Skip to main content

Feedback and Affectivity in Intelligent Tutoring Systems

  • Chapter
  • First Online:
Affective Feedback in Intelligent Tutoring Systems

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

  • 626 Accesses

Abstract

The learning process using computers started three decades ago. Several computer-based learning systems have been developed during these years. Computer-based learning systems are particularly appropriate for remote teaching and learning at any time and place, away from classrooms and do not necessarily require the presence of a human instructor (Alepis and Virvou in Expert Syst. Appl. 38:9840–9847, 2011, [1]). There are several types of computer-based learning systems such as Computer Assisted Instruction (CAI), Cognitive Tools (CT), and Intelligent Tutoring Systems (ITS).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Alepis, E., Virvou, M.: Automatic generation of emotions in tutoring agents for affective e-learning in medical education. Expert Syst. Appl. 38(8), 9840–9847 (2011). https://doi.org/10.1016/j.eswa.2011.02.021

    Article  Google Scholar 

  2. Como, G., Docente, E., Vigo, U.D.: Ventajas e inconvenientes de la tutoría group tutoring as a quality resource in higher education. Case Study 1(4), 155–166 (2010)

    Google Scholar 

  3. García, F., Portillo, J., Romo, J., Benito, M.: Nativos digitales y modelos de aprendizaje. CEUR Workshop Proc. 318 (2007)

    Google Scholar 

  4. Pagano, C.M.: Los tutores en la educación a distancia. Un aporte teórico. Revista de Universidad y Sociedad del Conocimiento 4(2), 1–11 (2008)

    Google Scholar 

  5. Mitchell, C.M., Ha, E.Y., Boyer, K.E., Lester, J.C.: Learner characteristics and dialogue: recognising effective and student-adaptive tutorial strategies. Int. J. Learn. Technol. 8(4), 382 (2013). https://doi.org/10.1504/IJLT.2013.059132, http://www.inderscience.com/link.php?id=59132

    Article  Google Scholar 

  6. Juárez-Ramírez, R., Navarro-Almanza, R., Gomez-Tagle, Y., Licea, G., Huertas, C., Quinto, G.: Orchestrating an adaptive intelligent tutoring system: towards integrating the user profile for learning improvement. Procedia Soc. Behav. Sci. 106, 1986–1999 (2013). https://doi.org/10.1016/j.sbspro.2013.12.227

    Article  Google Scholar 

  7. Becker, L., Palmer, M., van Vuuren, S., Ward, W.: Question ranking and selection in tutorial dialogues. In: The 7th Workshop on the Innovative Use of NLP for Building Educational Applications, pp. 1–11. Association for Computational Linguistics, Montreal, Canada (2012). http://dl.acm.org/citation.cfm?id=2390384.2390385

  8. Vaessen, B.E., Prins, F.J., Jeuring, J.: University students’ achievement goals and help-seeking strategies in an intelligent tutoring system. Comput. Educ. 72, 196–208 (2014). https://doi.org/10.1016/j.compedu.2013.11.001, http://www.sciencedirect.com/science/article/pii/S0360131513003060

    Article  Google Scholar 

  9. Latham, A.M., Crockett, K.A., McLean, D.A., Edmonds, B., O’Shea, K.: Oscar: an intelligent conversational agent tutor to estimate learning styles. In: International Conference on Fuzzy Systems, pp. 1–8. IEEE (2010). https://doi.org/10.1109/FUZZY.2010.5584064

  10. Rahati, A., Kabanza, F.: Persuasive dialogues in an intelligent tutoring. Intell. Tutoring Syst. 2010, 51–61 (2010)

    Google Scholar 

  11. Stottler, D., Domeshek, E.: Intelligent Tutoring Systems (ITSs): advanced learning technology for enhancing warfighter performance. In: Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC) 2005, vol. 2112, pp. 1–7 (2005)

    Google Scholar 

  12. Motola, R., Jaques, P., Axt, M., Vicari, R.: Architecture for animation of affective behaviors in pedagogical agents. J. Braz. Comput. Soc. 15(4), 3–13 (2009). https://doi.org/10.1007/bf03194509

    Article  Google Scholar 

  13. Ferreira, A., Atkinson, J.: Designing a feedback component of an intelligent tutoring system for foreign language. Knowl. Based Syst. 22(7), 496–501 (2009). https://doi.org/10.1016/j.knosys.2008.10.012

    Article  Google Scholar 

  14. D’Mello, S.K., Graesser, A.: Language and discourse are powerful signals of student emotions during tutoring. IEEE Trans. Learn. Technol. 5(4), 304–317 (2012). https://doi.org/10.1109/TLT.2012.10

