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Emotions and Personality in Adaptive e-Learning Systems: An Affective Computing Perspective

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Emotions and Personality in Personalized Services

Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

This chapter reports how affective computing (in terms of detection methods and intervention approaches) is considered in adaptive e-learning systems. The goal behind is to enrich the personalized support provided in online educational settings by taking into account the influence that emotions and personality have in the learning process. The main contents of the chapter consist in the review of 26 works that present current research trends regarding the detection of the learners’ affective states and the delivery of the appropriate affective support in diverse educational settings. In addition, the chapter discusses open issues regarding affective computing in the educational domain.

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Notes

  1. 1.

    http://www4.ncsu.edu/unity/lockers/users/f/felder/public/ILSpage.html.

  2. 2.

    APML: http://apml.areyoupayingattention.com/.

  3. 3.

    CAM: https://sites.google.com/site/camschema/home.

  4. 4.

    IMS: http://www.imsglobal.org/.

References

  1. Afzal, S., Robinson, P.: Modelling affect in learning environments—motivation and methods. In: IEEE 10th International Conference on Advanced Learning Technologies (ICALT), 2010, pp. 438, 442, 5–7 July (2010). doi:10.1109/ICALT.2010.127

  2. Allport, G.W.: Personality. Holt, New York (1937)

    Google Scholar 

  3. Baimbetov, Y., Khalil, I., Steinbauer, M., Anderst-Kotsis, G.: Using Big data for emotionally intelligent mobile services through multi-modal emotion recognition. Inclusive smart cities and e-health. In: Lecture Notes in Computer Science, vol. 9102, pp. 127–138 (2015)

    Google Scholar 

  4. Barsade, S.: The ripple effect: emotional contagion and its influence on group behavior. Adm. Sci. Q. 47, 644–675 (2002)

    Article  Google Scholar 

  5. Blanchard, E.G., Volfson, B., Hong, Y.J. Lajoie, S.P.: Affective artificial intelligence in education: from detection to adaptation. In: Proceedings of the 2009 conference on artificial intelligence in education: building learning systems that care: from knowledge representation to affective modelling (AIED 2009), pp. 81–88 (2009)

    Google Scholar 

  6. Bloom, B.S.: Taxonomy of educational objectives. In: Handbook 1: Cognitive domain. New York, NY: David McKay (1956)

    Google Scholar 

  7. Calvo, R., D’Mello, S.K., Gratch, J., Kappas, A.: The Oxford Handbook of Affective Computing. Oxford University Press, New York, NY (2014)

    Google Scholar 

  8. Calvo, R.A.: Incorporating affect into educational design patterns and frameworks. In: Proceedings—2009 9th IEEE international conference on advanced learning technologies, ICALT 2009, pp. 377–381 (2009)

    Google Scholar 

  9. Calvo, R.A., D’Mello, S.K.: Affect detection: an interdisciplinary review of models, methods, and their applications. T. Affect. Comput. 1(1), 18–37 (2010)

    Article  Google Scholar 

  10. Conati C., Maclaren H.: Modeling user affect from causes and effects. In: Proceedings of UMAP 2009, First and Seventeenth International Conference on User Modeling, Adaptation and Personalization. Springer (2009)

    Google Scholar 

  11. Conati, C., Zhou, X.: Modelling students’ emotions from cognitive appraisal in educational games. Intell. Tutor. Syst. (2002)

    Google Scholar 

  12. Conati, C., Marsella, S., Paiva, A.: Affective interactions: the computer in the affective loop. In: Riedl, J., Jameson, A. (eds.) Proceedings of the 10th International Conference on Intelligent User Interfaces, ACM, New York, NY, 7 (2005)

    Google Scholar 

  13. D’Mello, S.: A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. J. Educ. Psychol. 105, 1082–1099 (2013)

    Article  Google Scholar 

  14. D’Mello, S.K.: Emotional rollercoasters: day differences in affect incidence during learning. In: The Twenty-Seventh International Flairs Conference (2014)

    Google Scholar 

  15. D’Mello, S., Kory, J.: A review and meta-analysis of multimodal affect detection systems. ACM Comput. Surv. 47(3) (Article 43, Publication date: February 2015) (2015)

    Google Scholar 

  16. D’Mello, S., Graesser, A.: AutoTutor and affective autotutor: learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Trans. Interact. Intell. Syst. 2, 4, Article 23, 39 p (2012)

