Affective Learning: Principles, Technologies, Practice

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10512)


Although the issues around emotions and learning are not new, the term affective learning has only recently been defined as the learning that relates to the learner’s interests, attitudes, and motivations. In the digital age we live though, affective learning is destined to be technology driven or at least enhanced. Having overemphasised the cognitive and relatively neglecting the affective dimension in the past, technology enhanced learning is now enforced by new neuroscience findings that confirmed that affect is complexly intertwined with thinking, and performing important functions that may guide rational behaviour, assist memory retrieval, support decision-making and enhance creativity. To cope with personalised learning experiences in such models of learners though, intelligent tutoring systems must now contain “emotion, affect and context”, in analogy to successful human tutors. However, measuring and modelling learners’ emotional and affective states remains a difficult task, especially when real-time interactions are envisaged. In this paper, the concept of affective learning is furnished with case studies where the roles of technologies, neuroscience, learning and education are interwoven. Medical education is borrowed as a domain of reference. Neuroscientific emphasis is placed in the synergy of two perspectives, namely, the detection and recording of emotions from humans and ways to facilitate their elicitation and their subsequent exploitation in the decision-making process. The paper concludes with a visionary use case towards affective facilitation of training against medical errors and decision making by intelligent, self-regulated systems that could exploit scenario based learning to augment medical minds for tomorrow’s doctors.


Emotion Learning Affective computing Affective learning Technology enhanced learning Self-regulation Scenario based learning Decision making Affective neuroscience Brain function Skill enhancement MOOC Bullying 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Mosby’s Medical Dictionary, 9th edn. Elsevier (2009)Google Scholar
  2. 2.
    Gano-Phillips, S.: Affective learning in general education. Special Topic: Assessment in University General Education Program 6(1), 1–44 (2009)Google Scholar
  3. 3.
    Bloom, B.S., Engelhart, M.D., Furst, E.J., Hill, W.H., Krathwohl, D.R.: Taxonomy of educational objectives, handbook I: The cognitive domain, vol. 19, p. 56. David McKay Co Inc., New York (1956)Google Scholar
  4. 4.
    Bloom, B.S.: Taxonomy of educational objectives, vol. 1: Cognitive domain, pp. 20–24. McKay, New York (1956)Google Scholar
  5. 5.
    Krathwohl, D.R., Bloom, B.S., Masia, B.B.: Handbook II: affective domain. David McKay, New York (1964)Google Scholar
  6. 6.
    Picard, R.W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Resnick, M., Roy, D., Strohecker, V.: Affective learning—a manifesto. BT Technology Journal 22(4), 253–269 (2004)CrossRefGoogle Scholar
  7. 7.
    Picard, R.W.: Affective Computing. The MIT Press, Cambridge (1997)CrossRefGoogle Scholar
  8. 8.
    Luneski, A., Konstantinidis, E., Bamidis, P.D.: Affective medicine: A review of affective computing efforts in medical informatics. Methods of Information in Medicine 49(3), 207–218 (2010)CrossRefGoogle Scholar
  9. 9.
    Cytowic, R.E.: Synesthesia: A union of the senses. MIT press (2002)Google Scholar
  10. 10.
    Goleman, D.: Emotional intelligence. Bantam Books (2006)Google Scholar
  11. 11.
    Paiva, A., Dias, J., Sobral, D., Aylett, R., Woods, S., Hall, L., Zoll, C.: Learning by feeling: evoking empathy with synthetic characters. Applied Artificial Intelligence 19(3–4), 235–266 (2005). doi: 10.1080/08839510590910165 CrossRefGoogle Scholar
  12. 12.
    Persico, D., Steffens, K.: Self-regulated learning in technology enhanced learning environments. In: Technology Enhanced Learning, pp. 115–126. Springer International Publishing (2017)Google Scholar
  13. 13.
    Zeidner, M., Boekaerts, M., Pintrich, P.: Self-regulation. directions and challenges for future research. In: Boekaerts, M., Pintrich, P., Zeidner, M. (eds.) Handbook of self-regulation, pp. 749–768. Academic Press, New York (2000)Google Scholar
  14. 14.
    Herder, E., Sosnovsky, S., Dimitrova, V.: Adaptive intelligent learning environments. In: Technology Enhanced Learning, pp. 109–114. Springer International Publishing (2017)Google Scholar
  15. 15.
    MacLean, P.D.: The brain in relation to empathy and medical education. The journal of nervous and Mental Disease 144(5), 374–382 (1967)CrossRefGoogle Scholar
  16. 16.
    Levine, H.G., McGuire, C.H.: The use of role-playing to evaluate affective skills in medicine. Academic Medicine 45(9), 700–5 (1970)Google Scholar
  17. 17.
    Poulton, T., Balasubramaniam, C.: Virtual patients: a year of change. Medical Teacher 33(11), 933–937 (2011)CrossRefGoogle Scholar
  18. 18.
