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

  • Jason M. Harley
  • Cassia K. Carter
  • Niki Papaionnou
  • François Bouchet
  • Ronald S. Landis
  • Roger Azevedo
  • Lana Karabachian


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.


Emotions Agent-directed emotions Trait emotions  Personality traits Pedagogical agents Intelligent tutoring systems Adaptivity 



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.


  1. Arroyo, I., Burleson, W., Tai, M., Muldner, K., Woolf, B.P.: Gender differences in the use and benefit of advanced learning technologies for mathematics. J. Educ. Psychol. 105, 957–969 (2013)CrossRefGoogle Scholar
  2. Arroyo, I., Woolf, B.P., Royer, J.M., Tai, M.: Affective gendered learning companions. In: Dimitrova, V., Mizoguchi, R., du Boulay, B., Graesser, A. (eds.) Proceedings of the International Conference on Artificial Intelligence in Education, pp. 41–48. IOS Press, Amsterdam (2009)Google Scholar
  3. Azevedo, R., Aleven, V. (eds.): International Handbook of Metacognition and Learning Technologies. Springer, Amsterdam (2013)Google Scholar
  4. Azevedo, R., Behnagh, R., Duffy, M., Harley, J., Trevors, G.: Metacognition and self regulated learning with advanced learning technologies. In: Jonassen, D., Land, S. (eds.) Theoretical Foundations of Learning Environments, 2nd edn, pp. 171–197. Erlbaum, Mahwah (2012)Google Scholar
  5. Azevedo, R., Chauncey Strain, A.D.: Integrating cognitive, metacognitive, and affective regulatory processes with MetaTutor. In: Calvo, R.A., D’Mello, S.K. (eds.) New Perspectives on Affect and Learning Technologies, pp. 141–154. Springer, Amsterdam (2011)CrossRefGoogle Scholar
  6. Azevedo, R., Harley, J., Trevors, G., Feyzi-Behnagh, R., Duffy, M., Bouchet, F., Landis, R.S.: Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In: Azevedo, R., Aleven, V. (eds.) International Handbook of Metacognition and Learning Technologies, pp. 427–449. Springer, Amsterdam (2013)CrossRefGoogle Scholar
  7. Azevedo, R., Moos, D.C., Johnson, A.M., Chauncey, A.D.: Measuring cognitive and metacognitive regulatory processes during hypermedia learning: issues and challenges. Educational Psychologist 45, 210–223 (2010)CrossRefGoogle Scholar
  8. Azevedo, R., Witherspoon, A., Chauncey, A., Burkett, C., Fike, A.: MetaTutor: A MetaCognitive tool for enhancing self-regulated learning. In: Pirrone, R., Azevedo, R., Biswas, G. (eds.) Proceedings of the AAAI Fall Symposium on Cognitive and Metacognitive Educational Systems, pp. 14–19. Association for the Advancement of Artificial Intelligence (AAAI) Press, Menlo Park (2009)Google Scholar
  9. Bauer, K.W., Liang, Q.: The effect of personality and precollege characteristics on first-year activities and academic performance. J. Coll. Stud. Dev. 44, 277–290 (2003)CrossRefGoogle Scholar
  10. Baylor, A.L., Kim, S.: Designing nonverbal communication for pedagogical agents: when less is more. Comput. Human Behav. 25(2), 450–457 (2009)CrossRefGoogle Scholar
  11. Bidjerano, T., Yun Dai, D.: The relationship between the Big-Five Model of personality and self-regulated learning strategies. Learn. Individ. Diff. 17(1), 69–81 (2007)CrossRefGoogle Scholar
  12. Blanchard, E., Frasson, C.: Easy creation of game-like learning environments. Paper presented at the Workshop on Teaching with Robots and Agents held in conjunction with the 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan (2006)Google Scholar
