Revisión sistemática de la literatura sobre los sistemas tutores afectivos: 2001-2020

Arlem Aleida Castillo Avila, Juan Manuel González Calleros, Josefina Guerrero García

Resumo


El interés en el desarrollo de sistemas tutores inteligentes ha generado una amplia gama de estudios de investigación multidisciplinaria, así como el desarrollo de herramientas para distintas aplicaciones. Entre ellos se encuentran aquellos tutores que integran el estado afectivo del estudiante cuando tiene una sesión interactiva. Se realizó una revisión sistemática de la literatura en torno a los sistemas tutores inteligentes que toman en cuenta las emociones, denominados sistemas tutores afectivos, entre el periodo de 2001 y 2020. Este documento reporta los resultados de un análisis bibliométrico a un conjunto de 198 documentos obtenidos de la Web of Science, Scopus, ERIC y Dimensions. Se reportan los principales hallazgos con relación a 7 preguntas de investigación, de las cuales 3 implicaron un análisis cualitativo, finalmente se dan algunas conclusiones preliminares tomando en cuenta el escenario de México con respecto al desarrollo de los sistemas tutores afectivos.


Palavras-chave


Affective Tutoring Systems, Intelligent Tutoring Systems, Computer-Mediated Learning

Texto completo:

PDF (Español (España))

Referências


Alyuz, N. (2016). Shaping the future of education with empathic companions. En ERM4CT 2016 - Proceedings of the 2nd Workshop on Emotion Representations and Modelling for Companion Systems (pp. 1–2). doi: 10.1145/3009960.3009964. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006990918&doi=10.1145%2f3009960.3009964&partnerID=40&md5=f035dbfbb894c293c531607e18daae63

Andres, J., Paquette, L., Ocumpaugh, J., Jiang, Y., Baker, R., Karumbaiah, S., ..., & Biswas, G. (2019). Affect sequences and learning in Betty’s brain. En ACM International Conference Proceeding Series (pp. 383–390). doi: 10.1145/3303772.3303807. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062792861&doi=10.1145%2f3303772.3303807&partnerID=40&md5=a0c7ad0f37adba93ccef35385237c04f

Arguedas, M., Casillas, L., Xhafa, F., Daradoumis, T., Pena, A., & Caballe, S. (2016). A Fuzzy-Based Approach for Classifying Students’ Emotional States in Online Collaborative Work. En Proceedings - 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2016 (pp. 61–68). doi: 10.1109/CISIS.2016.141. Descargado de https://www.scopus.com/record/display.uri?eid=2-s2.0-85011028481&doi=10.1109%2fCISIS.2016.141&origin=inward&txGid=e6f143afd097ce7c8c2a2f221edc8bba

Arguel, A., Lockyer, L., Chai, K., Pachman, M., & Lipp, O. (2019). Puzzle-solving activity as an indicator of epistemic confusion. Frontiers in Psychology, 10(JAN). doi: 10.3389/fpsyg.2019.00163. Descargado de https://www.scopus.com/record/display.uri?eid=2-s2.0-85061061370&doi=10.3389%2ffpsyg.2019.00163&origin=inward&txGid=b7d1256f2e7ba2ffce8f9a6ecaf3704d

Arroyo, I., Schultz, S., Wixon, N., Muldner, K., Burleson, W., & Woolf, B. (2016). Addressing affective states with empathy and growth mindset. En CEUR Workshop Proceedings (Vol. 1618). [GS Search]

Arroyo, I., Woolf, B. P., Burelson, W., Muldner, K., Rai, D., Tai, M. (2014, diciembre). A Multimedia Adaptive Tutoring System for Mathematics that Addresses Cognition, Metacognition and Affect. International Journal of Artificial Intelligence in Education, 24(4), 387–426. doi: 10.1007/s40593-014-0023-y. Descargado 2020-08-14, de http://link.springer.com/10.1007/s40593-014-0023-y

Ashwin, T., & Guddeti, R. (2020). Impact of inquiry interventions on students in e-learning and classroom environments using affective computing framework. User Modeling and User-Adapted Interaction. doi: 10.1007/s11257-019-09254-3. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077538149&doi=10.1007%2fs11257-019-09254-3&partnerID=40&md5=5e581dd6d58d2aed5a84e38bfe1bf120

