Skip to main content
SpringerLink
Log in

Meta-Affective Behaviour within an Intelligent Tutoring System for Mathematics

  • Article
  • Published:
International Journal of Artificial Intelligence in Education Aims and scope Submit manuscript

Abstract

Many previous studies have highlighted the influence of learners’ affective states on learning with tutoring systems. However, the associations between learning and learners’ meta-affective capability are still unclear. The goal of this paper is to analyse meta-affective capability and its influence on learning outcomes as well as the dynamics of affect over time. Two criteria, awareness and self-regulation, were employed to define meta-affective capability. An exploratory study (n = 54) was conducted in which students at the secondary level were asked to interact with an intelligent tutoring system for mathematics and to self-report their affect during their interactions with the system. Pre-post learning outcomes were also measured. A post-hoc comparison of learning gains was made between more meta-affectively capable and less meta-affectively capable students. The results provide some empirical evidence to support the hypothesis that having meta-affective capability is positively associated with learning. Students not demonstrating meta-affective capability seemed to transition frequently from boredom to frustration (p = .0284) and from concentration to neutral (p = 0.0017). However, only a small percentage of the sample were classified as having meta-affective capability, indicating that it is important to scaffold students who are not meta-affectively capable.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. http://www.enlace.sep.gob.mx/

References

  • Ahmed, W., van der Werf, G., Kuyper, H., & Minnaert, A. (2013). Emotions, self-regulated learning, and achievement in mathematics: A growth curve analysis. Journal of Educational Psychology, 105(1), 150–161. https://doi.org/10.1037/a0030160.

    Article  Google Scholar 

  • Andres, J. M. A. L., Paquette, L., Ocumpaugh, J., Jiang, Y., Baker, R. S., Karumbaiah, S., et al. (2019). Affect sequences and learning in Betty’s brain. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (pp. 383–390). ACM International Conference Proceeding Series. https://doi.org/10.1145/3303772.3303807.

  • Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working memory, math anxiety, and performance. Journal of Experimental Psychology: General, 130(2), 224–237. https://doi.org/10.1037/0096-3445.130.2.224.

    Article  Google Scholar 

  • Baker, R. S., Corbett, A. T., Koedinger, K. R., & Schneider, M. P. (2003). A formative evaluation of a tutor for scatterplot generation: Evidence on difficulty factors. In U. Hoppe, F. Verdejo, & J. Kay (Eds.), Artificial Intelligence in Education: Shaping the Future of Learning through Intelligent Technologies, Proceedings of AI-ED 2003 (pp. 107–114). IOS Press: Amsterdam.

    Google Scholar 

  • Baker, R. S., Corbett, A. T., Koedinger, K. R., & Wagner, A. Z. (2004). Off-task behavior in the cognitive tutor classroom: When students game the system. In Proceedings of the ACM Conference on Human Factors in Computing Systems (pp. 383–390). ACM Press. https://doi.org/10.1145/985692.985741.

  • Baker, R. S., D’Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive-affective states during interactions with three different computer-based learning environments. International Journal of Human Computer Studies, 68(4), 223–241. https://doi.org/10.1016/j.ijhcs.2009.12.003.

    Article  Google Scholar 

  • Ben-Eliyahu, A., & Linnenbrink-Garcia, L. (2015). Integrating the regulation of affect, behavior, and cognition into self-regulated learning paradigms among secondary and post-secondary students. Metacognition and Learning, 10(1), 15–42. https://doi.org/10.1007/s11409-014-9129-8.

    Article  Google Scholar 

  • Botelho, A. F., Baker, R. S., Ocumpaugh, J., & Heffernan, N. T. (2018). Studying affect dynamics and chronometry using sensor-free detectors. In Proceedings of the 11th International Conference on Educational Data Mining. EDM 2018.

  • Brown, A. L. (1978). Knowing when, where, and how to remember: A problem of metacognition. Advances in Instructional Psychology. Volume, 1, 225–253.

    Google Scholar 

  • Conati, C., & Gutica, M. (2016). Interaction with an Edu-game: A detailed analysis of student emotions and judges’ perceptions. International Journal of Artificial Intelligence in Education, 26(4), 975–1010. https://doi.org/10.1007/s40593-015-0081-9.