  13. Bouchet, F., Harley, J.M., Azevedo, R.: The impact of different pedagogical agents’ adaptive self-regulated prompting strategies with MetaTutor. In: Lane, C.H., Yacef, K., Mostow, J., Pavik, P. (eds.) Artificial Intelligence in Education. Lecture notes in computer science, vol. 7926, pp. 815–819. Springer, Berlin (2013)CrossRefGoogle Scholar
  14. Bouchet, F., Sansonnet, J.P.: Influence of personality traits on the rational process of cognitive agents. 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, pp. 81–88. IEEE Computer Society, Lyon (2011)Google Scholar
  15. Busato, V.V., Prins, F.J., Elshout, J.J., Hamaker, C.: Intellectual ability, learning style, personality, achievement motivation and academic success of psychology students in higher education. Pers. Individ. Diff. 29, 1057–1068 (2000)CrossRefGoogle Scholar
  16. Calvo, R.A., Mac Kim, S.: Emotions in text: dimensional and categorical models. Comput. Intell. pp. 1–17, doi: 10.1111/j.1467-8640.2012.00456.x (2013)
  17. Calvo, R.A., D’Mello, S. (eds.): New Perspectives on Affect and Learning Technologies. Springer, New York (2011)Google Scholar
  18. Calvo, R.A., D’Mello, A.C.: Frontiers of affect-aware learning technologies. Intel. Syst. 27(6), 86–89 (2012)CrossRefGoogle Scholar
  19. Chamorro-Premuzic, T., Furnham, A.: Personality traits and academic examination performance. Eur. J. Pers. 17, 237–250 (2003a)CrossRefGoogle Scholar
  20. Chamorro-Premuzic, T., Furnham, A.: Personality predicts academic performance: evidence from two longitudinal university samples. J. Res. Pers. 37, 319–338 (2003b)CrossRefGoogle Scholar
  21. Chamorro-Premuzic, T., Furnham, A.: A possible model for explaining the personality-intelligence interface. Br. J. Psychol. 95, 249–264 (2004)CrossRefGoogle Scholar
  22. Chamorro-Premuzic, T., Furnham, A.: Personality and Intellectual Competence. Erlbaum, Mahwah (2005)Google Scholar
  23. Chamorro-Premuzic, T., Furnham, A.: Intellectual competence and the intelligent personality: a third way in differential psychology. Rev. General Psychol. 10, 251–267 (2006)CrossRefGoogle Scholar
  24. Chamorro-Premuzic, T., Furnham, A.: Mainly openness: the relationship between the Big Five personality traits and learning approaches. Learn. Individ. Diff. 19, 524–529 (2009)CrossRefGoogle Scholar
  25. Chauncey-Strain, A., Azevedo, R., D’Mello, S.: Exploring relationships between learners’ affective states, metacognitive processes, and learning outcomes. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) Proceedings of the 11th International Conference of Intelligent Tutoring Systems, pp. 59–64. Amsterdam (2012)Google Scholar
  26. Conati, C., Maclaren, H.: Empirically building and evaluating a probabilistic model of user affect. User Model. User-Adapted Interact. 19, 267–303 (2009)CrossRefGoogle Scholar
  27. Conard, M.A.: Aptitude is not enough: how personality and behavior predict academic performance. J. Res. Pers. 40, 339–346 (2006)CrossRefGoogle Scholar
  28. Conrad, N., Patry, M.W.: Conscientiousness and academic performance: a mediational analysis. Int. J. Scholarsh. Teach. Learn. 6(1), 1–14 (2012)Google Scholar
  29. Costa, P.T., McCrae, R.R.: Normal personality assessment in clinical practice: the NEO personality inventory. Psychol. Assess. 4(1), 5–13 (1992)CrossRefGoogle Scholar
  30. Cowley, B., Charles, D.: Behavelets: a method for practical player modeling using psychology-based player traits and domain specific features. User Model. User-Adapted Interact. (in press; this issue)Google Scholar
  31. Digman, J.M.: Personality structure: emergence of the five factor model. Annu. Rev. Psychol. 41, 417–440 (1990)CrossRefGoogle Scholar