Aslan, S., Okur, E., Alyuz, N., Esme, A., & Baker, R. (2019). Human expert labeling process: Valence-arousal labeling for students’ affective states. Advances in Intelligent Systems and Computing, 804, 53–61. doi: 10.1007/978-3-319-98872-6_7. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057307096&doi=10.1007%2f978-3-319-98872-6_7&partnerID=40&md5=56f53a28bfde112946f2fd47dd0d64f7

Baldassarri, S., Hupont, I., Abadía, D., & Cerezo, E. (2015). Affective-aware tutoring platform for interactive digital television. Multimedia Tools and Applications, 74(9), 3183-3206. doi: 10.1007/s11042-013-1779-z. Descargado de https://www.scopus.com/record/display.uri?eid=2-s2.0-84928376617&doi=10.1007%2fs11042-013-1779-z&origin=inward&txGid=8593a75198aa04d87d31318aaa257b70

Bosch, N., & D’Mello, S. (2013). Programming with your heart on your sleeve: Analyzing the affective states of computer programming students. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7926 LNAI, 908–911. doi: 10.1007/978-3-642-39112-5_143. Descargado de https://www.scopus.com/record/display.uri?eid=2-s2.0-84880011305&doi=10.1007%2f978-3-642-39112-5-143&origin=inward&txGid=f45b763d74b7218ce79425251581ce30

Cabada, R. Z., Estrada, M. L. B., Beltran V., J. A., Cibrian R., F., Garcia, C. A. R., & Perez, Y. H. (2012, julio). Fermat: Merging Affective Tutoring Systems with Learning Social Networks. En 2012 IEEE 12th International Conference on Advanced Learning Technologies (pp. 337–339). Rome: IEEE. doi: 10.1109/ICALT.2012.140. Descargado de https://ieeexplore.ieee.org/document/6268112/

Cabada, R. Z., Estrada, M. L. B., Hernández, F. G., & Bustillos, R. O. (2015). An affective learning environment for Java. En 2015 IEEE 15th International Conference on Advanced Learning Technologies (pp. 350–354). doi: 10.1109/ICALT.2015.53 [GS Search]

Chao, C.-J., Tsai, S.-C., Lee, C.-H., Wang, T.-H., & Lin, H.-C. (2014). The impact of affective tutoring system and information literacy on elementary school students’ cognitive load and learning outcomes. En Workshop Proceedings of the 22nd International Conference on Computers in Education, ICCE 2014 (pp. 847–856). [GS Search]

Chaouachi, M., & Frasson, C. (2012). Mental workload, engagement and emotions: An exploratory study for intelligent tutoring systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7315 LNCS, 65–71. doi: 10.1007/978-3-642-30950-2_9. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862503273&doi=10.1007%2f978-3-642-30950-2_9&partnerID=40&md5=476806f9f66064230a9dfa8ec8fdebf2

Cruces, A. L. L., & De Arriaga, F. (2000). Reactive agent design for intelligent tutoring systems. Cybernetics & Systems, 31(1), 1–47. Taylor & Francis. doi: 10.1080/019697200124900 [GS Search]

D’Mello, S., Lehman, B., & Person, N. (2010). Monitoring affect states during effortful problem-solving activities. International Journal of Artificial Intelligence in Education, 20(4), 361–389. doi: 10.3233/JAI-2010-012. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959316616&doi=10.3233%2fJAI-2010-012&partnerID=40&md5=07e098c320694bd54e934a0704fa3570

Duffy, M., & Azevedo, R. (2015). Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. Computers in Human Behavior, 52, 338–348. doi: 10.1016/j.chb.2015.05.041. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-84936744787&doi=10.1016%2fj.chb.2015.05.041&partnerID=40&md5=0f6966d221c0a8a5993bf4e0595fcd6d

Duffy, M. C., Lajoie, S. P., Pekrun, R., & Lachapelle, K. (2020). Emotions in medical education: Examining the validity of the Medical Emotion Scale (MES) across authentic medical learning environments. Learning and Instruction, 101150. Elsevier. doi: 10.1016/j.learninstruc.2018.07.001 [GS Search]

Ekman, P., & Keltner, D. (1997). Universal facial expressions of emotion.

