Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

  • Jason M. Harley
  • Susanne P. Lajoie
  • Claude Frasson
  • Nathan C. Hall


A growing body of work on intelligent tutoring systems, affective computing, and artificial intelligence in education is exploring creative, technology-driven approaches to enhance learners’ experience of adaptive, positively-valenced emotions while interacting with advanced learning technologies. Despite this, there has been no published work to date that captures this topic’s breadth. We took up this grand challenge by integrating related empirical studies and existing conceptual work and proposing a theoretically-guided taxonomy for the development and improvement of emotion-aware systems. In particular, multiple strategies system developers may use to help learners experience positive emotions are mapped out, including those that require different amounts and types of information about the user, as well as when this information is required. Examples from the literature are provided to illustrate how different emotion-aware system approaches can be combined to take advantage of different types of data, both prior to and during the learner-system interaction. High-level system features that emotion-aware systems can tailor to learners in order to elicit positive emotions are also described and exemplified. Theoretically, the taxonomy is primarily informed by the control-value theory of achievement emotions (Pekrun 2006, 2011) and its assumptions about the relationship between distal and proximal antecedents and the elicitation and regulation of emotion. The taxonomy expands upon a dichotomy of emotion-aware systems proposed by D’Mello and Graesser (2015) and is intended to guide the design of emotion-aware systems that can foster positive emotions during learner-system interactions through the use of varied approaches, data sources, and design features.


Emotions Affect Emotion regulation Emotion-aware systems Advanced learning technologies Intelligent tutoring systems 



The research presented in this paper has been supported by a postdoctoral fellowship from the Fonds Québécois de recherche – Société et culture (FQRSC) awarded to the first author. This research has also been supported by funding from the Social Sciences and Humanities Research Council of Canada. The authors would like to thank Reinhard Pekrun and James Gross for their thoughts and feedback on similarities between their theory and model with regard to emotion regulation.


  1. Alexander, P. A. (2003). The development of expertise: the journey from acclimation to proficiency. Educational Researcher, 32(8), 10–14.CrossRefGoogle Scholar
  2. Arroyo, I., Cooper, D., Burleson, W., & Woolf, B. P. (2010). Bayesian networks and linear regression models of students’ goals, moods, and emotions. In C. Romero, S. Ventura, M. Pechenzkiy, & R. Baker (Eds.), Handbook of educational data mining (pp. 323–338). Boca Raton: CRC Press.CrossRefGoogle Scholar
  3. Arroyo, I., Burleson, W., Tai, M., Muldner, K., & Woolf, B. P. (2013). Gender differences in the use and benefit of advanced learning tech. for mathematics. Journal of Educational Psychology, 105, 957–969.CrossRefGoogle Scholar
  4. Arroyo, I., Muldner, K., Burleson, W., & Woolf, B. (2014). Adaptive interventions to address students’ negative activating and deactivating emotions during learning activities. In R. Sottilare, A. Graesser, X. Hu, & H. Holden (Eds.), Design recommendations for adaptive intelligent tutoring systems (pp. 79–92). Orlando: U.S. Army Research Lab.Google Scholar
  5. Ayres, J., & Kalyuga, S. (Eds.). (2011). Cognitive load theory. New York: Springer.Google Scholar
  6. Azevedo, R. (2015). Defining and measuring engagement and learning in science: conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50(1), 84–94.MathSciNetCrossRefGoogle Scholar