    Google Scholar 

  17. Dahlbäck, N., Jönsson, A., Ahrenberg, L.: Wizard of Oz studies: why and how. Knowl.-Based Syst. 6(4), 258–266 (1993)

    Article  Google Scholar 

  18. Daniel, B.K., Butson, R.J.: Foundations of big data and analytics in higher education. In: International Conference on Analytics Driven Solutions, IBM Centre for Business Analytics and Performance, University of Ottawa, Ottawa, Canada, September 29–30 (2014)

    Google Scholar 

  19. Dennis, M., Masthoff, J., Mellish, C.: Adapting progress feedback and emotional support to learner personality. Int. J. Artif. Intell. Educ. 26(2) (2016). http://link.springer.com/article/10.1007%2Fs40593-015-0059-7

    Google Scholar 

  20. Dugan, J.E.: Second language acquisition and schizophrenia. Second Lang. Res. 30(3), 307–321 (2014)

    Google Scholar 

  21. Ekman, P.: An argument for basic emotions. Cogn. Emot. 6(3–4), 169–200 (1992)

    Article  Google Scholar 

  22. El Kaliouby, R., Picard, R., Baron-Cohen, S.: Affective computing and autism. Ann. New York Acad. Sci. 1093, 228–248 (2006)

    Article  Google Scholar 

  23. El-Bishouty, M.M., Chang, T.W., Graf, S., Kinshuk, and Chen, N.S.: Smart e-course recommender based on learning styles. J. Comput. Educ. 1(1), 99–111 (2014)

    Google Scholar 

  24. Felipe, D.A.M., Gutierrez, K.I.N., Quiros, E.C.M., Vea, L.A.: Towards the development of intelligent agent for novice C/C++ programmers through affective analysis of event logs. Proc. Int. MultiConference Eng. Comput. Sci. 1 (2012)

    Google Scholar 

  25. Fleeson, W.: Toward a structure-and process-integrated view of personality: traits as density distributions of states. J. Pers. Soc. Psychol. 80(6), 1011 (2001)

    Article  Google Scholar 

  26. Fleury, A., Sugar, M., Chau, T.: E-textiles in clinical rehabilitation: a scoping review. Electronics 4, 173–203 (2015)

    Article  Google Scholar 

  27. Forbes-Riley, K., Litman, D.: Adapting to multiple affective states in spoken dialogue. In: Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), Seoul, South Korea, pp. 217–226 July (2012)

    Google Scholar 

  28. Gosling, S.D., Augustine, A.A., Vazire, S., Holtzman, N., Gaddis, S.: Manifestations of personality in online social networks: self-reported facebook-related behaviors and observable profile information. Cyberpsychol. Behav. Soc. Netw. 14, 483–488 (2011). doi:10.1089/cyber.2010.0087

    Article  Google Scholar 

  29. Gosling, S.D., Mehl, M.R., Pennebaker, J.W.: Personality in its natural habitat: manifestations and implicit folk theories of personality in daily life. J. Pers. Soc. Psychol. 90(5), 862–877 (2006)

    Article  Google Scholar 

  30. Grawemeyer, B., Mavrikis, M., Holmes, W., Hansen, A., Loibl, K., Gutiérrez-Santos, S.: The impact of feedback on students’ affective states. International Workshop on Affect, Meta-Affect, Data and Learning. Madrid, Spain, AMADL (2015)

    Google Scholar 

  31. Gutica M., Conati C.: Student emotions with an edu game: a detailed analysis. In: Proceedings of ACII 2013, 5th International Conference on Affective Computing and Intelligent Interaction, IEEE, pp. 534–539 (2013)

    Google Scholar 

  32. Harley, J.M., Carter, K. C., Papaioannou, N., Bouchet, F., Landis, R.S., Azevedo, R., Karabachian, L.: Examining the predictive relationship between personality and emotion traits and learners’ agent-direct emotions. AIED 2015, LNAI 9112, pp. 145–154 (2015)

    Google Scholar 

  33. Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A., Khan, S.U.: The rise of big data on cloud computing: review and open research issues. Inform. Syst. 47, pp 98–115 (2015)

    Google Scholar 

  34. Janning, R., Schatten, C., Schmidt-Thieme, L.: Feature analysis for affect recognition supporting task sequencing in adaptive intelligent tutoring systems. Open learning and teaching in educational communities. Lecture Notes in Computer Science 8719, 179–192 (2014)

    Google Scholar 

  35. Jaques, N., Conati, C., Harley, J. and Azevedo, R.: Predicting affect from gaze data during interaction with an intelligent tutoring system. In: Proceedings of ITS 2014, 12th International Conference on Intelligent Tutoring Systems, pp. 29–28 (2014)