  32. D’Mello, S.K.: A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. J. Educ. Psychol. 105(4), 1082–1099 (2013)CrossRefGoogle Scholar
  33. D’Mello, S., Graesser, A.: AutoTutor and Affective Autotutor: learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems, 2(4), 1–39 (2013)Google Scholar
  34. D’Mello, S., Lehman, B., Pekrun, R., Graesser, A.: Confusion can be beneficial for learning. Learn. Instr. 29, 153–170 (2014)CrossRefGoogle Scholar
  35. D’Mello, S.K., Lehman, B.A., Person, N.: Monitoring affect states during effortful problem solving activities. Int. J. Artif. Intel. Educ. 20(4), 361–389 (2010)Google Scholar
  36. D’Mello, S.K., Lehman, B., Graesser, A.: A motivationally supportive affect-sensitive AutoTutor. In: Calvo, R.A., D’Mello, S. (eds.) New Perspectives on Affect and Learning Technologies, pp. 113–126. Springer, New York (2011)CrossRefGoogle Scholar
  37. Doce, T., Dias, J., Prada, R., Paiva, A.: Creating individual agents through personality traits. In: Allbeck, J., Badler, N., Bickmore, T., Pelachaud, C., Safonova, A. (eds.) Intelligent Virtual Agents, vol. 6356, pp. 257–264. Springer, Berlin (2010)CrossRefGoogle Scholar
  38. Donnellan, M.B., Oswald, F.L., Baird, B.M., Lucas, R.E.: The mini-IPIP scales: tiny-yet-effective measures of the Big Five factors of personality. Psychol. Assess. 18, 192–203 (2006)CrossRefGoogle Scholar
  39. du Boulay, B., Avramides, K., Luckin, R., Martinez-Miron, E., Mendez, G.R., Carr, A.: Towards systems that care: a conceptual framework based on motivation, metacognition, and affect. Int. J. Artif. Intel. Educ. 20, 197–229 (2010)Google Scholar
  40. Duckworth, A.L., Seligman, M.E.P.: Self-discipline outdoes IQ in predicting academic performance of adolescents. Psychol. Sci. 16, 939–944 (2005)CrossRefGoogle Scholar
  41. Duff, A., Boyle, E., Dunleavy, K., Ferguson, J.: The relationship between personality, approach to learning and academic performance. Pers. Individ. Diff. 36(8), 1907–1920 (2004)CrossRefGoogle Scholar
  42. Duffy, M.C., Azevedo, R.: Motivation matters: interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. Comput. Human Behav. 52, 338–348 (2015)CrossRefGoogle Scholar
  43. Dragon, T., Arroyo, I., Woolf, B., Burleson, W., El Kaliouby, R., Eydgahi, H.: Viewing student affect and learning through classroom observation and physical sensors. In: Woolf, B., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) Intel. Tutoring Syst. Lecture notes in computer science, vol. 5091, pp. 29–39. Springer, Berlin (2008)CrossRefGoogle Scholar
  44. Ekman, P.: An argument for basic emotions. Cognit. Emotion 6, 169 (1992)CrossRefGoogle Scholar
  45. Farnadi, G., Sitaraman, G., Sushmita, S., Celli, F., Kosinski, M., Stillwell, D., Davalos, S., Moens, M.-F., De Cock, M.: Computational personality recognition in social media. User Model. User-Adapted Interact. (in press / this issue)Google Scholar
  46. Farsides, T., Woodfield, R.: Individual differences and undergraduate academic success: the roles of personality, intelligence, and application. Person. Individ. Diff. 34, 1225–1243 (2003)CrossRefGoogle Scholar
  47. Funder, D.C.: Personality. Annu. Rev. Psychol. 52, 197–221 (2001)CrossRefGoogle Scholar
  48. Goldberg, L.R.: A broad-bandwidth, public-domain, personality inventory measuring the lower-level facets of several five-factor models. In: Mervielde, I., Deary, I., De Fruyt, F., Ostendorf, F. (eds.) Personality Psychology in Europe, vol. 7, pp. 7–28. Tilburg University Press, Tilburg (1999)Google Scholar
  49. Graesser, A.C., D’Mello, S.K.: Emotions in advanced learning technologies. In: Pekrun, R., Linnenbrink-Garcia, L. (eds.) Handbook of Emotions and Education, pp. 473–493. Taylor & Francis, New York (2014)Google Scholar