  7. Azevedo, R., Harley, J., Trevors, G., Feyzi-Behnagh, R., Duffy, M., Bouchet, F., & Landis, R. S. (2013). Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 427–449). Amsterdam: Springer.CrossRefGoogle Scholar
  8. Baker, R., Rodrigo, M., & Xolocotzin, U. (2007). The dynamics of affective transitions in simulation problem-solving environments. In A. R. Paiva, R. Prada, & R. Picard (Eds.), Affective computing and intelligent interaction (Vol. 4738, pp. 666–677). Berlin: Springer.CrossRefGoogle Scholar
  9. Baker, R. S., D’Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010). Better to be frustrated than bored. International Journal of Human-Computer Studies, 68(4), 223–241.CrossRefGoogle Scholar
  10. Bandura, A. (1997). Self-efficacy: toward a unifying theory of behavioral change. Psychological Review, 84, 191–215.CrossRefGoogle Scholar
  11. Bartsch, A., Vorderer, P., Manggold, R., & Viehoff, R. (2008). Appraisal of emotions in media use: toward a process model of meta-emotion and emotion regulation. Media Psychology, 11, 7–27.CrossRefGoogle Scholar
  12. Baylor, A. L., & Kim, S. (2009). Designing nonverbal communication for pedagogical agents: when less is more. Computers in Human Behavior, 25(2), 450–457.CrossRefGoogle Scholar
  13. Boekaerts, M., Pintrich, P., & Zeidner, M. (2000). Handbook of self-regulation. San Diego: Academic.Google Scholar
  14. Bouchet, F., Harley, J. M., & Azevedo, R. (2013a). The impact of different pedagogical agents’ adaptive self-regulated prompting strategies with MetaTutor. In C. H. Lane, K. Yacef, J. Mostow, & P. Pavik (Eds.), Lecture Notes in artificial intelligence (Artificial intelligence in education, Vol. 7926, pp. 815–819). Berlin: Springer.Google Scholar
  15. Bouchet, F., Harley, J. M., Trevors, G., & Azevedo, R. (2013b). Clustering and profiling students according to their interactions with an intelligent tutoring system fostering self-regulated learning. Journal of Educational Data Mining, 5(1), 104–146.Google Scholar
  16. Bouchet, F., Harley, J. M., & Azevedo, R. (2016). Can adaptive pedagogical agents’ prompting strategies improve students’ learning and self-regulation? In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Lecture notes in computer science (Intelligent tutoring systems, Vol. 9684, pp. 368–374). Switzerland: Springer.Google Scholar
  17. Butler, E. A., Egloff, B., Wilhelm, F. W., Smith, N. C., Erickson, E. A., & Gross, J. J. (2003). The social consequences of expressive suppression. Emotion, 3, 48–67.CrossRefGoogle Scholar
  18. Calvo, R. A., & D’Mello, S. D. (2010). Affect detection: An inter. review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1, 18–37.CrossRefGoogle Scholar
  19. Calvo, R. A., & Peters, D. (2015). Positive computing: Technology for wellbeing and human potential. MA: MIT Press.Google Scholar
  20. Calvo, R., D’Mello, S., Gratch, J., & Kappas, A. (2015). The Oxford handbook of affective computing. New York: Oxford University Press.CrossRefGoogle Scholar
  21. Chalfoun, P., & Frasson, C. (2009). Optimal affective conditions for subconscious learning in a 3D intelligent tutoring system. In J. Jacko (Ed.), Human-computer interaction: interacting in various application domains (Vol. 5613, pp. 39–48). Berlin Heidelberg: Springer.CrossRefGoogle Scholar
  22. Chauncey-Strain, A., & D’Mello, S. K. (2015). Affect regulation during learning: the enhancing effect of cognitive reappraisal. Applied Cognitive Psychology, 29, 1–19.CrossRefGoogle Scholar
  23. Cowley, B., & Charles, D. (2016). Behavelets: a method for practical player modeling using psychology-based player traits and domain specific features. User Modeling and User-Adapted Interaction, 26(2), 257–306.CrossRefGoogle Scholar
  24. Craig, S., Graesser, A., Sullins, J., & Gholson, J. (2004). Affect and learning: an exploratory look into the role of affect in learning. Journal of Educational Media, 29, 241–250.CrossRefGoogle Scholar
