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Stimuli-Based Control of Negative Emotions in a Digital Learning Environment

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Advances in Intelligent Systems Research and Innovation

Abstract

The proposed system for coping negative emotions arising during the learning process is considered as an embedded part of the complex intelligent learning system realized in a digital environment. By applying data-driven procedures on the current and retrospective data the main didactic-based stimuli provoking emotion generation are identified. They are examined as dominant negative emotions in the context of learning. Due to the presence of strong internal and output interconnections between teaching and emotional states, an intelligent decoupling multidimensional control scheme is accepted to overcome the lack of sample effective independent control actions that separately affect the states of learning and emotions. To avoid existing drawbacks in emotion-focused partial control, an approach with integrating emotions with a low-dimensional representation is accepted. Two-stage procedure is proposed to compensate the negative impact of emotions on the learning process.

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References

  1. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd ed. Pearson (2009)

    Google Scholar 

  2. Negnevitsky, M.: Artificial Intelligence: A Guide to Intelligent Systems, 3rd edn. Pearson Education, Canada (2014)

    Google Scholar 

  3. Baesens, B.: Analytics in a Big Data World. Wiley, New York (2014)

    Google Scholar 

  4. Berthold, M., Hand, D.: Intelligent Data Analysis, 2nd ed. Springer, Berlin (2007)

    Google Scholar 

  5. Kordon, A.K.: Applying Data Science: How to Create Value with Artificial Intelligence. Springer, Berlin (2020)

    Google Scholar 

  6. McKinsey: Global Institute, Artificial Intelligence: The Next Digital Frontier? (2017)

    Google Scholar 

  7. Pascual, D.: Artificial Intelligence Tools: Decision Support Systems in Conditions Monitoring and Diagnosis. CRC Press, Boca Raton (2015)

    Google Scholar 

  8. Chen, N., Christensen, L., Gallagher, K., Mate, R., Rafert, G.: Global Economic Impacts Associated with Artificial Intelligence. Study Analysis Group, Boston, MA, vol. 25 (2016)

    Google Scholar 

  9. Andreu-Perez, J., Poon, C.C., Merrifield, R.D., Wong, S.T., Yang, G.-Z.: Big Data for Health. IEEE J. Biomed. Health Inform. 19, 1193–1208 (2015)

    Article  Google Scholar 

  10. Kober, J., Bagnell, J.A., Peters, J.: Reinforcement learning in robotics: a survey. Int. J. Robot. Res. 32, 1238–1274 (2013)

    Article  Google Scholar 

  11. Sobh, T., Elleithy, K. (eds.): Emerging Trends in Computing, Informatics, Systems Sciences, and Engineering. Springer, Berlin (2013)

    Google Scholar 

  12. IEEE Computer Society’s Top 12 Technology Trends for 2020, https://www.prnewswire.com/news-releases/300971863.html.

  13. Crawley, E.F., Hosoi, A., Mitra, A.: Redesigning undergraduate engineering education at MIT—the new engineering education transformation (NEET) initiative. American Society for Engineering Education (2018)

    Google Scholar 

  14. Graham, R.: The Global State of the Art in Engineering Education. Survey Report commissioned by MIT, Phase I 2016, Phase II (2018)

    Google Scholar 

  15. Natarajan, R. (ed.): Proceedings of the International Conference on Transformations in Engineering Education (ICTIEE 2014). Springer, Berlin (2014)

    Google Scholar 

  16. Dahdouh, K., Dakkak, A., Oughdir, L., Ibriz, A.: Improving Online Education Using Big Data Technologies (2020). https://doi.org/10.5772/intechopen.88463

  17. Vaessen, M., van der Heijden, K., de Gelder, B.: Decoding of emotion expression in the face, body and voice reveals sensory modality specific representations. bioRxiv preprint (2019)

    Google Scholar 

  18. Kleinjohann, B.: Fuzzy emotion recognition in natural speech. In: IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2005), pp. 317–322 (2005)

    Google Scholar 

  19. Yadegaridehkordi, E., Noor, N.F.B.M., Ayub, M.N.B., Affal, H.B., Hussin, N.B.: Affective computing in education: a systematic review and future research. Comput. Educ. 42, (2019). https://doi.org/10.1016/j.compedu.2019.103649

  20. Troussas, C., Virvou, M.: Affective computing and motivation in educational contexts: data pre-processing and ensemble learning. In: Advances in Social Networking-Based Learning, Intelligent Systems Reference Library, vol. 181. Springer, Berlin (2020)