    Google Scholar 

  36. Järvenoja, H., Järvelä, S.: Emotion control in collaborative learning situations: do students regulate emotions evoked by social challenges? Brit. J. Educ. Psychol. 79(3), 463–481 (2009)

    Article  Google Scholar 

  37. Jones, A., Issroff, K.: Learning technologies: affective and social issues in computer-supported collaborative learning. Comput. Educ. 44(4), 395–408 (2005)

    Article  Google Scholar 

  38. Jraidi, I., Chaouachi, M., Frasson, C.: A hierarchical probabilistic framework for recognizing learners’ interaction experience trends and emotions. Adv. Human-Comput. Interact. 2014(632630), 16 p (2014). doi:10.1155/2014/632630

    Google Scholar 

  39. Kai, S., Paquette, L., Baker, R.S., Bosch, N., D’Mello, S., Ocumpaugh, J., Shute, V., Ventura, M.: A comparison of face-based and interaction-based affect detectors in physics playground. In: Proceedings of the 8th International Conference on Educational Data Mining, pp. 77–84 (2015)

    Google Scholar 

  40. Khan, F.A., Graf, S., Weippl, E.R., Iqbal, T., Tjoa, A.M.: Role of learning styles and affective states in web-based adaptive learning environments. In: Proceedings of the World Conference on Educational Multimedia, Hypermedia and Telecommunications (ED-Media 2010), June 2010, AACE Press, Toronto, Canada, pp. 3896–3905 (2010)

    Google Scholar 

  41. Khan, I.A., Brinkman, W.-P., Fine, N., Hierons, R.M.: Measuring personality from keyboard and mouse use. In: Abascal, J., Fajardo, I., Oakley, I. (eds.) Proceedings of the 15th European Conference on Cognitive ergonomics: The Ergonomics of Cool Interaction (ECCE ’08), ACM, New York, NY, USA, Article 38, 8 p (2008)

    Google Scholar 

  42. Khan, I.A., Brinkman, W.-P., Hierons, R.: Towards estimating computer users’ mood from interaction behaviour with keyboard and mouse. Front. Comput. Sci. 1–12 (2013)

    Google Scholar 

  43. Kim, J., Lee, A., Ryu, H.: Personality and its effects on learning performance: design guidelines for an adaptive e-learning system based on a user model. Int. J. Indus. Ergon. 43, 450–461 (2013)

    Google Scholar 

  44. Kolakowska, A.: A review of emotion recognition methods based on keystroke dynamics and mouse movements. In: 2013 The 6th International Conference on Human System Interaction (HSI), pp. 548–555 (2013)

    Google Scholar 

  45. Kort, B., Reilly, R., Picard, R.W.: An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion. In: Proceedings of the IEEE International Conference on Advanced Learning Technologies, Los Alamitos: CA: IEEE Computer Society Press, pp. 43–46 (2001)

    Google Scholar 

  46. Leontidis, M., Halatsis, C.: Integrating Learning styles and personality traits into an affective model to support learner’s learning. Advances in web based learning—ICWL 2009. Lecture Notes in Computer Science 5686, 225–234 (2009)

    Google Scholar 

  47. Leontidis, M., Halatsis, C., Grigoriadou, M.: Using an affective multimedia learning framework for distance learning to motivate the learner effectively. IJLT 6(3), 223–250 (2011)

    Article  Google Scholar 

  48. Litman, D., Forbes-Riley, K.: Evaluating a spoken dialogue system that detects and adapts to user affective states. In: Proceedings 15th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL), Philadelphia, PA, June (2014)

    Google Scholar 

  49. Liu, C., Conn, K., Sarkar, N., Stone, W.: Physiology-based affect recognition for computer-assisted intervention of children with autism spectrum disorder. Int. J. Human-Comput. Stud. 66, 662–677 (2008)

    Article  Google Scholar 

  50. Lopatovska, I.: Researching emotion: challenges and solutions. In Proceedings of the 2011 iConference (iConference’11). ACM, New York, NY, USA, 225–229 (2011). doi:10.1145/1940761.1940792

  51. Mairesse, F., Walker, M.A., Mehl, M.R., Moore, R.K.: Using linguistic cues for the automatic recognition of personality in conversation and text. J. Artif. Intell. Res. 30, 457–500 (2007)