  25. Csikszentmihalyi, M. (2000). Beyond boredom and anxiety. San Francisco: Jossey-Bass.Google Scholar
  26. D’Mello, S. K., & Graesser, A. C. (2015). Feeling, thinking, and computing with affect-aware learning technologies. In R. A. Calvo, S. K. D’Mello, J. Gratch, & A. Kappas (Eds.), Handbook of affective computing (pp. 419–434). United Kingdom: Oxford University Press.Google Scholar
  27. D’Mello, S., Lehman, B., Sullins, J., Daigle, R., Combs, R., Vogt, K., & Graesser, A. (2010). A time for emoting: When affect-sensitivity is and isn’t effective at promoting deep learning. In V. Aleven, J. Kay, & J. Mostow (Eds.), Lecture notes in computer science (Intelligent tutoring systems, Vol. 6094, pp. 245–254). Berlin: Springer.Google Scholar
  28. D’Mello, S. K., Blanchard, N., Baker, R., Ocumpaugh, J., & Brawner, K. (2014a). I feel your pain: A selective review of affect-sensitive instructional strategies. In R. Sottilare, A. Graesser, X. Hu, & H. Holden (Eds.), Design recommendations for adaptive intelligent tutoring systems (pp. 35–48). Orlando: U.S. Army Research Lab.Google Scholar
  29. D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014b). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170.CrossRefGoogle Scholar
  30. D’Mello, S., Olney, A., Williams, C., & Hays, P. (2012). Gaze tutor: a gaze-reactive ITS. International Journal of Human-Computer Studies, 70, 377–398.CrossRefGoogle Scholar
  31. du Boulay, B. (2011). Towards a motivationally intelligent pedagogy. R. Calvo, & S. D’Mello (Eds.) New Perspectives on Affect and Learning Technologies (pp. 41-52). Springer.Google Scholar
  32. du Boulay, B., Avramides, K., Luckin, R., Martínez-Mirón, E., Méndez, G. R., & Carr, A. (2010). Towards systems that care: a conceptual framework based on motivation, metacognition and affect. International Journal of Artificial Intelligence in Education, 20(3), 197–229.Google Scholar
  33. Duckworth, A. L., Gendler, T. S., & Gross, J. J. (2014). Self-control in school-age children. Educational Psychologist, 49(3), 199–217.CrossRefGoogle Scholar
  34. Dweck, C. S. (2002). Messages that motivate: How praise molds students’ beliefs, motivation, and performance (in surprising ways). In J. M. Aronson (Ed.), Improving academic achievement: Impact of psychological factors on education (pp. 37–60). New York: Academic.CrossRefGoogle Scholar
  35. Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3), 169–200.CrossRefGoogle Scholar
  36. Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: potential of the concept, state of the evidence. Review of Educational Research, 74, 59–109.CrossRefGoogle Scholar
  37. Frenzel, A. C., Thrash, T. M., Pekrun, R., & Goetz, T. (2007). Achievement emotions in Germany and China a cross-cultural validation of the academic emotions questionnaire—mathematics. Journal of Cross-Cultural Psychology, 38(3), 302–309.CrossRefGoogle Scholar
  38. Goetz, T., Bieg, M., Lüdtke, O., Pekrun, R., & Hall, N. C. (2013). Do girls really experience more anxiety in mathematics? Psychological Science, 24(10), 2079–2087.CrossRefGoogle Scholar
  39. Gratch, J., & Marsella, S. (2015). Appraisal models. In R. Calvo, S. D’Mello, J. Gratch, & A. Kappas (Eds.), The Oxford handbook of affective computing (pp. 1–16). New York: Oxford University Press.Google Scholar
  40. Gross, J. J. (1998). Antecedent and response-focused emotion regulation: divergent consequences for experience, expression, and physiology. Journal of Personality and Social Psychology, 74, 224–237.CrossRefGoogle Scholar
  41. Gross, J. J. (2010). The future’s so bright, I gotta wear shades. Emotion Review, 2, 212–216.CrossRefGoogle Scholar
  42. Gross, J. J. (2013). Emotion regulation: taking stock and moving forward. Emotion, 13, 359–365.CrossRefGoogle Scholar