    Google Scholar 

  21. Hernández, Y., Sucar, L.E., Arroyo-Figueroa, G.: Building an affective model for intelligent tutoring systems with base on teachers’ expertise. In: Gelbukh, A., Morales, E.F. (eds.) MICAI 2008: Advances in Artificial Intelligence MICAI 2008, Lecture Notes in Computer Science, vol. 5317. Springer, Berlin (2008)

    Google Scholar 

  22. Han, J., Zhao, W., Jiang, Q., Oubibi, M., Hu, X.: Intelligent tutoring system trends 2006–2018: a literature review. In: 2019 Eighth International Conference on Educational Innovation through Technology (EITT), Biloxi, MS, USA, pp. 153–159 (2019)

    Google Scholar 

  23. Kulik, J.A., Fletcher, J.D.: Effectiveness of intelligent tutoring systems—a meta-analytic review. Rev. Educ. Res. (2015). https://doi.org/10.0132/0034654315581420

    Article  Google Scholar 

  24. Pratap, M.: How Learning Management System Revolutionizing the Education Sector (2020). https://hackernoon.com/qor32bx

  25. Popenici, S., Kerr, S.: Exploring the impact of artificial intelligence on teaching and learning in higher education. Res. Practice Technol. Enhanced Learn. 12(1), 1–13 (2017)

    Google Scholar 

  26. Saltan, F.: The new generation of interactive whiteboards: how students perceive and conceptualize?. Participatory Educ. Res. 6(2), 93–102 (2019)

    Google Scholar 

  27. van Alten, D.C.D., Phielix, C., Janssen, J., Kester, L.: Effects of flipping the classroom on learning outcomes and satisfaction: a meta-analysis. Educ. Res. Rev. 28, 1–18 (2019)

    Google Scholar 

  28. Alexander, P.A., Pekrun, R., Linnenbrink-Garcia, L. (eds.) International Handbook of Emotions in Education. Routledge, London (2014)

    Google Scholar 

  29. Faria, R., Almeida, A., Martins, C., Gonçalves, R., Figueiredo, L.: Including emotion in learning process. In: Portuguese Conference on Artificial Intelligence, pp. 27–32. Springer, Berlin (2015)

    Google Scholar 

  30. Kuppens, P., Oravecz, Z., Tuerlinckx, F.: feelings change: accounting for individual differences in the temporal dynamics of affect. J. Personal. Social Psychol. 99(6), 1042–1060 (2010)

    Google Scholar 

  31. Hamann, S., Canli, T.: Individual differences in emotion processing. Curr. Opin. Neurobiol. 14, 233–238 (2004)

    Article  Google Scholar 

  32. Vishkin, A., Hasson, Y., Millgram, Y., Tamir, M.: One size does not fit all: tailoring cognitive reappraisal to different emotions. In: Personality and Social Psychology Bulletin, Society for Personality and Social Psychology, pp. 1–16 (2019)

    Google Scholar 

  33. Lewis, M.D.: Bridging emotion theory and neurobiology through dynamic systems modeling. Behav. Brain Sci. 28(2), 169–194 (2005)

    Google Scholar 

  34. D’ Mello, S., Graesser, A.: The half-life of cognitive-affective states during complex learning. Cogn. Emotion (2011). https://doi.org/10.1080/02699931.2011.613668

  35. Ekman, P.: What scientists who study emotion agree about. Persp. Psychol. Sci. 11, 31–34 (2016)

    Article  Google Scholar 

  36. Tiedens, L.Z., Linton, S.: Judgment under Emotional Certainty and Uncertainty: the effects of specific emotions on information processing. J. Pers. Soc. Psychol. 81, 973–988 (2001)

    Article  Google Scholar 

  37. Feidakis, M., Daradoumis, T., Caballe, S.: Emotion measurement in intelligent tutoring systems: what, when and how to measure? In: Third International Conference on Intelligent Networking and Collaborative Systems, pp. 807–813 (2011)

    Google Scholar 

  38. D’Mello, S., Lehman, B., Pekrun, R., Graesser, A.: Confusion can be beneficial for learning. Learn. Instr. 29, 153–170 (2014)

    Article  Google Scholar 

  39. Colombo, D., Fernandez-Alvarez, J., Palacios, A.G., Cipresso, P., Bottela, G., Riva, G.: New technologies for the understanding, assessment and intervention of emotion regulation. Front. Psychol. 10(12) (2019)

    Google Scholar 

  40. Västfjäll, D., Slovic, P., Burns, W.J., Erlandsson, A., Koppel, L., Eand, A.: et al.: The arithmetic of emotion: integration of incidental and integral affect in judgments and decisions. Front. Psychol. 7(325) (2016)

