff, J., Mellish, C.: Adapting performance feedback to a learner’s conscientiousness. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 297–302. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  17. 17.
    Desmarais, M.C., Baker, R.S.J.: A review of recent advances in learner and skill modeling in intelligent learning environments. User Modelling and User-Adapted Interaction 22(1–2), 9–38 (2012)CrossRefGoogle Scholar
  18. 18.
    Goldin, I.M., Carlson, R.: Learner differences and hint content. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 522–531. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  19. 19.
    Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. Advances in Psychology 52, 139–183 (1988)CrossRefGoogle Scholar
  20. 20.
    Jackson, G.T., Graesser, A.C.: Content matters: an investigation of feedback categories within an ITS. In: Proceedings of the 13th International Conference on Artificial Intelligence in Education, pp. 127–134. Los Angeles, California, USA (2007)Google Scholar
  21. 21.
    Mitchell, C.M., Ha, E.Y., Boyer, K.E., Lester, J.C.: Learner characteristics and dialogue: recognising effective and student-adaptive tutorial strategies. International Journal of Learning Technology 8(4), 382–403 (2013)CrossRefGoogle Scholar
  22. 22.
    Mitrovic, A.: Fifteen years of constraint-based tutors: What we have achieved and where we are going. User Modelling and User-Adapted Interaction 22(1–2), 39–72 (2012)CrossRefGoogle Scholar
  23. 23.
    Muldner, K., Burleson, W., Van de Sande, B., VanLehn, K.: An analysis of students gaming behaviors in an intelligent tutoring system: predictors and impacts. User Modeling and User-Adapted Interaction 21(1–2), 99–135 (2011)CrossRefGoogle Scholar
  24. 24.
    O’Brien, H.L., Toms, E.G.: The development and evaluation of a survey to measure user engagement. Journal of the American Society for Information Science and Technology 61(1), 50–69 (2010)CrossRefGoogle Scholar
  25. 25.
    Pirolli, P., Kairam, S.: A knowledge-tracing model of learning from a social tagging system. User Modeling and User-Adapted Interaction 23(2–3), 139–168 (2013)CrossRefGoogle Scholar
  26. 26.
    Rahimi, Z., Hashemi, H.B.: Turn-taking behavior in a human tutoring corpus. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 778–782. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  27. 27.
    San Pedro, M.O.Z., Baker, R.S.J., Gowda, S.M., Heffernan, N.T.: Towards an understanding of affect and knowledge from student interaction with an intelligent tutoring system. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 41–50. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  28. 28.
    Su, J.M., Tseng, S.S., Lin, H.Y., Chen, C.H.: A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments. User Modeling and User-Adapted Interaction 21(1–2), 5–49 (2011)CrossRefGoogle Scholar
  29. 29.
    Vail, A.K., Boyer, K.E.: Adapting to personality over time: examining the effectiveness of dialogue policy progressions in task-oriented interaction. In: Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 41–50. Philadelphia, Pennsylvania, USA (2014)Google Scholar
  30. 30.
    VanLehn, K., Graesser, A.C., Jackson, G.T., Jordan, P.W., Olney, A., Rosé, C.P.: When Are Tutorial Dialogues More Effective Than Reading? Cognitive Science 31(1), 3–62 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alexandria Katarina Vail
    • 1
    Email author
  • Kristy Elizabeth Boyer
    • 1
  • Eric N. Wiebe
    • 2
  • James C. Lester
    • 1
  1. 1.Department of Computer ScienceNorth Carolina State UniversityRaleighUSA
  2. 2.Department of STEM EducationNorth Carolina State UniversityRaleighUSA

Personalised recommendations