Erümit, A. K., & Çetin, I. (2020, abril). Design framework of adaptive intelligent tutoring systems. Education and Information Technologies. doi: 10.1007/s10639-020-10182-8. Descargado de http://link.springer.com/10.1007/s10639-020-10182-8

Fotopoulou, E., Zafeiropoulos, A., Feidakis, M., Metafas, D., & Papavassiliou, S. (2020). An interactive recommender system based on reinforcement learning for improving emotional competences in educational groups. In: Kumar V., Troussas C. (eds) Intelligent Tutoring Systems. ITS 2020. Lecture Notes in Computer Science, vol 12149. Springer, Cham. doi: 10.1007/978-3-030-49663-0_29 [GS Search]

Ghaleb, E., Popa, M., Hortal, E., Asteriadis, S., & Weiss, G. (2019). Towards Affect Recognition through Interactions with Learning Materials. En Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 (pp. 372–379). doi: 10.1109/ICMLA.2018.00062. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062207088&doi=10.1109%2fICMLA.2018.00062&partnerID=40&md5=5a9c7eaa1f61de16968deac4bdf8d764

Graesser, A., Forsyth, C., & Lehman, B. (2017). Two heads may be better than one: Learning from computer agents in conversational trialogues. Teachers College Record, 119(3). [GS Search]

Graesser, A. C., Wiemer-Hastings, K., Wiemer-Hastings, P., Kreuz, R., Group, T. R., & others. (1999). AutoTutor: A simulation of a human tutor. Cognitive Systems Research, 1(1), 35–51. Elsevier. doi: 10.1016/S1389-0417(99)00005-4 [GS Search]

Grafsgaard, J., Wiggins, J., Boyer, K., Wiebe, E., & Lester, J. (2013). Automatically recognizing facial expression: Predicting engagement and frustration. En Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013. [GS Search]

Gudino-Penaloza, F., Mendoza, M. G., Gress, N. H., & Vargas, J. M. (2009). Intelligent tutorial system for teaching of probability and statistics at high school in Mexico. World Journal on Educational Technology, 1(2). [GS Search]

Han, J., Zhao, W., Jiang, Q., Oubibi, M., & Hu, X. (2019). Intelligent tutoring system trends 2006-2018: A literature review. En Proceedings - 2019 8th International Conference of Educational Innovation through Technology, EITT 2019 (pp. 153–159). doi: 10.1109/EITT.2019.00037. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077071940&doi=10.1109%2fEITT.2019.00037&partnerID=40&md5=10b90e22ac1868db4e9df03e27650a3b

Harley, J., Poitras, E., Jarrell, A., Duffy, M., & Lajoie, S. (2016). Comparing virtual and location-based augmented reality mobile learning: emotions and learning outcomes. Educational Technology Research and Development, 64(3), 359–388. doi: 10.1007/s11423-015-9420-7. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953393252&doi=10.1007%2fs11423-015-9420-7&partnerID=40&md5=8d7df5cf9f8d583b9350b87a479f3c24

Hull, A., & du Boulay, B. (2015). Motivational and metacognitive feedback in SQL-Tutor*. Computer Science Education, 25(2), 238–256. doi: 10.1080/08993408.2015.1033143. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929516090&doi=10.1080%2f08993408.2015.1033143&partnerID=40&md5=7a220681315a2311d0ca80356cba854e

Hussain, M., Alzoubi, O., Calvo, R., & D’Mello, S. (2011). Affect detection from multichannel physiology during learning sessions with autotutor. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6738 LNAI, 131–138. doi: 10.1007/978-3-642-21869-9_19. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959307563&doi=10.1007%2f978-3-642-21869-9_19&partnerID=40&md5=57561a130deb88a4ac68850c67b7f1f0

Jimenez, S., Juarez-Ramirez, R., Castillo, V., Licea, G., Ramírez-Noriega, A., & Inzunza, S. (2018). A feedback system to provide affective support to students. Computer Applications in Engineering Education, 26(3), 473–483. doi: 10.1002/cae.21900. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047882201&doi=10.1002%2fcae.21900&partnerID=40&md5=7cb1d660a6c3758df6b982133d64bde0

Joshi, A., Allessio, D., Magee, J., Whitehill, J., Arroyo, I., Woolf, B., ..., & Betke, M. (2019). Affect-driven learning outcomes prediction in intelligent tutoring systems. En Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019. doi: 10.1109/FG.2019.8756624. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070439611&doi=10.1109%2fFG.2019.8756624&partnerID=40&md5=923ac0112ed5578d8c7359dc810b2f72