    Google Scholar 

  41. Feidakis, M.: A review of emotion-aware systems for e-learning in virtual environments in formative assessment. In: Learning Data Analytics and Gamification. Elsevier, Amsterdam (2016)

    Google Scholar 

  42. Cowena, A.S., Keltnera, D.: Self-report captures 27 distinct categories of emotion bridged by continuous gradients. PNAS, USA, Published online 5 Sept 2017

    Google Scholar 

  43. Harley, J.M., Pekrun, R., Taxer, J.L., Gross, J.J.: Emotion regulation in achievement situations: an integrated model. Educ. Psychol. 54(2), 106–126 (2019)

    Google Scholar 

  44. Kort, B., Reilly, R.: Analytical models of emotions, learning and relationships: towards an affect-sensitive cognitive machine. In: Proceedings of the International Conference on Virtual Worlds and Simulation (VWSim), San Antonio, Texas (2002)

    Google Scholar 

  45. Ibrahimoglu, N., Unaldi, I., Samancioglu, M., Baglibel, M.: The relationship between personality traits and learning styles: a cluster analysis. Asian J. Manage. Sci. Educ. 93–108 (2013)

    Google Scholar 

  46. Lerner, J.S., Li, Y., Valdesolo, P., Kassam, K.: Emotion and decision making. Ann Rev. Psychol. (2014)

    Google Scholar 

  47. Gross, J.J.: Emotion regulation: current Status and future prospects. Psychol. Inq. 26, 1–26 (2015)

    Article  Google Scholar 

  48. Esau, N., Kleinjohann, L., Kleinjohann, B.: An adaptive fuzzy emotion model for emotion recognition. In: Proceedings of the 4-th European Society for Fuzzy Logic and Technology, Barcelona, pp. 73–78 (2005)

    Google Scholar 

  49. Zhou, R., Feng, J., Chang, H., Zhou, Y.: fuzzification of attribute information granules and its formal reasoning model. CAAI Trans. Intell. Technol. 2(3), 116–125 (2017)

    Google Scholar 

  50. Damasio, A.R., Grabowski, T.J., Bechara, A., Damasio, H., Ponto, L.L., Parvizi, J., et al.: Subcortical and cortical brain activity during the feeling of self-generated emotions. Nat. Neurosci. 3, 1049–1056 (2000)

    Article  Google Scholar 

  51. Ozawa, S., Matsuda, G., Hiraki, K.: Negative emotion modulates prefrontal cortex activity during a working memory task: a NIRS study. Front. Hum. Neurosci. 8(46) (2014)

    Google Scholar 

  52. Kahneman, D., Riis, J.: Living, and thinking about it: two perspectives on life. In: Kahneman, D., Riis, J.: The Science of Well-Being (2005). books.google.com

    Google Scholar 

  53. Shafir, R., Thiruchselvam, R., Suri, G., Gross, J.J., Sheppes, G.: Neural processing of emotional-intensity predicts emotion regulation choice. Social Cogn. Affect. Neurosci. 1863–1871 (2016)

    Google Scholar 

  54. Wortha, F., Azevedo, R., Taub, M., Narciss, S.: Multiple negative emotions during learning with digital learning environments—evidence on their detrimental effect on learning from two methodological approaches. Front. Psychol. 10 (2019). https://doi.org/10.3389/fpsyg.2019.02678

  55. Thagard, P., Nerb, J.: Emotional gestalts: appraisal, change, and the dynamics of affect personality and social psychology review. 6(4), 274–282 (2002)

    Google Scholar 

  56. Gregoire, M.: Is it a challenge or a threat? a dual-process model of teachers’ cognition and appraisal processes during conceptual change. Educ. Psychol. Rev. 15(2) (2003)

    Google Scholar 

  57. Pekrun, R., Erenzel, A., Goetz, T., Perry, R.: The control-value theory of achievement emotions: an integrative approach to emotions in education. In: Schutz, P., Pekrun, R. (eds.) Emotion in Education, pp. 13–36. Academic Press, Amsterdam (2007)

    Chapter  Google Scholar 

  58. Lange, J., Dalege, J., Borsboom, D., van Kleef, G.A., Fischer, A.H.: Toward an integrative psychometric model of emotions. Perspect. Psychol. Sci. 15(2), 444–468

    Google Scholar 

  59. Seta, J.J., Haire, A., Seta, C.E.: Averaging and summation: positivity and choice as a function of the number and affective intensity of life events. J. Exp. Soc. Psychol. 44, 173–186 (2008)

    Article  Google Scholar 

  60. Tsiourti, C., Weiss, A., Wac, K., et al.: Multimodal integration of emotional signals from voice, body, and context: effects of congruence on emotion recognition and attitudes towards robots. Int. J. Soc. Robot. 11, 555–573 (2019)