  43. Gross, J. J. (2015). The extended process model of emotion regulation: elaborations, applications, and future directions. Psychological Inquiry, 26(1), 130–137.CrossRefGoogle Scholar
  44. Gross, J. J., & Barret, L. F. (2011). Emotion generation and emotion regulation: one or two depends on your point of view. Emotion Review, 3, 8–16.CrossRefGoogle Scholar
  45. Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. Journal of Personality and Social Psychology, 85, 348–362.CrossRefGoogle Scholar
  46. Gross, J. J., & Levenson, R. W. (1993). Emotional suppression: physiology, self-report, and expressive behavior. Journal of Personality and Social Psychology, 64, 970–986.CrossRefGoogle Scholar
  47. Gross, J. J., & Levenson, R. W. (1997). Hiding feelings: the acute effects of inhibiting negative and positive emotion. Journal of Abnormal Psychology, 106, 95–103.CrossRefGoogle Scholar
  48. Hall, N. C. (2008). Self-regulation of primary and secondary control in achievement settings: a process model. Journal of Social and Clinical Psychology, 27(10), 1126–1164.CrossRefGoogle Scholar
  49. Hall, N. C., Perry, R. P., Chipperfield, J. G., Clifton, R. A., & Haynes, T. L. (2006a). Enhancing primary and secondary control in achievement settings through writing-based attributional retraining. Journal of Social & Clinical Psychology, 25, 361–391.CrossRefGoogle Scholar
  50. Hall, N. C., Chipperfield, J. G., Perry, R. P., Ruthig, J. C., & Goetz, T. (2006b). Primary and secondary control in academic development: gender-specific implications for stress and health in college students. Anxiety, Stress and Coping, 19, 189–210.CrossRefGoogle Scholar
  51. Harley, J. M. (2015). Measuring emotions: A survey of cutting-edge methodologies used in computer-based learning environment research. In S. Tettegah & M. Gartmeier (Eds.), Emotions, technology, design, and learning (pp. 89–114). London: Academic.Google Scholar
  52. Harley, J. M., & Azevedo, R. (2014). Toward a feature-driven understanding of students’ emotions during interactions with agent-based learning environments: a selective review. International Journal of Gaming and Computer-Mediated Simulation, 6(3), 17–34.CrossRefGoogle Scholar
  53. Harley, J. M., Bouchet, F., & Azevedo, R. (2013). Aligning and comparing data on learners’ emotions experienced with MetaTutor. In C. H. Lane, K. Yacef, J. Mostow, & P. Pavik (Eds.), Lecture notes in artificial intelligence (Artificial intelligence in education, Vol. 7926, pp. 61–70). Berlin: Springer.Google Scholar
  54. Harley, J. M., Bouchet, F., Papaionnou, N., Carter, C., Azevedo, R., & Landis, R. (2014). Assessing learning with MetaTutor, a multi-agent hypermedia learning environment. In M.W. Chu, & J. M. Harley (Chairs), Innovative practices for assessment in computer based learning environments. Symposium conducted at the annual meeting of the American Educational Research Association, Philadelphia, PA.Google Scholar
  55. Harley, J. M., Lajoie, S. P., Frasson, C., & Hall, N. C. (2015a). An integrated emotion-aware framework for intelligent tutoring systems. In C. Conati & N. Heffernan (Eds.), Artificial intelligence in education (pp. 620–624). Switzerland: Springer.Google Scholar
  56. Harley, J. M., Rowe, J. P., Lester, J. C., & Frasson, C. (2015b). Designing story-centric games for player emotion: A theoretical perspective. Proceedings of the eighth workshop on Intelligent Narrative Technologies (pp. 34–37). Palo Alto: AAAI Press.Google Scholar
  57. Harley, J. M., Bouchet, F., Hussain, S., Azevedo, R., & Calvo, R. (2015c). A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Computers in Human Behavior, 48, 615–625.CrossRefGoogle Scholar
  58. Harley, J. M., Poitras, E. G., Jarrell, A., Duffy, M. C., & Lajoie, S. P. (2016a). Comparing virtual and location-based augmented reality mobile learning: emotions and learning outcomes. Educational Technology Research and Development, 64(3), 359–388.CrossRefGoogle Scholar
