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).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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
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)
García, F., Portillo, J., Romo, J., Benito, M.: Nativos digitales y modelos de aprendizaje. CEUR Workshop Proc. 318 (2007)
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)
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
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
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
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
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
Rahati, A., Kabanza, F.: Persuasive dialogues in an intelligent tutoring. Intell. Tutoring Syst. 2010, 51–61 (2010)
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)
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
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
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
Padayachee, I.: Intelligent tutoring systems: architecture and characteristics. In: Proceedings of the 32nd Annual SACLA Conference (Cité p. 2), pp. 1–8, January 2002
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
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)
Hattie, J., Timperley, H.: The power of feedback. Rev. Educ. Res. 77(1), 81–112 (2007). https://doi.org/10.3102/003465430298487
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
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
Colman, A.: A Dictionary of Psychology, 3rd edn. Oxford University Press (2014)
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
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
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)
Shephard, K.: Higher education for sustainability: seeking affective learning outcomes (2008). https://doi.org/10.1108/14676370810842201
García, B.: Las Dimensiones Afectivas de La Docencia. Revista Digital Universitaria 10, 1–14 (2009)
Lara, V.R.: Affectivity in mathematical learning: experimental case in University of Veracruz. Ph.D. thesis, Universidad Autónoma de Tamaulipas, Mexico (2003)
Armour, W.: Emotional intelligence, student engagement, teaching practice, employability, ethics curriculum. Invest. Univ. Teach. Learn. 8(2004), 4–10 (2012)
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
Dennis, M., Masthoff, J., Mellish, C.: Towards a model of personality, affective state, feedback and learner motivation. CEUR Workshop Proc. 872, 17–22 (2012)
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
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
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)
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)
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
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
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
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
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
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
Redondo, J., Fraga, I., Comesaña, M., Perea, M.: Estudio normativo del valor afectivo de 478 palabras españolas. Psicologica 26(2), 317–326 (2005)
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)
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
Ekman, P.: Argument for basic emotions. Cogn. Emot. 6(3–4), 169–200 (1992)
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)
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
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
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
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
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
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
Jaques, P., Vicari, R., Pesty, S.: Applying affective tactics for a better learning. In: 16th ECAI 2004, pp. 1–5 (2004)
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)
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
Banda, N., Robinson, P.: Multimodal affect recognition in intelligent tutoring systems. In: Fourth International Conference. ACII 2011, pp. 200–207. Springer, Memphis (2011)
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
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
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
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
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
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
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
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
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)
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
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
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
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
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
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
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
Orhan, Ç., Çetin, B., Imran, A.: A motivation study on the effectiveness of intrinsic and extrinsic factors. Econ. Manag. 16, 690–696 (2011)
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
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
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
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)
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
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
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
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2018 The Author(s), under exclusive license to Springer International Publishing AG, part of Springer Nature
About this chapter
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)