Lajoie, S., Zheng, J., Li, S., Jarrell, A., & Gube, M. (2019). Examining the interplay of affect and self-regulation in the context of clinical reasoning. Learning and Instruction. 72, 101219. doi: 10.1016/j.learninstruc.2019.101219. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068006597&doi=10.1016%2fj.learninstruc.2019.101219&partnerID=40&md5=6914da9c10e99c62fa941ff402998450

Lehman, B., D’Mello, S., & Graesser, A. (2012). Confusion and complex learning during interactions with computer learning environments. Internet and Higher Education, 15(3), 184–194. doi: 10.1016/j.iheduc.2012.01.002. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861734887&doi=10.1016%2fj.iheduc.2012.01.002&partnerID=40&md5=ef9a32a9cc62be945112bf1b4cc23db7

Leyzberg, D., Spaulding, S., & Scassellati, B. (2014). Personalizing robot tutors to individuals’ learning differences. En ACM/IEEE International Conference on Human- Robot Interaction (pp. 423–430). doi: 10.1145/2559636.2559671. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896904456&doi=10.1145%2f2559636.2559671&partnerID=40&md5=a6f5512554fda3b1cc00a4732bb27ddd

Liu, Z., Baker, R., Pataranutaporn, V., & Ocumpaugh, J. (2013). Sequences of frustration and confusion, and learning. En Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013. [GS Search]

Long, Y., & Aleven, V. (2017). Educational Game and Intelligent Tutoring System: A Classroom Study and Comparative Design Analysis. ACM Transactions on Computer-Human Interaction, 24(3), 20. doi: 10.1145/3057889. Descargado de https://app.dimensions.ai/details/publication/pub.1085093673

Long, Z., Luo, D., Xu, S., & Hu, X. (2019). Agents’ cognitive vs. Socio-affective support in response to learner’s confusion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11528 LNCS, 233–235. [GS Search]

Magner, U., Schwonke, R., Renkl, A., Aleven, V., & Popescu, O. (2010). Pictorial illustrations in intelligent tutoring systems: Do they distract or elicit interest and engagement? En Learning in the Disciplines: ICLS 2010 Conference Proceedings - 9th International Conference of the Learning Sciences (Vol. 1, pp. 1–8). [GS Search]

Mahmoud, M. H. (2019, noviembre). A Survey of Some Interdisciplinary Methods and Tools to Measure Learners’ Emotions in Intelligent Tutoring Systems. En 2019 6th International Conference on Advanced Control Circuits and Systems (ACCS) & 2019 5th International Conference on New Paradigms in Electronics & information Technology (PEIT) (pp. 1–6). Hurgada, Egypt: IEEE. doi: 10.1109/ACCS-PEIT48329.2019.9062885. Descargado 2020-08-19, de https://ieeexplore.ieee.org/document/9062885/

Marco-Gimenez, L., Arevalillo-Herraez, M., Ferri, F., Moreno-Picot, S., Boticario, J., Santos, O., ..., & Ramzan, N. (2016). Affective and behavioral assessment for adaptive intelligent tutoring systems. En CEUR Workshop Proceedings (Vol. 1618). [GS Search]

Martin, J. M., Ortigosa, A., & Carro, R. M. (2012). SentBuk: Sentiment analysis for e-learning environments. En 2012 International Symposium on Computers in Education (SIIE) (pp. 1–6). [GS Search]

Mayer, R. E. (2019). Searching for the role of emotions in e-learning. Learning and Instruction, 70, 101213. doi: 10.1016/j.learninstruc.2019.05.010 [GS Search]

McQuiggan, S., Robison, J., & Lester, J. (2008). Affective transitions in narrative-centered learning environments. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5091 LNCS, 490–499. doi: 10.1007/978-3-540-69132-7_52. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349878682&doi=10.1007%2f978-3-540-69132-7-52&partnerID=40&md5=0ac3dae1baf2726375379354907b639b

Mohanan, R., Stringfellow, C., & Gupta, D. (2017, julio). An emotionally intelligent tutoring system. En 2017 Computing Conference (pp. 1099–1107). London: IEEE. doi: 10.1109/SAI.2017.8252228. Descargado 2020-08-10, de http://ieeexplore.ieee.org/document/8252228/

Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., & Ghazi Saeedi, M. (2021). Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, 29(1), 142-163. doi: 10.1080/10494820.2018.1558257 [GS Search]

Muldner, K., Wixon, M., Rai, D., Burleson, W., Woolf, B., & Arroyo, I. (2015). Exploring the impact of a learning dashboard on student affect. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9112, 307–317. doi: 10.1007/978-3-319-19773-9_31. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948987947&doi=10.1007%2f978-3-319-19773-9_31&partnerID=40&md5=649f72fe909dfa44cd5700dcbe1e9a8a

Muñoz, K., Kevitt, P., Lunney, T., Noguez, J., & Neri, L. (2010). PlayPhysics: An emotional game learning environment for teaching physics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6291 LNAI, 400–411. doi: 10.1007/978-3-642-15280-1_37. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-78049392263&doi=10.1007%2f978-3-642-15280-1_37&partnerID=40&md5=f66c9b0a303ee3118f88e048947c24b5

Nakamura, J., & Csikszentmihalyi, M. (2014). The concept of flow. En Flow and the foundations of positive psychology (pp. 239–263). doi: 10.1007/978-94-017-9088-8_16 [GS Search]

Okoli, C., & Schabram, K. (2010). A Guide to Conducting a Systematic Literature Review of Information Systems Research. SSRN Electronic Journal. doi: 10.2139/ssrn.1954824. Descargado 2020-10-31, de http://www.ssrn.com/abstract=1954824

Ortony, A., Clore, G. L., & Collins, A. (1988). The cognitive structure of emotions. Cambridge: Cambridge University Press. doi: 10.1017/CBO9780511571299

Padron-Rivera, G., Joaquin-Salas, C., Patoni-Nieves, J.-L., & Bravo-Perez, J.-C. (2018). Patterns in Poor Learning Engagement in Students While They Are Solving Mathematics Exercises in an Affective Tutoring System Related to Frustration. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10880 LNCS, 169–177. doi: 10.1007/978-3-319-92198-3_17. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049325808&doi=10.1007%2f978-3-319-92198-3_17&partnerID=40&md5=cb4b0719d9bd3f1825147eecb1742d15

Padrón-Rivera, G., Rebolledo-Mendez, G., Parra, P., & Huerta-Pacheco, N. (2016). Identification of action units related to affective states in a tutoring system for mathematics. Educational Technology and Society, 19(2), 77–86. [GS Search]

Pardos, Z., Baker, R., San Pedro, M., Gowda, S., & Gowda, S. (2013). Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes. En ACM International Conference Proceeding Series (pp. 117–124). doi: 10.1145/2460296.2460320. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876461451&doi=10.1145%2f2460296.2460320&partnerID=40&md5=43beba0d43e0343405e8ba3286d208a5

Pedroza-Méndez, B. E., Gonzalez-Calleros, J. M., Guerrero-García, J., & Collazos, C. A. (2019). Continuous Evaluation of the Learning Process of Algebra Through a Semi-Automated Tool. Journal of Information Technology Research, 12(3), 1–20. doi: 10.4018/JITR.2019070101. Descargado de https://app.dimensions.ai/details/publication/pub.1119999110andhttp://pdfs.semanticscholar.org/d6fc/9ad47307c792ab5bb52b1113d9c4c3e9841a.pdf

Pekrun, R. (1992). The impact of emotions on learning and achievement: Towards a theory of cognitive/motivational mediators. Applied Psychology, 41(4), 359–376. Wiley Online Library. doi: 10.1111/j.1464-0597.1992.tb00712.x [GS Search]

Pham, P., & Wang, J. (2018). Predicting learners’ emotions in mobile MOOC learning via a multimodal intelligent tutor. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10858 LNCS, 150–159. doi: 10.1007/978-3-319-91464-0_15. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048327959&doi=10.1007%2f978-3-319-91464-0_15&partnerID=40&md5=c6c14bbdb675a8f04be4685af9e3a90d

Price, M., Mudrick, N., Taub, M., & Azevedo, R. (2018). The role of negative emotions and emotion regulation on self-regulated learning with MetaTutor. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10858 LNCS, 170–179. doi: 10.1007/978-3-319-91464-0_17. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048331537&doi=10.1007%2f978-3-319-91464-0_17&partnerID=40&md5=8d9a4de04dd7bd15dd9649bb7c5a9fae

Richey, J., Andres-Bray, J., Mogessie, M., Scruggs, R., Andres, J., Star, J., ..., & McLaren, B. (2019). More confusion and frustration, better learning: The impact of erroneous examples. Computers and Education, 139, 173–190. doi: 10.1016/j.compedu.2019.05.012. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067959303&doi=10.1016%2fj.compedu.2019.05.012&partnerID=40&md5=c4a4db95740d52842679a428788bb11d

Rodrigo, M., Baker, R., Agapito, J., Nabos, J., Repalam, M., Reyes Jr., S., & San Pedro, M. (2012). The effects of an interactive software agent on student affective dynamics while using an intelligent tutoring system. IEEE Transactions on Affective Computing, 3(2), 224–236. doi: 10.1109/T-AFFC.2011.41. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863971844&doi=10.1109%2fT-AFFC.2011.41&partnerID=40&md5=dd0d85be4fea55af2b2a9a668d11e04f

Ruiz, S., Urretavizcaya, M., Rodríguez, C., & Fernández-Castro, I. (2020). Predicting students’ outcomes from emotional response in the classroom and attendance. Interactive Learning Environments, 28(1), 107–129. doi: 10.1080/10494820.2018.1528282. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054480130&doi=10.1080%2f10494820.2018.1528282&partnerID=40&md5=380b531ad850a3cc75a4d353585a0a9a

Russell, J. A. (1983). Pancultural aspects of the human conceptual organization of emotions. Journal of personality and social psychology, 45(6), 1281. American Psychological Association. doi: 10.1037/0022-3514.45.6.1281 [GS Search]

Strain, A., D’Mello, S., & Graesser, A. (2011). Training emotion regulation strategies during computerized learning: A method for improving learner self-regulation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6738 LNAI, 617–619. doi: 10.1007/978-3-642-21869-9_119. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959296367&doi=10.1007%2f978-3-642-21869-9_119&partnerID=40&md5=d76336e527cd6aca820206918620e813

Taub, M., Sawyer, R., Lester, J., & Azevedo, R. (2020). The Impact of Contextualized Emotions on Self-Regulated Learning and Scientific Reasoning during Learning with a Game-Based Learning Environment. International Journal of Artificial Intelligence in Education, 30(1), 97–120. doi: 10.1007/s40593-019-00191-1. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076626753&doi=10.1007%2fs40593-019-00191-1&partnerID=40&md5=0657bbcf660dcb6a427d6f4928b3f3b6

Vail, A., Wiggins, J., Grafsgaard, J., Boyer, K., Wiebe, E., & Lester, J. (2016). The affective impact of tutor questions: Predicting frustration and engagement. En Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016 (pp. 247–254). doi: 10.1145/1235. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030214847&doi=10.1145%2f1235&partnerID=40&md5=3477e19ea241677be335eeabc0d9cf6c

VanLehn, K. (2011, octubre). The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems. Educational Psychologist, 46(4), 197–221. doi: 10.1080/00461520.2011.611369. Descargado de http://www.tandfonline.com/doi/abs/10.1080/00461520.2011.611369

Wang, C.-H., & Lin, H.-C. (2018). Constructing an Affective Tutoring System for Designing Course Learning and Evaluation. Journal of Educational Computing Research, 55(8), 1111–1128. doi: 10.1177/0735633117699955. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036644928&doi=10.1177%2f0735633117699955&partnerID=40&md5=438f0b77dcb23f4979606fa290033d03

Woolf, B., Arroyo, I., Cooper, D., Burleson, W., & Muldner, K. (2010). Affective tutors: Automatic detection of and response to student emotion. Studies in Computational Intelligence, 308, 207–227. doi: 10.1007/978-3-642-14363-2_10. Descargado de https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957255983&doi=10.1007%2f978-3-642-14363-2_10&partnerID=40&md5=9630962a1a10caa67b7b9b294195db5e

Xie, H., Chu, H.-C., Hwang, G.-J., & Wang, C.-C. (2019, octubre). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 103599. doi: 10.1016/j.compedu.2019.103599. Descargado 2020-10-31, de https://linkinghub.elsevier.com/retrieve/pii/S0360131519301526

Zakharov, K., Mitrovic, A., & Johnston, L. (2007). Intelligent Tutoring Systems respecting human nature. En Proceedings of NZCSRSC 2007, the 5th New Zealand Computer Science Research Student Conference. [GS Search]




DOI: https://doi.org/10.5753/rbie.2021.29.0.928

DOI (PDF (Español (España))): https://doi.org/10.5753/rbie.2021.29.0.928

____________________________________________________________________________

Revista Brasileira de Informática na Educação (RBIE) (ISSN: 1414-5685; online: 2317-6121)
Brazilian Journal of Computers in Education (RBIE) (ISSN: 1414-5685; online: 2317-6121)