  59. Harley, J. M., Carter, C. K., Papaionnou, N., Bouchet, F., Azevedo, R., Landis, R. L., & Karabachian, L. (2016b). Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: towards emotionally-adaptive agent-based learning environments. User Modeling and User-Adapted Interaction, 26, 177–219.CrossRefGoogle Scholar
  60. Harp, S. F., & Mayer, R. E. (1998). How seductive details do their damage: a theory of cognitive interest in science learning. Journal of Educational Psychology, 90(3), 414–434.CrossRefGoogle Scholar
  61. Heckhausen, H. (1991). Motivation and action. New York: Springer.CrossRefGoogle Scholar
  62. Jaques, N., Conati, C., Harley, J. M., & Azevedo, R. (2014). Predicting affect from gaze behavior data during interactions with an intelligent tutoring system. In S. Trausan-Matu, K. Boyer, M. Crosby, & K. Panourgia (Eds.), Lecture notes in computer science: Vol. 8474. Intelligent tutoring systems (pp. 29–38). Switzerland: Springer.Google Scholar
  63. Jarrell, A., Doleck, T., Poitras, E., Lajoie, S. P., & Tressel, T. (2015a). Learning to diagnose a virtual patient: An investigation of cognitive errors in medical problem solving. In C. Conati & N. Heffernan (Eds.), Lectures notes in artificial intelligence (Artificial intelligence in education, Vol. 9112, pp. 176–184). Switzerland: Springer.Google Scholar
  64. Jarrell, A., Harley, J. M., Lajoie, S. P., & Naismith, L. (2015b). Examining the relationship between performance feedback and emotions in diagnostic reasoning: Toward a predictive framework for emotional support. In C. Conati & N. Heffernan (Eds.), Lectures notes in artificial intelligence (Artificial intelligence in education, Vol. 9112, pp. 657–660). Switzerland: Springer.Google Scholar
  65. Jarrell, A., Harley, J.M., & Lajoie, S.P. (2016). The link between achievement emotions, appraisals and task performance: Pedagogical considerations for emotions in CBLEs. Journal of Computers in Education, 1–19. doi:  10.1007/s40692-016-0064-3.
  66. Lajoie, S. P. (2003). Transitions and trajectories for studies of expertise. Educational Researcher, 32, 21–25.CrossRefGoogle Scholar
  67. Lajoie, S. P., & Lesgold, A. (1992). Dynamic assessments of proficiency for solving procedural knowledge tasks. Educational Psychologist, 27(3), 365–384.CrossRefGoogle Scholar
  68. Lajoie, S. P., Naismith, L., Poitras, E., Hony, Y. J., Cruz-Panesso, I., Ranelluci, J., Mamane, S., & Wiseman, J. (2013). Technology-rich tools to support self-regulated learning and performance in medicine. In R. Azevedo & A. Aleven (Eds.), International handbook of metacognition and learning. New York: Springer.Google Scholar
  69. Leighton, J. P., Chu, M.-W., & Seitz, P. (2012). Cognitive diagnostic assessment and the learning errors and formative feedback (LEAFF) model. In R. Lissitz (Ed.), Informing the practice of teaching using formative and interim assessment: A systems approach (pp. 183–207). Greenwich: Information Age.Google Scholar
  70. Lepper, M. (1988). Motivational considerations in the study of instruction. Cognition and Instruction, 5(4), 289–309.CrossRefGoogle Scholar
  71. Leroy, V., Grégoire, J., Magen, E., Gross, J. J., & Mikolajczak, M. (2012). Resisting the sirens of temptation while studying. Learning & Individual Differences, 22, 263–268.CrossRefGoogle Scholar
  72. Mauss, I. B., & Robinson, M. D. (2009). Measures of emotion: a review. Cognition and Emotion, 23, 209–237.CrossRefGoogle Scholar
  73. Mayer, R. E. (2015). (Ed.). The Cambridge handbook of multimedia learning (2nd Ed.). Cambridge University Press: New York, NY.Google Scholar
  74. McCrae, K., Misra, S., Prasad, A. K., Pereira, S. C., & Gross, J. J. (2012). Bottom-up and top-down emotion generation: implications for emotion regulation. Social Cognitive and Affective Neuroscience, 7(3), 253–262.CrossRefGoogle Scholar