    Antoniou, P.E., Dafli, E., Arfaras, G., Bamidis, P. D.: Versatile mixed reality educational spaces-a medical education implementation case. In: International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security (IUCC-CSS), pp. 132–137. IEEE (2016)Google Scholar
  19. 19.
    Cooper, B., Brna, P., Martins, A.: Effective affective in intelligent systems – building on evidence of empathy in teaching and learning. In: Paiva, A. (ed.) IWAI 1999. LNCS, vol. 1814, pp. 21–34. Springer, Heidelberg (2000). doi: 10.1007/10720296_3 CrossRefGoogle Scholar
  20. 20.
    Petrantonakis, P.C., Hadjileontiadis, L.J.: Emotion recognition from brain signals using hybrid adaptive filtering and higher-order crossings analysis. IEEE Transactions on Affective Computing 1(2), 81–97 (2010)CrossRefGoogle Scholar
  21. 21.
    Boulding, K.E.: General systems theory-the skeleton of science. Management Science 2(3), 197–208 (1956)CrossRefGoogle Scholar
  22. 22.
    Hastings, J.: The MFOEM Emotion Ontology (2017).
  23. 23.
    Activity Model ONtology (AMOn) (2017).
  24. 24.
    Thakker, D., Dimitrova, V., Ediboglu, G.: Introducing cultural prompts in a semantic data browser. In: International Workshop on Intelligent Exploration of Semantic Data (IESD 2012) in Conjuction with EKAW, Galway (2012)Google Scholar
  25. 25.
    Arnold K.E., Pistilli M.D.: Course signals at purdue: using learning analytics to increase student success. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 267–270. ACM (2012)Google Scholar
  26. 26.
    Poulton, T.: Hot topics in elearning & distance learning. In: Proceedings of MEI2015 (2015). Access June 2017
  27. 27.
    Poulton, T., Ellaway, R.H., et al.: Exploring the efficacy of replacing linear paper-based patient cases in problem-based learning with dynamic Web-based virtual patients: randomized controlled trial. J. Med. Internet Res. 16(11) (2014)Google Scholar
  28. 28.
    Antoniou, P.E., et al.: Exploring design requirements for repurposing dental virtual patients from the web to second life: a focus group study. J. Med. Internet Res. 16(6) (2014)Google Scholar
  29. 29.
    Margaryan, A., Bianco, M., Littlejohn, A.: Instructional quality of Massive Open Online Courses (MOOCs). Computers and Education 80, 77–83 (2015). doi: 10.1016/j.compedu.2014.08.005 CrossRefGoogle Scholar
  30. 30.
    Harder, B.: Are MOOCs the future of medical education?. BMJ: British Medical Journal 346 (2013)Google Scholar
  31. 31.
    Bamidis, P.D., Papadelis, C., Kourtidou-Papadeli, C., Pappas, C., Vivas, A.: Affective computing in the era of contemporary neurophysiology and health informatics. Interacting with Computers 16(4), 715–721 (2004)CrossRefGoogle Scholar
  32. 32.
    Lithari, C., Frantzidis, C.A., Papadelis, C., Vivas, A.B., Klados, M.A., Kourtidou-Papadeli, C., Pappas, C., Ioannides, A.A., Bamidis, P.D.: Are females more responsive to emotional stimuli? A Neurophysiological Study Across Arousal and Valence Dimensions Brain Topography 23(1), 27–40 (2010)Google Scholar
  33. 33.
    Styliadis, C., Ioannides, A.A., Bamidis, P.D., Papadelis, C.: Amygdala responses to valence and its interaction by arousal revealed by MEG. International Journal of Psychophysiology 93(1), 121–133 (2014)CrossRefGoogle Scholar
  34. 34.
    Klados, M.A., Frantzidis, C., Vivas, A.B., Papadelis, C., Lithari, C., Pappas, C., Bamidis, P.D.: A framework combining delta event-related oscillations (EROs) and synchronisation effects (ERD/ERS) to study emotional processing. Computational Intelligence and Neuroscience (2009). Art. no. 549419Google Scholar
  35. 35.
    Lithari, C., Klados, M.A., Pappas, C., Albani, M., Kapoukranidou, D., Kovatsi, L., Bamidis, P.D., Papadelis, C.L.: Alcohol Affects the Brain’s Resting-State Network in Social Drinkers PLoS ONE 7(10) (2012). Art. no. e48641Google Scholar
  36. 36.
    Ladas, A., Frantzidis, C., Bamidis, P., Vivas, A.B.: Eye blink rate as a biological marker of mild cognitive impairment. International Journal of Psychophysiology 93(1), 12–16 (2014)CrossRefGoogle Scholar
  37. 37.
    Frantzidis, C.A., Vivas, A.B., Tsolaki, A., Klados, M.A., Tsolaki, M., Bamidis, P.D.: Functional disorganization of small-world brain networks in mild Alzheimer’s Disease and amnestic Mild Cognitive Impairment: an EEG study using Relative Wavelet Entropy (RWE). Frontiers in Aging Neuroscience 6 (2014)Google Scholar
  38. 38.