    Article  Google Scholar 

  15. Padayachee, I.: Intelligent tutoring systems: architecture and characteristics. In: Proceedings of the 32nd Annual SACLA Conference (Cité p. 2), pp. 1–8, January 2002

    Google Scholar 

  16. Rahati, A., Kabanza, F.: Automated planning of tutorial dialogues. In: 2010 International Conference on Autonomous and Intelligent Systems, AIS 2010, pp. 1–6 (2010). https://doi.org/10.1109/AIS.2010.5547015, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5547015

  17. Angelaki, C., Mavroidis, I.: Communication and social presence: the impact on adult learners’ emotions in distance learning. Eur. J. Open Distance E-learn. 16(1), 78–93 (2013)

    Google Scholar 

  18. Hattie, J., Timperley, H.: The power of feedback. Rev. Educ. Res. 77(1), 81–112 (2007). https://doi.org/10.3102/003465430298487

    Article  Google Scholar 

  19. Nicol, D.: From monologue to dialogue: improving written feedback processes in mass higher education. Assess. Eval. High. Educ. 35(5), 501–517 (2010). https://doi.org/10.1080/02602931003786559

    Article  Google Scholar 

  20. Gillies, R.M.: Dialogic interactions in the cooperative classroom. Int. J. Educ. Res. (2015). https://doi.org/10.1016/j.ijer.2015.02.009, http://linkinghub.elsevier.com/retrieve/pii/S0883035515000117

  21. Colman, A.: A Dictionary of Psychology, 3rd edn. Oxford University Press (2014)

    Google Scholar 

  22. Brookhart, S.M.: Effective Feedback, 1st edn. Association for Supervision and Curriculum Development, USA (2008). https://doi.org/10.1016/j.ajic.2009.04.219

  23. Dennis, M., Masthoff, J., Mellish, C.: Adapting progress feedback and emotional support to learner personality. Int. J. Artif. Intell. Educ. 26(3), 877–931 (2016). https://doi.org/10.1007/s40593-015-0059-7

    Article  Google Scholar 

  24. Rica, U.D.C., Pedro, S., Oca, M.D., Rica, C.: The emotional intelligence, its importance in the learning process. Educación 36(1), 1–24 (2012)

    Google Scholar 

  25. Shephard, K.: Higher education for sustainability: seeking affective learning outcomes (2008). https://doi.org/10.1108/14676370810842201

    Article  Google Scholar 

  26. García, B.: Las Dimensiones Afectivas de La Docencia. Revista Digital Universitaria 10, 1–14 (2009)

    Google Scholar 

  27. Lara, V.R.: Affectivity in mathematical learning: experimental case in University of Veracruz. Ph.D. thesis, Universidad Autónoma de Tamaulipas, Mexico (2003)

    Google Scholar 

  28. Armour, W.: Emotional intelligence, student engagement, teaching practice, employability, ethics curriculum. Invest. Univ. Teach. Learn. 8(2004), 4–10 (2012)

    Google Scholar 

  29. Minghe, G.U.O., Yuan, W.: Affective factors in oral English teaching and learning. High. Educ. Soc. Sci. 5(3), 57–61 (2013). https://doi.org/10.3968/j.hess.1927024020130503.2956

    Article  Google Scholar 

  30. Dennis, M., Masthoff, J., Mellish, C.: Towards a model of personality, affective state, feedback and learner motivation. CEUR Workshop Proc. 872, 17–22 (2012)

    Google Scholar 

  31. Letzring, T.D., Adamcik, L.A.: Personality traits and affective states: relationships with and without affect induction. Pers. Individ. Diff. 75, 114–120 (2015). https://doi.org/10.1016/j.paid.2014.11.011

    Article  Google Scholar 

  32. Wolfe, C.R., Widmer, C.L., Reyna, V.F., Hu, X., Cedillos, E.M., Fisher, C.R., Brust-Renck, P.G., Williams, T.C., Damas Vannucchi, I., Weil, A.M.: The development and analysis of tutorial dialogues in AutoTutor Lite. Behav. Res. Methods 45(3), 623–36 (2013). https://doi.org/10.3758/s13428-013-0352-z

    Article  Google Scholar 

  33. Barón-Estrada, M.L., Zatarain-Cabada, R., Zatarain-Cabada, R., Barrón-Estrada, A.: Design and implementation of an affective ITS. Res. Comput. Sci. 56(August), 60–68 (2012)