  75. McQuiggan, S. W., & Lester, J. C. (2007). Modeling and evaluating empathy in embodied companion agents. International Journal of Human-Computer Studies, 65(4), 348–360.CrossRefGoogle Scholar
  76. McQuiggan, S. W., & Lester, J. C. (2009). Modeling affect expression and recognition in an interactive learning environment. International Journal of Learning Technology, 4, 216–233.CrossRefGoogle Scholar
  77. McQuiggan, S. W., Robison, J. L., & Lester, J. C. (2010). Affective transitions in narrative-centered learning environments. Educational Technology & Society, 13, 40–53.Google Scholar
  78. Muis, K. R., Psaradellis, C., Lajoie, S. P., Di Leo, I., & Chevrier, M. (2015). The role of epistemic emotions in mathematics problem solving. Contemporary Educational Psychology, 42, 172–185.CrossRefGoogle Scholar
  79. Pekrun, R. (2006). The control-value theory of achievement emotions. Educational Psychology Review, 18(4), 315–341.CrossRefGoogle Scholar
  80. Pekrun, R. (2011). Emotions as drivers of learning and cognitive development. In R. A. Calvo & S. D’Mello (Eds.), New perspectives on affect and learning technologies (pp. 23–39). New York: Springer.CrossRefGoogle Scholar
  81. Pekrun, R., & Linnenbrink-Garcia. (2014a). Introduction to emotions in education. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 1–10). New York: Routledge.Google Scholar
  82. Pekrun, R., & Linnenbrink-Garcia. (2014b). Academic emotions and student engagement. In S. L. Christenson et al. (Eds.), Handbook of research on student engagement (pp. 259–282). New York: Springer.Google Scholar
  83. Pekrun, R., & Perry, R. P. (2014). Control-value theory of achievement emotions. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 120–141). New York: Routledge.Google Scholar
  84. Pekrun, R., Hall, N. C., Goetz, T., & Perry, R. P. (2014). Boredom and academic achievement: testing a model of reciprocal causation. Journal of Educational Psychology, 106(3), 696.CrossRefGoogle Scholar
  85. Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). San Diego: Academic.CrossRefGoogle Scholar
  86. Poitras, E. G., & Lajoie, S. P. (2014). Developing an agent-based adaptive system for scaffolding self-regulated inquiry learning in history education. Educational Technology Research and Development, 62, 335–366.CrossRefGoogle Scholar
  87. Poitras, E. G., Harley, J. M., Compeasu, T., Kee, K., & Lajoie, S.P. (in press). Augmented reality in informal learning settings: Leveraging technology for the love of history. In R. Zheng & G. Michael (Eds.). Handbook of Research on Serious Games for Educational Applications. Google Scholar
  88. Porayska-Pomsta, K., Mavrikis, M., D’Mello, S., Conati, C., & Baker, R. S. (2013). Knowledge elicitation methods for affect modeling in education. International Journal of Artificial Intelligence in Education, 22(3), 107–140.Google Scholar
  89. Quoidbach, J., Mikolajczak, M., & Gross, J. J. (2015). Positive interventions: an emotion regulation perspective. Psychological Bulletin, 141(3), 655.CrossRefGoogle Scholar
  90. Robison, J., McGuiggan, S. W., & Lester, J. (2009). Evaluating the consequences of affective feedback in intelligent tutoring systems. In J. Cohn, A. Nijholt, & M. Pantic (Eds.), Proceedings of the International Conference on Affective Computing & Intelligent Interaction (pp. 37–42). Amsterdam: IEEE Press.Google Scholar
  91. Rosiek, J. (2003). Emotional scaffolding an exploration of the teacher knowledge at the intersection of student emotion and the subject matter. Journal of Teacher Education, 54(5), 399–412.CrossRefGoogle Scholar
  92. Rowe, J. P., & Lester, J. C. (2015). Improving student problem solving in narrative- centered learning environments: A modular reinforcement learning framework. In C. Conati & N. Heffernan (Eds.), Lectures notes in artificial intelligence (Artificial intelligence in education, Vol. 9112, pp. 419–428). Switzerland: Springer.Google Scholar