    Klados, M.A., Kanatsouli, K., Antoniou, I., Babiloni, F., Tsirka, V., Bamidis, P.D., Micheloyannis, S.: A graph theoretical approach to study the organization of the cortical networks during different mathematical tasks. PloS One 8(8), e71800 (2013)CrossRefGoogle Scholar
  39. 39.
    Konstantinidis, E., Luneski, A., Frantzidis, C., Nikolaidou, M., Hitoglou-Antoniadou, M., Bamidis, P.D.: Information and Communication Technologies (ICT) for enhanced education of children with autism spectrum disorders. Journal on Information Technology in Healthcare 7(5), 284–292 (2009)Google Scholar
  40. 40.
    Lithari, C., Klados, M.A., Papadelis, C., Pappas, C., Albani, M., Bamidis, P.D.: How does the metric choice affect brain functional connectivity networks? Biomedical Signal Processing and Control 7(3), 228–236 (2012)CrossRefGoogle Scholar
  41. 41.
    Klados, M.A., Papadelis, C., Braun, C., Bamidis, P.D.: REG-ICA: A hybrid methodology combining Blind Source Separation and regression techniques for the rejection of ocular artifacts. Biomedical Signal Processing and Control 6(3), 291–300 (2011)CrossRefGoogle Scholar
  42. 42.
    Antoniou, P.E., Kartsidis, P., Xefteris, S., Arfaras, G., Konstantinidis, E., Bamidis, P.D.: Towards medical education neuroscience: pilot results of integrative affective analytics from clinical skills workshops (2016).
  43. 43.
    Βillis, A., Styliadis, C., Baka, M., Arfaras, G., Bamidis, P.D.: Affective neuroscience/computing approaches in evaluating an interactive educational tool to counteract bullying (2016).
  44. 44.
    Alevizos, S., Lagoumintzi, I., Salichos, P.: The Interaction between Τheory and Practice in Social Pedagogy : Α European Campaign and an Interactive Social Pedagogical Tool against Bullying in Schools. The International Journal of Social Pedagogy 4(1), 55–64 (2015)CrossRefGoogle Scholar
  45. 45.
    Broekens, J., Brinkman, W.P.: AffectButton: A method for reliable and valid affective self-report. International Journal of Human Computer Studies 71(6), 641–667 (2013). doi: 10.1016/j.ijhcs.2013.02.003 CrossRefGoogle Scholar
  46. 46.
    Hadjileontiadou, J.S., Dias, B.S., Diniz, A.J., Hadjileontiadis, J.L.: Fuzzy Logic-Based Modeling in Collaborative and Blended Learning, in Advances in Educational Technologies and Instructional Design (AETID). IGI Global, Hershey (2015)Google Scholar
  47. 47.
    Antoniou P.E., et al.: Applications of educational neuroscience: the use of emotional estimators in three educational scenarios. In: Workshop on Aristotle University of Thessaloniki Studies and the Globe in 21st Century (2016). (In Greek). Access Jul 2017
  48. 48.
    Watson, D., Clark, A.L., Tellegen, A.: Development and validation of brief measures of positive and negative affect: the PANAS scales. Journal of Personality and Social Psychology 54(6), 1063–1070 (1988). doi: 10.1037/0022-3514.54.6.1063 CrossRefGoogle Scholar
  49. 49.
    Young, J.Q., Ranji, S.R., Wachter, R.M., Lee, C.M., Niehaus, B., Auerbach, A.D.: “July Effect”: Impact of the Academic Year-End Changeover on Patient Outcomes. A Systematic Review. Annals of Internal Medicine 155(5), 309–315 (2011)CrossRefGoogle Scholar
  50. 50.
    Dafli, E., Antoniou, P., Ioannidis, L., Dombros, N., Topps, D., Bamidis, P.D.: Virtual patients on the semantic Web: a proof-of-application study. Journal of Medical Internet Research 17(1) (2015)Google Scholar
  51. 51.
    Antoniou, P.E., Dafli, E., Bamidis, P.D.: Design of novel teaching episodes in medical education using emerging experiential digital assets: technology enabled medical education beyond the gimmicky. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Liverpool, pp. 1560–1565 (2015). doi: 10.1109/CIT/IUCC/DASC/PICOM.2015.360
  52. 52.
    Bamidis, P.D., Dimitrova, V., Tresure-Jones, T., Poulton, T., Roberts T.E.: Augmented minds: technology’s role in supporting. In: 21st Century Doctors. EC-TEL Workshop on European Technologies and Workplace Learning and Professional Development (2017)Google Scholar
  53. 53.
    Piotrkowicz, A., Dimitrova, V., Treasure-Jones, T., Smithies, A., Harkin, P., Kirby, J., Roberts, T.: Quantified self analytics tools for self-regulated learning with myPAL EC-TEL (2017)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Aristotle University of ThessalonikiThessalonikiGreece
  2. 2.University of LeedsLeedsUK

Personalised recommendations