    Article  Google Scholar 

  • Craig, S., Graesser, A., Sullins, J., & Gholson, B. (2004). Affect and learning: An exploratory look into the role of affect in learning with AutoTutor. Journal of Educational Media, 29(3), 241–250. https://doi.org/10.1080/1358165042000283101.

    Article  Google Scholar 

  • D’Mello, S., & Graesser, A. (2011). The half-life of cognitive-affective states during complex learning. Cognition & Emotion, 25(7), 1299–1308. https://doi.org/10.1080/02699931.2011.613668.

    Article  Google Scholar 

  • D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157. https://doi.org/10.1016/j.learninstruc.2011.10.001.

    Article  Google Scholar 

  • D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170. https://doi.org/10.1016/j.learninstruc.2012.05.003.

    Article  Google Scholar 

  • D’Mello, S. K., Strain, A. C., Olney, A., & Graesser, A. (2013). In R. Azevedo & V. Aleven (Eds.), Affect, meta-affect, and affect regulation during complex learning BT - international handbook of metacognition and learning technologies (pp. 669–681). New York, NY: Springer New York. https://doi.org/10.1007/978-1-4419-5546-3_44.

    Chapter  Google Scholar 

  • Debellis, V. A., & Goldin, G. A. (2006). Affect and meta-affect in mathematical problem solving: A representational perspective. Educational Studies in Mathematics, 63(2), 131–147. https://doi.org/10.1007/s10649-006-9026-4.

    Article  Google Scholar 

  • DeFalco, J. A., Rowe, J. P., Paquette, L., Georgoulas-Sherry, V., Brawner, K., Mott, B. W., Baker, R. S., & Lester, J. C. (2018). Detecting and addressing frustration in a serious game for military training. International Journal of Artificial Intelligence in Education, 28(2), 152–193. https://doi.org/10.1007/s40593-017-0152-1.

    Article  Google Scholar 

  • Dennis, M., Masthoff, J., & Mellish, C. (2016). Adapting Progress feedback and emotional support to learner personality. International Journal of Artificial Intelligence in Education, 26(3), 877–931. https://doi.org/10.1007/s40593-015-0059-7.

    Article  Google Scholar 

  • Efklides, A., Kourkoulou, A., Mitsiou, F., & Ziliaskopoulou, D. (2006). Metacognitive knowledge of effort, personality factors, and mood state: Their relationships with effort-related metacognitive experiences. Metacognition and Learning, 1(1), 33–49. https://doi.org/10.1007/s11409-006-6581-0.

    Article  Google Scholar 

  • Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906.

    Article  Google Scholar 

  • Friesen, A. P., Lane, A. M., Devonport, T. J., Sellars, C. N., Stanley, D. N., & Beedie, C. J. (2013). Emotion in sport: Considering interpersonal regulation strategies. International Review of Sport and Exercise Psychology, 6, 139–154. https://doi.org/10.1080/1750984X.2012.742921.

    Article  Google Scholar 

  • Gabadinho, A., Ritschard, G., Müller, N. S., & Studer, M. (2011). Analyzing and visualizing state sequences in R with TraMineR. Journal of Statistical Software, 40(4), 1–37. https://doi.org/10.18637/jss.v040.i04.

    Article  Google Scholar 

  • Goldin, G. A. (2000). Affective pathways and representation in mathematical problem solving. Mathematical Thinking and Learning, 2(3), 209–219. https://doi.org/10.1207/S15327833MTL0203_3.

    Article  Google Scholar 

  • Goldin, G. A. (2004). Problem solving heuristics, affect, and discrete mathematics. ZDM, 36(2), 56–60. https://doi.org/10.1007/BF02655759.

    Article  Google Scholar 

  • Gross, J. J. (2008). Emotion regulation. In Handbook of emotions (3rd ed., pp. 497–512). The Guilford Press.

  • Hannula, M. S. (2001). In M. Ahtee, O. Björkqvist, E. Pehkonen, & V. Vatanen (Eds.), The metalevel of cognition-emotion interaction. University of Jyväskylä, Institute for Educational Research.

  • Harley, J. M., Bouchet, F., & Azevedo, R. (2013). Aligning and comparing data on emotions experienced during learning with MetaTutor. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial Intelligence in Education. AIED 2013. Lecture notes in computer science (vol. 7926). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-39112-5_7.

    Chapter  Google Scholar 

  • Harley, J. M., Lajoie, S. P., Frasson, C., & Hall, N. C. (2017). Developing emotion-aware, advanced learning technologies: A taxonomy of approaches and features. International Journal of Artificial Intelligence in Education, 27(2), 268–297. https://doi.org/10.1007/s40593-016-0126-8.

    Article  Google Scholar 

  • Karumbaiah, S., Baker, R. S., & Ocumpaugh, J. (2019). The case of self-transitions in affective dynamics. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (vol. 11625 LNAI, pp. 172–181). https://doi.org/10.1007/978-3-030-23204-7_15.

  • Kim, C., Park, S. W., & Cozart, J. (2014). Affective and motivational factors of learning in online mathematics courses. British Journal of Educational Technology, 45(1), 171–185. https://doi.org/10.1111/j.1467-8535.2012.01382.x.

    Article  Google Scholar 

  • Koedinger, K. R., & Corbett, A. (2006). Cognitive tutors: Technology bringing learning sciences to the classroom. In The Cambridge handbook of: The learning sciences. (pp. 61–77): Cambridge University press.

  • Lane, A. M., Beedie, C. J., Devonport, T. J., & Stanley, D. M. (2011). Instrumental emotion regulation in sport: Relationships between beliefs about emotion and emotion regulation strategies used by athletes. Scandinavian Journal of Medicine and Science in Sports, 21(6), e445–e451. https://doi.org/10.1111/j.1600-0838.2011.01364.x.

    Article  Google Scholar 

  • Lee, D. M. C., Rodrigo M. M. T., Baker R. S. J., Sugay J. O., & Coronel A. (2011). Exploring the relationship between novice programmer confusion and achievement. In: D’Mello S., Graesser A., Schuller B., Martin JC. (Eds.), Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24600-5_21.

  • 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. https://doi.org/10.1016/j.iheduc.2012.01.002.

    Article  Google Scholar 

  • Lehman, B., D’Mello, S., Strain, A., Mills, C., Gross, M., Dobbins, A., et al. (2013). Inducing and tracking confusion with contradictions during complex learning. International Journal of Artificial Intelligence in Education, 22, 85–105. https://doi.org/10.3233/JAI-130025.

    Article  Google Scholar 

  • Liu, Z., Baker, R. S. J. D., Pataranutaporn, V., & Ocumpaugh, J. (2013). Sequences of frustration and confusion, and learning. In proceedings of the 6th international conference on educational data mining, EDM 2013.

  • Moscucci, M. (2009). Why is there not enough fuss about affect and Meta-affect among mathematics teachers? In V. Durand-Guerrier, S. Soury-Lavergne, & F. Arzarello (Eds.), Proceedings of the sixth conference of European research in mathematics education (pp. 1811–1820). France: Lyon http://ife.ens-lyon.fr/editions/editions-electroniques/cerme6/.

    Google Scholar 

  • Mrazek, A. J., Ihm, E. D., Molden, D. C., Mrazek, M. D., Zedelius, C. M., & Schooler, J. W. (2018). Expanding minds: Growth mindsets of self-regulation and the influences on effort and perseverance. Journal of Experimental Social Psychology, 79, 164–180. https://doi.org/10.1016/j.jesp.2018.07.003.

    Article  Google Scholar 

  • Namkung, J. M., Peng, P., & Lin, X. (2019). The relation between mathematics anxiety and mathematics performance among school-aged students: A Metaanalysis. Review of Educational Research, 89(3), 459–496. https://doi.org/10.3102/0034654319843494.

    Article  Google Scholar 

  • Ogan, A., Walker, E., Baker, R. S. J. D., Rebolledo-Mendez, G., Catro, M. J., Laurentino, T., & de Cavello, A. (2012). Collaboration in cognitive tutor use in latin America: field study and design recommendations. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1381–1390). https://doi.org/10.1145/2207676.2208597.

    Chapter  Google Scholar 

  • Pardos, Z. A., Baker, R. S. J. D., San Pedro, M., Gowda, S. M., & Gowda, S. M. (2014). Affective states and state tests: Investigating how affect and engagement during the school year predict end-of-year learning outcomes. Journal of Learning Analytics, 1(1 SE), 107–128. https://doi.org/10.18608/jla.2014.11.6.

    Article  Google Scholar 

  • Richey, J. E., Andres-Bray, J. M. L., Mogessie, M., Scruggs, R., Andres, J. M. A. L., Star, J. R., Baker, R. S., & McLaren, B. M. (2019). More confusion and frustration, better learning: The impact of erroneous examples. Computers & Education, 139, 173–190. https://doi.org/10.1016/j.compedu.2019.05.012.

    Article  Google Scholar 

  • Rodrigo, M. M. T., Baker, R. S. J. D., Agapito, J., Nabos, J., Repalam, M. C., Reyes, S. S., & Pedro, M. O. C. Z. S. (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. https://doi.org/10.1109/T-AFFC.2011.41.

    Article  Google Scholar 

  • Schwarz, N. (2012). Feelings-as-information theory. In P. A. Van Lange, A. W. Kruglanski, & E. T. Higgins (Eds.), Handbook of theories of social psychology (vol. 1, pp. 289–308). SAGE Publications Ltd. https://doi.org/10.4135/9781446249215.n15.

  • Shute, V. J., D’Mello, S., Baker, R., Cho, K., Bosch, N., Ocumpaugh, J., et al. (2015). Modeling how incoming knowledge, persistence, affective states, and ingame progress influence student learning from an educational game. Computers & Education, 86, 224–235. https://doi.org/10.1016/j.compedu.2015.08.001.

    Article  Google Scholar 

  • Spann, C. A., Shute, V. J., Rahimi, S., & D’Mello, S. K. (2019). The productive role of cognitive reappraisal in regulating affect during game-based learning. Computers in Human Behavior, 100, 358–369. https://doi.org/10.1016/j.chb.2019.03.002.

    Article  Google Scholar 

  • Sutter-Brandenberger, C. C., Hagenauer, G., & Hascher, T. (2018). Students’ self-determined motivation and negative emotions in mathematics in lower secondary education—Investigating reciprocal relations. Contemporary Educational Psychology, 55, 166–175. https://doi.org/10.1016/j.cedpsych.2018.10.002.

    Article  Google Scholar 

  • Tzohar-Rozen, M., & Kramarski, B. (2017). Metacognition and meta-affect in young students: Does it make a difference in mathematical problem solving? Teachers College Record, 119(13).

  • Wagstaff, C. R. D. (2014). Emotion regulation and sport performance. Journal of Sport & Exercise Psychology, 36(4), 401–412. https://doi.org/10.1123/jsep.2013-0257.

    Article  Google Scholar 

Download references

Acknowledgements

The first author acknowledges the technical support of Writing Lab, Institute for the Future of Education, Tecnologico de Monterrey, Mexico in the production of this work. The second author gratefully acknowledges financial support from the Mexican Council of Science and Technology (CONACYT). The first author thanks Dr. Mario Martinez for suggesting the TraMineR library applied in this paper.

Author information

Authors and Affiliations

  1. Tecnológico de Monterrey, Writing Lab, Institute for the Future of Education, 64849, Monterrey, Nuevo León, Mexico

    Genaro Rebolledo-Mendez

  2. Consejo Nacional de Ciencia y Tecnología - Ciencia Forense, Facultad de Medicina, Universidad Nacional Autónoma de México, 04510, Ciudad de México, Mexico

    N. Sofia Huerta-Pacheco

  3. Graduate School of Education, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Ryan S. Baker

  4. Human-Centred Technology Research Group, Department of Informatics, University of Sussex, Brighton, BN1 9QJ, UK

    Benedict du Boulay

Authors
  1. Genaro Rebolledo-Mendez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Sofia Huerta-Pacheco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ryan S. Baker
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Benedict du Boulay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Genaro Rebolledo-Mendez.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rebolledo-Mendez, G., Huerta-Pacheco, N.S., Baker, R.S. et al. Meta-Affective Behaviour within an Intelligent Tutoring System for Mathematics. Int J Artif Intell Educ 32, 174–195 (2022). https://doi.org/10.1007/s40593-021-00247-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40593-021-00247-1

Keywords

Search

Navigation