    MATH  Google Scholar 

  52. Matthews, G., Campbell, S.E.: Sustained performance under overload: personality and individual differences in stress and coping. Theor. Issues Ergon. Sci. 10(5), 417–442 (2009)

    Article  Google Scholar 

  53. Mauss, I.B., Robinson, M.D.: Measures of emotion: a review. Cogn. Emot. 23(2), 209–237 (2009)

    Article  Google Scholar 

  54. Mavrikis, M., D’Mello, S.K., Porayska-Pomsta, K., Cocea, M., Graesser, A.: Modeling affect by mining students’ interactions within learning environments. Handb. Educ. Data Mining, pp. 231–244 (2010)

    Google Scholar 

  55. Messinger, D.S., Lobo Duvivier, L., Warren, Z.E., Mahoor, M., Baker, J., Warlaumont, A.S., Ruvolo, P.: Affective computing, emotional development, and autism. In: The Oxford Handbook of Affective Computing, pp. 516–536 (2014)

    Google Scholar 

  56. Mukhopadhyay, S.C.: Wearable sensors for human activity monitoring: a review. IEEE Sens. J. 15, 1321–1330 (2015)

    Article  Google Scholar 

  57. Murray, B., Silver-Pacuila, H., Helsel, F.I.: Improving basic mathematics instruction: promising technology resources for students with special needs. Technol. Action 2(5), 1–6 (2007)

    Google Scholar 

  58. Nunes, M.A.S.N., Bezerra, J.S., Oliveira, A.A.: PersonalityML: a markup language to standardize the user personality in recommender systems. Revista GEINTEC- Gestão, Inovação e Tecnologias 2, 255–273 (2012)

    Article  Google Scholar 

  59. Ocumpaugh, J., Baker, R.S., Rodrigo, M.M.T.: Baker rodrigo ocumpaugh monitoring protocol (BROMP) 2.0 technical and training manual. Technical Report (2015)

    Google Scholar 

  60. Ortigosa, A., Carro, R.M., Quiroga, J.I.: Predicting user personality by mining social interactions in Facebook. J. Comput. Syst. Sci. 80(1), 57–71 (2014)

    Article  MathSciNet  Google Scholar 

  61. Ortony, A., Clore, G.L., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press, Cambridge (1988)

    Google Scholar 

  62. Paquette, L., Jonathan Rowe, J., Ryan Baker, R., Bradford Mott, B., James Lester, J., Jeanine Defalco, J., Keith Brawner, K., Robert Sottilare, R., Vasiliki Georgoulas, V.: Sensor-free or sensor-full: a comparison of data modalities in multi-channel affect detection. In: Proceedings of the Eighth International Conference on Educational Data Mining, pp. 93–100, Madrid, Spain (2015)

    Google Scholar 

  63. Pekrun, R., Elliot, A.J., Maier, M.A.: Achievement goals and achievement emotions: testing a model of their joint relations with academic performance. J. Educ. Psychol. 101, 115–135 (2009)

    Article  Google Scholar 

  64. Picard, R.W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Resnick, M., Roy, D., Strohecker, C.: Affective learning – a manifesto. BT Technol. J. 22(4), 253–269 (2004)

    Article  Google Scholar 

  65. Porayska-Pomsta, K., Mavrikis, M.: D’Mello, S.k., Conati, C., Baker, R. Knowledge elicitation methods for affect modelling in education. J. Artif. Intell. Educ 22(3), 107–140 (2013)

    Google Scholar 

  66. Porayska-Pomsta, K., Mavrikis, M., Pain, H.: Diagnosing and acting on student affect: the tutor’s perspective. User Model. User-Adapt. Interact. 18(1–2), 125–173 (2008)

    Article  Google Scholar 

  67. Robison, J.L., McQuiggan, S.W., Lester, J.C.: Developing empirically based student personality profiles for affective feedback models. Intell. Tutor. Syst. 285–295, 2010 (2010)

    Google Scholar 

  68. Rodriguez, P., Ortigosa, A., Carro, R.M.: Detecting and making use of emotions to enhance student motivation in e-learning environments. Int. J. Continuing Eng. Educ. Life Long Learn. 24(2), 168–183 (2014)

    Article  Google Scholar 

  69. Rusting, C.L., Larsen, R.J.: Extraversion, neuroticism, and susceptibility to positive and negative affect: a test of two theoretical models. Pers. Indiv. Differ. 22(5), 607–612 (1997)

    Article  Google Scholar 

  70. Sabourin, J.L., Lester, J.C.: Affect and engagement in game-based learning environments. IEEE Trans. Affect. Comput. 5(1), 45–56 (2014)