    Article  Google Scholar 

  61. Byun, S.-W., Lee, S.-P.: Human emotion recognition based on the weighted integration method using image sequences and acoustic features. Multimedia Tools Appl. 9 (2020)

    Google Scholar 

  62. Zhou, R., Feng, J., Chang, H., Zhou, Y.: Fuzzification of attribute information granules and its formal reasoning model. CAAI Trans. Intell. Technol. 2(3), 116–125

    Google Scholar 

  63. Liliana, D.Y., Basaruddin, T., Widyanto, M.R., Oriza, I.I.D.: Fuzzy emotion: a natural approach to automatic facial expression recognition from psychological perspective using fuzzy system (2019)

    Google Scholar 

  64. Ray, T., Kordon, A., Wells, C.: Applied Data Mining in Forecasting. SAS Institute (2012)

    Google Scholar 

  65. Roth, G., Vansteenkiste, R., Ryan, M.: Integrative Emotion Regulation: Process and Development from a Self-determination Theory Perspective, pp. 1–12. Development and Psychopathology, Cambridge University Press (2019)

    Google Scholar 

  66. Roth, G., Shahar, B.H., Zohar-Shefer, Y., Benita, M., Moed, A., Bibi, U., Ryan, R.M.: Benefits of emotional integration and costs of emotional distancing. J. Pers. 86, 919–934 (2018)

    Article  Google Scholar 

  67. Melo, F.R., Flores, E.L., Carvalho, S.D.: Multilevel content's structure for personalization in conexionist intelligent tutor systems. In: 8th International Conference on Information Systems and Technology Mmanagement, 2011, Proceedings of 8th CONTECSI, TECSI, EAC FEA USP (2011)

    Google Scholar 

  68. Reigeluth, C.M., Aslan, S., Chen, Z., et al.: Personalized integrated educational system: technology functions for the learner-centered paradigm of education. J. Educ. Comput. Res. 53(3), 459–496

    Google Scholar 

  69. Zhou, G., Azizsoltani, H., Ausin, M.S., Barnes, T., Chi, M.: Hierarchical reinforcement learning for pedagogical policy induction. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R.R. (eds.) Artificial Intelligence in Education AIED 2019, Lecture Notes in Computer Science, vol. 625. Springer, Berlin (2019)

    Google Scholar 

  70. Frijda, N.H.: Emotions, individual differences and time course: reflections. Cogn. Emotion 23(7), 1444–1461 (2009)

    Google Scholar 

  71. Jevsikova, T., Berniukevicius, E., Kurilovas, E.: Application of resource description framework to personalise learning: systematical review and methodology. Inf. Educ. 16(1), 61–82 (2017)

    Google Scholar 

  72. Connor, C.M.: Using technology and assessment to personalize instruction: preventing reading problems. Prev. Sci. 20(1), 89–99 (2019)

    Google Scholar 

  73. Stewart, C.: Learning analytics: shifting from theory to practice. J. Empower Teach. Excel l(1) (2017)

    Google Scholar 

  74. Kaltenborn, R., Hadjiski, M., Koynov, S.: Intelligent control of negative emotions in a computer-based learning system. In: Proceedings of 2020 IEEE 10th International Conference on Intelligent Systems, Bulgaria, Varna, pp. 119–124, 28–30 Aug 2020

    Google Scholar 

  75. Kaltenborn, R.: Embedding the assessment of emotion in the learning process with ai-driven technologies. In: Petrov, V., Anderson, K. (eds.) Traditional Learning Theories, Process Philosophy and AI, pp. 145–166. Les Editions Chromatica (2019)

    Google Scholar 

  76. Hadjiski, M., Kaltenborn, R.: Learnability as an indicator for planning and control of learning systems. Inform. Technol. Control 4 (2020)

    Google Scholar 

  77. Chang, M., D’Anjello, G., Gaeta, M., Orciuoli, F., Sampson, D., Simonelli, C.: Building ontology-driven tutoring models for intelligent tutoring systems using data mining. IEEE Access 8, 48151–48162 (2020)

    Article  Google Scholar 

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Kaltenborn, R., Hadjiski, M., Koynov, S. (2022). Stimuli-Based Control of Negative Emotions in a Digital Learning Environment. In: Sgurev, V., Jotsov, V., Kacprzyk, J. (eds) Advances in Intelligent Systems Research and Innovation. Studies in Systems, Decision and Control, vol 379. Springer, Cham. https://doi.org/10.1007/978-3-030-78124-8_18

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