  93. Rowe, J. P., Shores, L. R., Mott, B. W., & Lester, J. C. (2011). Integrating learning, problem solving, and engagement in narrative-centered learning environments. International Journal of Artificial Intelligence in Education, 21(1), 115–133.Google Scholar
  94. Russell, J. A., Weiss, A., & Mendelsohn, G. A. (1989). Affect grid: a single-item scale of pleasure and arousal. Journal of Personality and Social Psychology, 57(3), 493.CrossRefGoogle Scholar
  95. Sabourin, J. L., & Lester, J. C. (2014). Affect and engagement in game-based learning environments. IEEE Transactions on Affective Computing, 5, 45–55.CrossRefGoogle Scholar
  96. Segedy, J. R., Kinnebrew, J. S., & Biswas, G. (2013). The effect of contextualized conversational feedback in a complex open-ended learning environment. Educational Technology Research and Development, 61(1), 71–89.CrossRefGoogle Scholar
  97. Shute, V. J., & Ke, F. (2012). Games, learning, and assessment. In D. Ifenthaler, D. Eseryel, & X. Ge (Eds.), Assessment in game-based learning: Foundations, innovations, and perspectives (pp. 43–58). New York: Springer.CrossRefGoogle Scholar
  98. Shute, V. J., Ventura, M., & Kim, Y. J. (2013). Assessment and learning of qualitative physics in Newton’s Playground. The Journal of Educational Research, 106, 423–430.CrossRefGoogle Scholar
  99. Shute, V., Leighton, J. P., Jang, E., & Chu, M.-W. (2016). Advances in the science of assessment. Educational Assessment, 21, 34–59.CrossRefGoogle Scholar
  100. Sinatra, G. M., Broughton, S. H., & Lombardi, D. (2014). Emotions in science education. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 415–436). New York: Routledge.Google Scholar
  101. Smaldino, S. E., Lowther, D. L., Russell, J. D., & Mims, C. (2015). Instructional technology and media for learning. Upper Saddle River: Pearson Education.Google Scholar
  102. Sottilare, R., Graesser, A., Hu, X., & Holden, H. (Eds.). (2013). Design recommendations for ITS. Orlando: U.S. Army Research Lab.Google Scholar
  103. Taub, M., Azevedo, R., Bouchet, F., & Khosravifar, B. (2014). Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments? Computers in Human Behavior, 39, 356–367.CrossRefGoogle Scholar
  104. Trevors, G., Duffy, M., & Azevedo, R. (2014). Note-taking within MetaTutor. Educational Technology Research and Development, 62, 507–528.CrossRefGoogle Scholar
  105. Tsai, Y. M., Kunter, M., Lüdtke, O., Trautwein, U., & Ryan, R. M. (2008). What makes lessons interesting? the role of situational and individual factors in three school subjects. Journal of Educational Psychology, 100(2), 460.CrossRefGoogle Scholar
  106. Virk, S., Clark, D., & Sengupta, P. (2015). Digital games as multirepresentational environments for science learning: implications for theory, research, and design. Educational Psychologist, 50(4), 284–312.CrossRefGoogle Scholar
  107. Vygotsky, L. S. (1987). Thinking and speech (N. Minick, Trans.). In R. W. Rieber & A. S. Carton (Eds.), The collected works of L. S. Vygotsky: Vol. 1. Problems of general psychology (pp. 39–285). New York: Plenum Press (Original work published 1934).Google Scholar
  108. Zimmerman, B. J., & Schunk, D. H. (Eds.). (2001). Self-regulated learning and academic achievement: theoretical perspectives. New York: Erlbaum.Google Scholar

Copyright information

© International Artificial Intelligence in Education Society 2016

Authors and Affiliations

  • Jason M. Harley
    • 1
  • Susanne P. Lajoie
    • 2
  • Claude Frasson
    • 3
  • Nathan C. Hall
    • 2
  1. 1.Educational PsychologyUniversity of AlbertaEdmontonCanada
  2. 2.Educational and Counselling PsychologyMcGill UniversityMontréalCanada
  3. 3.Computer Science and Operations ResearchUniversité de MontréalMontréalCanada

Personalised recommendations