    Article  Google Scholar 

  71. Salmeron-Majadas, S., Arevalillo-Herráez, M., Santos, O.C., Saneiro, M., Cabestrero, R., Quirós, P., Arnau, D., Boticario, J.G.: Filtering of spontaneous and low intensity emotions in educational contexts. AIED 2015. LNCS 9112, pp. 429–438 (2015)

    Google Scholar 

  72. Saneiro, M., Santos, O.C., Salmeron-Majadas, S., Boticario, J.G.: Towards emotion detection in educational scenarios from facial expressions and body movements through multimodal approaches. Sci. World J. 2014, Article ID 484873, 14 p (2014). doi:10.1155/2014/484873

    Google Scholar 

  73. Santos, O.C.: Training the body: The potential of AIED to support personalized motor skills learning. Special Issue “The next 25 Years: How advanced, interactive educational technologies will change the world”. Int. J. Artif. Intell. Educ. Springer. June 2016, 26(2), 730–755 (2016a). doi:10.1007/s40593-016-0103-2

    Google Scholar 

  74. Santos, O.C.: Beyond cognitive and affective issues. Tangible recommendations for psychomotor personalized learning. In: Spector, J.M., Lockee, B.B., Childress, M.D. (eds.) Learning, Design, and Technology. An International Compendium of Theory, Research, Practice, and Policy. Springer, (2016b, in press). doi:10.1007/978-3-319-17727-4_8-1

    Google Scholar 

  75. Santos, O.C., Boticario, J.G.: Requirements for Semantic educational recommender systems in formal e-learning scenarios. Algorithms 4(2), 131–154 (2011)

    Article  Google Scholar 

  76. Santos, O.C., Boticario, J.G.: Involving users to improve the collaborative logical framework. Sci. World J. 2014, Article ID 893525, 15 p (2014). doi:10.1155/2014/893525

    Google Scholar 

  77. Santos, O.C., Saneiro, M., Boticario, J., Rodriguez-Sanchez, C.: Toward interactive context-aware affective educational recommendations in computer assisted language learning. New Rev. Hypermedia Multimedia 22(1–2), 27–57 (2016). http://www.tandfonline.com/toc/tham20/current

    Google Scholar 

  78. Santos, O.C., Boticario, J.G.: Practical guidelines for designing and evaluating educationally oriented recommendations. Comput. Educ. 81, 354–374 (2015). doi:10.1016/j.compedu.2014.10.008

    Article  Google Scholar 

  79. Santos, O.C., Rodriguez-Ascaso, A., Boticario, J.G., Salmeron-Majadas, S., Quirós, P., Cabestrero, R.: Challenges for inclusive affective detection in educational scenarios. In: Universal Access in Human-Computer Interaction. Design Methods, Tools, and Interaction Techniques for eInclusion. Lecture Notes in Computer Science 8009, pp. 566–575 (2013)

    Google Scholar 

  80. Santos, O.C., Salmeron-Majadas, S., Boticario, J.G.: Emotions detection from math exercises by combining several data sources. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) Artificial Intelligence in Education, pp. 742–745. Springer, Berlin Heidelberg (2013)

    Google Scholar 

  81. Santos, O.C., Saneiro, M., Salmeron-Majadas, S., Boticario, J.G.: A methodological approach to eliciting affective educational recommendations. In: 2014 IEEE 14th International Conference on Advanced Learning Technologies (ICALT), pp. 529–533 (2014)

    Google Scholar 

  82. Santos, O.C., Uria-Rivas, R., Rodriguez-Sanchez, M.C., Boticario, J.G.: An open sensing and acting platform for context-aware affective support in ambient intelligent educational settings. IEEE Sens. J. 16(10), 3865–3874 May 15 (2016)

    Google Scholar 

  83. Scherer, K.R.: What are emotions? and how can they be measured? Soc. Sci. Inform. 44(4), 695–729 (2005)

    Article  Google Scholar 

  84. Schröeder, M., Baggia, P., Burkhardt, F., Pelachaud, C., Peter, C., Zovato, E.: Emotion markup language (EmotionML) 1.0. W3C Candidate Recommendation 10, 2012 May (2012)

    Google Scholar 

  85. Schwarcer, R.: Measurement of perceived self-efficacy. Psychometric scales for crosscultural research. Freie Universit, Berlin (1993)