    Google Scholar 

  34. Koutropoulos, A., Gallagher, M.S., Abajian, S.C., de Waard, I., Hogue, R.J., Keskin, N.O., Rodriguez, O.C.: Emotive vocabulary in MOOCs: context & participant retention. Eur. J. Open Distance E-Learn. 23 (2012)

    Google Scholar 

  35. Hinojosa, J.A., Martínez-García, N., Villalba-García, C., Fernández-Folgueiras, U., Sánchez-Carmona, A., Pozo, M.A., Montoro, P.R.: Affective norms of 875 Spanish words for five discrete emotional categories and two emotional dimensions. Behav. Res. Methods 48(1), 1–13 (2015). https://doi.org/10.3758/s13428-015-0572-5, http://link.springer.com/10.3758/s13428-015-0572-5

    Article  Google Scholar 

  36. Warriner, A.B., Kuperman, V., Brysbaert, M.: Norms of valence, arousal, and dominance for 13,915 English lemmas. Behav. Res. Methods 45(4), 1191–1207 (2013). https://doi.org/10.3758/s13428-012-0314-x, http://link.springer.com/10.3758/s13428-012-0314-x

    Article  Google Scholar 

  37. Schauenburg, G., Ambrasat, J., Schröder, T., von Scheve, C., Conrad, M.: Emotional connotations of words related to authority and community. Behav. Res. Methods 47(3), 720–35 (2015). https://doi.org/10.3758/s13428-014-0494-7, http://www.ncbi.nlm.nih.gov/pubmed/24928263

    Article  Google Scholar 

  38. Wang, J., Yu, L.C., Lai, K.R., Zhang, X.: Locally weighted linear regression for cross-lingual valence-arousal prediction of affective words. Neurocomputing 194, 271–278 (2016). https://doi.org/10.1016/j.neucom.2016.02.057

    Article  Google Scholar 

  39. Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013). https://doi.org/10.1111/j.1467-8640.2012.00460.x

    Article  MathSciNet  Google Scholar 

  40. Bradley, M.M., Lang, P.P.J.: Affective Norms for English Words (ANEW): instruction manual and affective ratings. Psychol. Tech. 0 (1999). https://doi.org/10.1109/MIC.2008.114

  41. Redondo, J., Fraga, I., Comesaña, M., Perea, M.: Estudio normativo del valor afectivo de 478 palabras españolas. Psicologica 26(2), 317–326 (2005)

    Google Scholar 

  42. Bradley, M.M., Lang, P.J.: Measuring emotion: the self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 25(1), 49–59 (1994)

    Article  Google Scholar 

  43. Díaz Rangel, I., Sidorov, G., Suárez-Guerra, S.: Creación y Evaluación de un diccionario marcado emociones y ponderado para el español 1–23 (2014). https://doi.org/10.7764/onomazein.29.5

  44. Ekman, P.: Argument for basic emotions. Cogn. Emot. 6(3–4), 169–200 (1992)

    Article  Google Scholar 

  45. Baca-gómez, Y.R., Irazú, D., Farías, H., Rosso, P.: Impacto de la ironía en la minería de opiniones basada en un léxico afectivo 2014 (2015)

    Google Scholar 

  46. Mohammad, S.M., Turney, P.D.: Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon. In: CAAGET 2010 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 26–34, June 2010. http://dl.acm.org/citation.cfm?id=1860631.1860635

  47. Elizabeth, K., Michael, D., Mladen, A., James, C., Carolina, N.: Learner characteristics and feedback in tutorial dialogue. In: Proceedings of the Third ACL Workshop on Innovative Use of NLP for Building Educational Applications, pp. 53–61, June 2008

    Google Scholar 

  48. Wang, D., Han, H., Zhan, Z., Xu, J., Liu, Q., Ren, G.: A problem solving oriented intelligent tutoring system to improve students’ acquisition of basic computer skills. Comput. Educ. 81, 102–112 (2015). https://doi.org/10.1016/j.compedu.2014.10.003

    Article  Google Scholar 

  49. Tetreault, J., Litman, D.: Using reinforcement learning to build a better model of dialogue state. In: EACL, pp. 289–296 (2006). http://acl-arc.comp.nus.edu.sg/archives/acl-arc-090501d3/data/pdf/anthology-PDF/E/E06/E06-1037.pdf

  50. Forbes-Riley, K., Rotaru, M., Litman, D.J.: The relative impact of student affect on performance models in a spoken dialogue tutoring system. User Model. User Adapt. Interact. 18(1–2), 11–43 (2007). https://doi.org/10.1007/s11257-007-9038-5, http://link.springer.com/10.1007/s11257-007-9038-5