  50. Graesser, A., D’Mello, S.K.: Emotions during the learning of difficult material. In: Ross, B. (ed.) Psychology of Learning and Motivation, vol. 57, pp. 183–226. Elsevier, San Diego (2012)CrossRefGoogle Scholar
  51. Gray, E.K., Watson, D.: General and specific traits of personality and their relation to sleep and academic performance. J. Person. 70, 177–206 (2002)CrossRefGoogle Scholar
  52. Gross, J.J.: The future’s so bright, I gotta wear shades. Emotion Rev. 2, 212–216 (2010)CrossRefGoogle Scholar
  53. Harley, J.M.: Measuring emotions: a survey of cutting-edge methodologies used in computer-based learning environment research. In: Tettegah, S., Gartmeier, M. (eds.) Emotions, Technology, Design, and Learning, pp. 89–114. Academic Press, Elsevier, London (2015)Google Scholar
  54. Harley, J.M., Azevedo, R.: Toward a feature-driven understanding of students’ emotions during interactions with agent-based learning environments: A selective review. Int. J. Gaming Computer-Mediated Simul. 6(3), 17–34 (2014)CrossRefGoogle Scholar
  55. Harley, J.M., Bouchet, F., Azevedo, R.: Measuring learners’ co-occurring emotional responses during their interaction with a pedagogical agent in MetaTutor. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) Intelligent Tutoring Systems. Lecture notes in computer science, vol. 7315, pp. 40–45. Springer, Berlin (2012)CrossRefGoogle Scholar
  56. Harley, J.M., Bouchet, F., Azevedo, R.: Aligning and comparing data on learners’ emotions experienced with MetaTutor. In: Lane, C.H., Yacef, K., Mostow, J., Pavik, P. (eds.) Artificial Intelligence in Education. Lecture notes in computer science, vol. 7926, pp. 61–70. Springer, Berlin (2013)CrossRefGoogle Scholar
  57. Harley, J.M., Bouchet, F., Papaionnou, N., Carter, C., Azevedo, R., Landis, R.: Assessing learning with MetaTutor, a multi-agent hypermedia learning environment. Paper presented at a symposium on Innovative Practices for Assessment in Computer Based Learning Environments at the annual meeting of the American Educational Research Association, Philadelphia (2014, April)Google Scholar
  58. Harley, J.M., Carter, C.K., Papaionnou, N., Bouchet, F., Landis, R.S., Azevedo, R., Karabachian, L.R.: Examining the predictive relationship between personality and emotion traits and learners’ agent-directed emotions. In: Conati, C., Heffernan, N. (eds.) Artificial Intelligence in Education. Lectures notes in artificial intelligence, vol. 9112, pp. 145–154. Springer, Switzerland (2015a)CrossRefGoogle Scholar
  59. Harley, J.M., Bouchet, F., Hussain, S., Azevedo, R., Calvo, R.: A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Comput. Human Behav. 48, 615–625 (2015b)CrossRefGoogle Scholar
  60. Harley, J.M., Lajoie, S.P., Frasson, C., Hall, N.C.: An integrated emotion-aware framework for intelligent tutoring systems. In: Conati, C., Heffernan, N. (eds.) Artificial Intelligence in Education. Lectures notes in artificial intelligence, vol. 9112, pp. 620–624. Springer, Switzerland (2015c)CrossRefGoogle Scholar
  61. Hochberg, Y., Tamhane, A.C.: Multiple Comparison Procedures. Wiley, New York (1987)CrossRefMATHGoogle Scholar
  62. Howard, P.J., Howard, J.M.: An introduction to the five-factor model for personality for human resource professionals. Available on: (1998)
  63. Hussain, M.S., Monkaresi, H., Calvo, R.: Categorical vs. dimensional representations in multimodal affect detection during learning. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) Intelligent Tutoring Systems. Lecture notes in computer science, vol. 7315, pp. 78–83. Springer, Berlin (2012)CrossRefGoogle Scholar
  64. Kanfer, R.: Motivation theory and industrial and organization psychology. In: Dunnette, M.D., Hough, L.M. (eds.) Handbook of Industrial and Organizational Psychology, 2nd edn, pp. 75–170. Consulting Psychologists Press, Palo Alto (1990)Google Scholar
  65. Lepri, B., Staiano, J., Shmueli, E., Pianesi, F., Pentland, A.: The role of personality in shaping social networks and mediating behavioral change. User Model. User-Adapted Interact. (in press / this issue)Google Scholar
  66. Matthews, G., Zeidner, M.: Traits, states, and trilogy of mind: an adaptive perspective on intellectual functioning. In: Dai, D.Y., Sternberg, R.J. (eds.) Motivation, Emotion, and Cognition: Integrative Perspectives on Intellectual Functioning and Development, pp. 143–174. Erlbaum, Mahwah (2004)Google Scholar
  67. McCrae, R.R., Costa Jr, P.T.: Personality trait structure as a human universal. Am. Psychol. 52, 509–516 (1997)CrossRefGoogle Scholar
  68. McQuiggan, S.W., Robison, J.L., Lester, J.C.: Affective transitions in narrative-centered learning environments. Educ. Technol. Soc. 13, 40–53 (2010)Google Scholar
  69. Noftle, E.E., Robins, R.W.: Personality predictors of academic outcomes: Big Five correlates of GPA and SAT scores. Person. Process. Individ. Diff. 93, 116–130 (2007)Google Scholar
  70. Norman, G.: Likert scales, levels of measurement and the “laws” of statistics. Adv. Health Sci. Educ. 15, 625–632 (2010)CrossRefGoogle Scholar
  71. O’Connor, M., Paunonen, S.: Big Five personality predictors of post-secondary academic performance. Person. Individ. Diff. 43, 971–990 (2007)CrossRefGoogle Scholar
  72. Pekrun, R.: Emotions as drivers of learning and cognitive development. In: Calvo, R.A., D’Mello, S.K. (eds.) New Perspectives on Affect and Learning Technologies, pp. 23–39. Springer, New York (2011)CrossRefGoogle Scholar
  73. Pekrun, R.: The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice. Educ. Psychol. Rev. 18, 315–341 (2006). doi: 10.1007/s10648-006-9029-9 CrossRefGoogle Scholar
  74. Pekrun, R., Daniels, L.M., Perry, R.P., Goetz, T., Stupnisky, R.H.: Boredom in achievement settings: exploring control-value antecedents and performance outcomes of a neglected emotion. J. Educ. Psychol. 102, 531–549 (2010)CrossRefGoogle Scholar
  75. Pekrun, R., Goetz, T., Frenzel-Anne, C., Petra, B., Perry, R.P.: Measuring emotions in students’ learning and performance: the achievement emotions questionnaire (AEQ). Contemp. Educ. Psychol. 36, 34–48 (2011)CrossRefGoogle Scholar
  76. Pekrun, R., Goetz, T., Titz, W., Perry, R.: Academic achievement emotions in students’ self-regulated learning and achievement: a program of quantitative and qualitative research. Educ. Psychol. 37, 91–206 (2002)CrossRefGoogle Scholar
  77. Pekrun, R., Perry, R.P.: Control-value theory of achievement emotions. In: Pekrun, R., Linnenbrink-Garcia, L. (eds.) International Handbook of Emotions in Education, pp. 120–141. Routledge, New York (2014)Google Scholar
  78. Pintrich, P.: The role of goal orientation in self-regulated learning. In: Boekaerts, M., Pintrich, P., Zeidner, M. (eds.) Handbook of Self-regulation, pp. 451–502. Academic Press, San Diego (2000)CrossRefGoogle Scholar
  79. Porayska-Pomsta, K., Mavrikis, M., D’Mello, S., Conati, C., Baker, R.S.: Knowledge elicitation methods for affect modeling in education. Int. J. Artif. Intell. Educ. 22, 107–140 (2013)Google Scholar
  80. Rao, A.S., Georgeff, M.P.: BDI agents: From theory to practice. In V. R. Lesser, S. Conry, Y. Demazeau., & M. Tokoro (Eds.), Proceedings of the 1st International Conference on Multi-Agent Systems (pp. 312–319). AAAI Press, Menlo Park (1995)Google Scholar
  81. Rowe, J.P., Shores, L.R., Mott, B.W., Lester, J.C.: Integrating learning problem solving, and engagement in narrative-centered learning environments. Int. J. Artif. Intell. Educ. 21, 115–133 (2011)Google Scholar
  82. Robison, J., McQuiggan, S., Lester, J.: Developing empirically based student personality profiles for affective feedback models. In: Aleven, V., Kay, J., Mostow, J. (eds.) Intell. Tutoring Syst., vol. 6094, pp. 285–295. Springer, Berlin (2010)CrossRefGoogle Scholar
  83. Russell, C.J., Bobko, P.: Moderated regression analysis and Likert scales: too coarse for comfort. J. Appl. Psychol. 77(3), 336–342 (1992)CrossRefGoogle Scholar
  84. Sabourin, J., Mott, B., Lester, J.C.: Modeling learner affect with theoretically grounded dynamic Bayesian networks. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) Affective Computing and Intelligent Interaction, vol. 6974, pp. 286–295. Springer, Berlin (2011)CrossRefGoogle Scholar
  85. Tabachnick, B.G., Fidell, L.S.: Using Multivariate Statistics, 5th edn. Pearson Education/Allyn and Bacon, Boston (2007)Google Scholar
  86. Taub, M., Azevedo, R., Bouchet, F., Khosravifar, B.: Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments? Comput. Human Behav. 39, 356–367 (2014)CrossRefGoogle Scholar
  87. Muis, K.R., Pekrun, R., Sinatra, G.M., Azevedo, R., Trevors, G., Meier, E., Heddy, B.C.: The curious case of climate change: testing a theoretical model of epistemic beliefs, epistemic emotions, and complex learning. Learn. Inst. 39, 168–183 (2015)CrossRefGoogle Scholar
  88. Wagerman, S.A., Funder, D.C.: Acquaintance reports of personality and academic achievement: a case for conscientiousness. J. Res. Person. 41, 221–229 (2007)CrossRefGoogle Scholar
  89. Winne, P.H., Azevedo, R.: Metacognition. In: Sawyer, K. (ed.) Cambridge Handbook of the Learning Sciences, 2nd edn, pp. 63–87. Cambridge University Press, Cambridge (2014)CrossRefGoogle Scholar
  90. Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., Picard, R.: Affect-aware tutors: Recognizing and responding to student affect. Int. J. Learn. Technol. 4, 129–164 (2009)CrossRefGoogle Scholar
  91. Woolf, B., Arroyo, I., Muldner, K., Burleson, W., Cooper, D., Dolan, R., Christopherson, R.M.: The effect of motivational learning companions on low-achieving students and students with learning disabilities. In: Aleven, V., Kay, J., Mostow, J. (eds.) Intelligent Tutoring Systems. Lecture notes in computer science, vol. 6094, pp. 327–337. Springer, Berlin (2010)CrossRefGoogle Scholar
  92. Zimmerman, B., Schunk, D.: Handbook of Self Regulation of Learning and Performance. Routledge, New York (2011)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Jason M. Harley
    • 1
    • 2
    • 3
  • Cassia K. Carter
    • 4
  • Niki Papaionnou
    • 4
  • François Bouchet
    • 5
  • Ronald S. Landis
    • 4
  • Roger Azevedo
    • 6
  • Lana Karabachian
    • 2
  1. 1.Department of Educational PsychologyUniversity of AlbertaEdmontonCanada
  2. 2.Department of Educational and Counselling PsychologyMcGill UniversityMontréalCanada
  3. 3.Computer Science and Operations ResearchUniversité de MontréalMontréalCanada
  4. 4.Department of PsychologyIllinois Institute of TechnologyChicagoUSA
  5. 5.CNRS, LIP6 UMR 7606Sorbonne Universités, UPMC Univ Paris 06ParisFrance
  6. 6.Department of PsychologyNorth Carolina State UniversityRaleighUSA

Personalised recommendations