    Google Scholar 

  86. Shen, L., Wang, M., Shen, R.: Affective e-learning: using emotional data to improve learning in pervasive learning environment. Educ. Technol. Soc. (ETS) 12(2), 176–189 (2009)

    Google Scholar 

  87. Soderstrom, N.C., Bjork, R.A.: Learning versus performance: An integrative review. Perspect. Psychol. Sci. 10(2), 176–199 (2015)

    Google Scholar 

  88. Soldz, S., Vaillant, G.: The big five personality traits and the life course: a 45 years longitudinal study. J. Res. Pers. 33, 208–232 (1998)

    Article  Google Scholar 

  89. Solimeno, A., Mebane, M.E., Tomai, M., Francescato, D.: The influence of students and teachers characteristics on the efficacy of face-to-face and computer supported collaborative learning. Comput. Educ. 51(1), 109–128 (2008)

    Article  Google Scholar 

  90. Vandewaetere, M., Desmet, P., Clarebout, G. The contribution of learner characteristics in the development of computer-based adaptive learning environments. Comput. Human Behav. 27(1), January 2011, pp. 118–130, ISSN 0747-5632 (2011). http://dx.doi.org/10.1016/j.chb.2010.07.038

    Google Scholar 

  91. VanLehn, K., Burleson, W., Girard, S., Chavez-Echeagaray, M.E., Gonzalez-Sanchez, J., Hidalgo-Pontet, Y., Zhang, L.: The affective meta-tutoring project: lessons learned. intelligent tutoring systems. Lecture Notes in Computer Science 8474, 84–93 (2014)

    Google Scholar 

  92. Verbert, K., Manouselis, N., Xavier, O., Wolpers, M., Drachsler, H., Bosnic, I., Duval, E.: Context-aware recommender systems for learning: a survey and future challenges. IEEE Trans. Learn. Technol. 5(4), 318–335 (2012)

    Article  Google Scholar 

  93. Vinciarelli, A., Mohammadi, G.: A survey of personality computing. IEEE Trans. Affective Comput. (2014)

    Google Scholar 

  94. Warren, F.: Treatment of personality disorders. In: Corr, P., Matthews, G. (eds.) The Cambridge Handbook of Personality Psychology. Cambridge University Press, Cambridge, U.K., pp. 799–819 (2009)

    Google Scholar 

  95. Wixon, M., Arroyo, I., Muldner, K., Burleson, W., Rai, D.: The opportunities and limitations of scaling up sensor-free affect detection—educational data mining (2014)

    Google Scholar 

  96. Woolf, B., Arroyo, I., Cooper, D., Burleson, W., Muldner, K.: Affective tutors: automatic detection of and response to student emotion. Adv. Intell. Tutor. Syst. Stud. Comput. Intell. 308, 207–227 (2010)

    Google Scholar 

  97. Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., Picard, R.: Affect-aware tutors: recognising and responding to student affect. Int. J. Learn. Technol. 4(3/4), 129–163, Inderscience Enterprises Ltd., (2009)

    Google Scholar 

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Acknowledgments

The research carried out to produce this chapter is partially supported by the Spanish Ministry of Economy and Competence under grants numbers TIN2011-29221-C03-01 (MAMIPEC project: Multimodal approaches for Affective Modelling in Inclusive Personalized Educational scenarios in intelligent Contexts) and TIN2014-59641-C2-2-P (BIG-AFF: Fusing multimodal Big Data to provide low-intrusive AFFective and cognitive support in learning contexts).

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  1. aDeNu Research Group, Artificial Intelligence Department,Computer Science School, UNED, C/Juan del Rosal, 16, 28040, Madrid, Spain

    Olga C. Santos

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  1. Olga C. Santos
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Editors and Affiliations

  1. Department of Computational Perception, Johannes Kepler University, Linz, Austria

    Marko TkalÄŤiÄŤ

  2. Department of Computer Science, University of Bari Aldo Moro, Bari, Italy

    Berardina De Carolis

  3. Department of Computer Science, University of Bari Aldo Moro, Bari, Italy

    Marco de Gemmis

  4. Preserje, Ljubljana, Slovenia

    Ante Odić

  5. Faculty of electrical engineering, University of Ljubljana, Ljubljana, Slovenia

    Andrej Košir

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Santos, O.C. (2016). Emotions and Personality in Adaptive e-Learning Systems: An Affective Computing Perspective. In: Tkalčič, M., De Carolis, B., de Gemmis, M., Odić, A., Košir, A. (eds) Emotions and Personality in Personalized Services. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-31413-6_13

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