    Article  Google Scholar 

  51. Garrett, P., Young, R.F.: Theorizing affect in foreign language learning: an analysis of one learner’s responses to a communicative portuguese course. Mod. Lang. J. 93(2), 209–226 (2009). https://doi.org/10.1111/j.1540-4781.2009.00857.x

    Article  Google Scholar 

  52. Jaques, P., Vicari, R., Pesty, S.: Applying affective tactics for a better learning. In: 16th ECAI 2004, pp. 1–5 (2004)

    Google Scholar 

  53. Kort, B., Reilly, R., Picard, R.W.: Affective model of interplay between emotions and learning- reengineering educational pedagogy-building a learning companion. In: Proceedings of IEEE International Conference on Advanced Learning Technologies, Madison, WI, pp. 43–46 (2001)

    Google Scholar 

  54. Abrami, P.C., Bernard, R.M., Bures, E.M., Borokhovski, E., Tamim, R.M.: Interaction in distance education and online learning: using evidence and theory to improve practice. J. Comput. High. Educ. 23(2–3), 82–103 (2011). https://doi.org/10.1007/s12528-011-9043-x

    Article  Google Scholar 

  55. Banda, N., Robinson, P.: Multimodal affect recognition in intelligent tutoring systems. In: Fourth International Conference. ACII 2011, pp. 200–207. Springer, Memphis (2011)

    Chapter  Google Scholar 

  56. 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), 1–39 (2012). https://doi.org/10.1145/2395123.2395128, http://dl.acm.org/citation.cfm?id=2395123.2395128

    Article  Google Scholar 

  57. D’Mello, S., Olney, A., Williams, C., Hays, P.: Gaze tutor: a gaze-reactive intelligent tutoring system. Int. J. Hum. Comput. Stud. 70(5), 377–398 (2012). https://doi.org/10.1016/j.ijhcs.2012.01.004

    Article  Google Scholar 

  58. D’Mello, S.K., Graesser, A.: Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Modell. User Adapt. Interact. 20(2), 147–187 (2010). https://doi.org/10.1007/s11257-010-9074-4

    Article  Google Scholar 

  59. Duffy, M.C., Azevedo, R.: Motivation matters: interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. Comput. Hum. Behav. 52, 338–348 (2015). https://doi.org/10.1016/j.chb.2015.05.041

    Article  Google Scholar 

  60. Grafsgaard, J.F., Wiggins, J.B., Boyer, K.E., Wiebe, E.N., Lester, J.C.: Embodied Affect in Tutorial Dialogue: Student Gesture and Posture. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNAI, vol. 7926, pp. 1–10 (2013). https://doi.org/10.1007/978-3-642-39112-5-1

    Google Scholar 

  61. Jaques, N., Conati, C., Harley, J., Azevedo, R.: Predicting affect from gaze data during interaction with an intelligent tutoring system. In: 12th International Conference, ITS 2014, pp. 29–38. Springer, Honolulu (2014). https://doi.org/10.1007/978-3-319-07221-0_4

    Chapter  Google Scholar 

  62. Munoz, K., Noguez, J., Kevitt, P.M., Lunney, T., Neri, L.: Work in progress: towards an emotional learning model for intelligent gaming. In: 2010 IEEE Frontiers in Education Conference (FIE), pp. T3G-1–T3G-2 (2010). https://doi.org/10.1109/FIE.2010.5673225

  63. Neviarouskaya, A., Prendinger, H., Ishizuka, M.: User study on AffectIM, an avatar-based instant messaging system employing rule-based affect sensing from text. Int. J. Hum. Comput. Stud. 68(7), 432–450 (2010). https://doi.org/10.1016/j.ijhcs.2010.02.003

    Article  Google Scholar 

  64. Vanlehn, K., Burleson, W., Echeagaray, M.E.C., Christopherson, R., Sanchez, J.G., Hastings, J., Pontet, Y.H., Zhang, L.: The affective meta-tutoring project: how to motivate students to use effective meta-cognitive strategies. In: 19th International Conference on Computers in Education, Chiang Mai, Thailand, pp. 1–3 (2011)

    Google Scholar 

  65. Forbes-Riley, K., Litman, D.: Benefits and challenges of real-time uncertainty detection and adaptation in a spoken dialogue computer tutor. Speech Commun. 53(9–10), 1115–1136 (2011). https://doi.org/10.1016/j.specom.2011.02.006, http://www.sciencedirect.com/science/article/pii/S0167639311000318

    Article  Google Scholar 

  66. Arguedas, M., Xhafa, F., Daradoumis, T.: An ontology about emotion awareness and affective feedback in e-learning. In: 2015 International Conference on Intelligent Networking and Collaborative Systems, pp. 156–163 (2015). https://doi.org/10.1109/INCoS.2015.78, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7312065

  67. Vicente, A.D., Pain, H.: Motivation diagnosis in intelligent tutoring systems. In: Proceedings of the 4th International Conference on Intelligent Tutoring Systems, vol. 83, pp. 86–95 (1998). https://doi.org/10.1103/PhysRevB.83.121309, http://portal.acm.org/citation.cfm?id=648029.745332

  68. Theng, Y.L., Aung, P.: Investigating effects of avatars on primary school children’s affective responses to learning. J. Multimodal User Interf. 5(1–2), 45–52 (2012). https://doi.org/10.1007/s12193-011-0078-0

    Article  Google Scholar 

  69. Woolf, B.P., Arroyo, I., Cooper, D., Burleson, W., Muldner, K.: Affective Tutors: Automatic Detection of and Response to Student Emotion. Studies in Computational Intelligence (Shute 2008), vol. 308, pp. 207–227 (2010). https://doi.org/10.1007/978-3-642-14363-2

    Google Scholar 

  70. Naghizadeh, M., Moradi, H.: A model for motivation assessment in intelligent tutoring systems. In: 2015 7th Conference on Information and Knowledge Technology (IKT), pp. 1–6 (2015). https://doi.org/10.1109/IKT.2015.7288774

  71. Van der Meij, H., Van der Meij, J., Harmsen, R.: Animated pedagogical agents effects on enhancing student motivation and learning in a science inquiry learning environment. Educ. Technol. Res. Dev. 63(3), 381–403 (2015). https://doi.org/10.1007/s11423-015-9378-5, http://link.springer.com/10.1007/s11423-015-9378-5

  72. Orhan, Ç., Çetin, B., Imran, A.: A motivation study on the effectiveness of intrinsic and extrinsic factors. Econ. Manag. 16, 690–696 (2011)

    Google Scholar 

  73. Keller, J.M.: Using the ARCS motivational process in computer-based instruction and distance education. New Dir. Teach. Learn. 78, 39–47 (1999). https://doi.org/10.1002/tl.7804

    Article  Google Scholar 

  74. Mustafa, S.M.S., Elias, H., Noah, S.M., Roslan, S.: A proposed model of motivational influences on academic achievement with flow as the mediator. Procedia Soc. Behav. Sci. 7(2), 2–9 (2010). https://doi.org/10.1016/j.sbspro.2010.001

    Article  Google Scholar 

  75. Mubeen, S., Reid, N.: The measurement of motivation with science students. Eur. J. Educ. Res. 3(3), 129–144 (2014). https://doi.org/10.12973/eu-jer.3.3.129

    Article  MathSciNet  Google Scholar 

  76. McCord, M., Matusovich, H.: Developing an instrument to measure motivation, learning strategies and conceptual change. In: 120th ASEE Annual Conference and Exposition, Atlanta, pp. 1–21 (2013)

    Google Scholar 

  77. Novak, E.: Toward a mathematical model of motivation, volition, and performance. Comput. Educ. 74, 73–80 (2014). https://doi.org/10.1016/j.compedu.2014.01.009

    Article  Google Scholar 

  78. Pakarinen, E., Aunola, K., Kiuru, N., Lerkkanen, M.K., Poikkeus, A.M., Siekkinen, M., Nurmi, J.E.: The cross-lagged associations between classroom interactions and children’s achievement behaviors. Contemp. Educ. Psychol. 39(3), 248–261 (2014). https://doi.org/10.1016/j.cedpsych.2014.06.001

    Article  Google Scholar 

  79. Song, D., Bonk, C.J., English, R.M.: Motivational factors in self-directed informal learning from online learning resources. Cogent Educ. 3, Article 1205838, July 2016. https://doi.org/10.1080/2331186X.2016.1205838, https://www.cogentoa.com/article/10.1080/2331186X.2016.1205838

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Reyes Juárez-Ramírez .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 The Author(s), under exclusive license to Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Jiménez, S., Juárez-Ramírez, R., Castillo, V.H., Tapia Armenta, J.J. (2018). Feedback and Affectivity in Intelligent Tutoring Systems. In: Affective Feedback in Intelligent Tutoring Systems. Human–Computer Interaction Series(). Springer, Cham. https://doi.org/10.1007/978-3-319-93197-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93197-5_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93196-8

  • Online ISBN: 978-3-319-93197-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics