Affect Dynamics and Behavioral Patterns in Intelligent Learning Environments
Abstract
This research examines the interplay between emotions and learning behaviors in Intelligent Learning Environments (ILEs) using data from Brazilian students. We analyze associations between emotions—including confusion, frustration, boredom, and engagement—and learning behaviors, focusing on the influence of gender and the duration of these emotions and behaviors. Data from 30 students in a math step-based tutoring system were annotated by human coders, with transition probabilities assessed using the L metric. Key findings indicate that engagement and confusion correlate with active task involvement, while boredom and frustration are linked to task withdrawal. Additionally, gender-specific dynamics between emotions and behaviors were observed. These results highlight the potential to use the observed relationships between emotions and behaviors to enhance personalized learning experiences and inform the design of adaptive learning environments.
Keywords:
Affect Dynamics, Behavioral Patterns, Emotions, Learning, Intelligent Learning Environments
References
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Baker, R. S., D’Mello, S. K., Rodrigo, M. M. T., and Graesser, A. C. (2010). Better to be frustrated than bored: ... Int. J. of HCS, 68(4):223–241.
Baker, R. S., Moore, G. R., Wagner, A. Z., Kalka, J., Salvi, A., Karabinos, M., Ashe, C. A., and Yaron, D. (2011). The dynamics between student affect and behavior occurring outside of educational software. In ACII, pages 14–24. Springer.
Baker, R. S., Rodrigo, M. M. T., and Xolocotzin, U. E. (2007). The dynamics of affective transitions in simulation problem-solving environments. In ACII, pages 666–677.
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D’Mello, S. and Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2):145–157.
Fancsali, S. (2014). Causal discovery with models: behavior, affect, and learning in cognitive tutor algebra. In Educational Data Mining 2014. EDMS.
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Frenzel, A. C., Pekrun, R., and Goetz, T. (2007). Girls and mathematics—a “hopeless” issue? a control-value approach to gender differences in emotions towards mathematics. European Journal of Psychology of Education, 22(4):497–514.
Graesser, A. and D’Mello, S. K. (2011). Theoretical perspectives on affect and deep learning. In New Perspectives on Affect and Learning Tec., pages 11–21. Springer, NY.
Graesser, A. C., D’Mello, S. K., and Strain, A. C. (2014). Emotions in advanced learning technologies. In International handbook of emotions in education, pages 483–503. Routledge, New York.
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Jaques, P. A., Seffrin, H., Rubi, G., Morais, F., Ghilardi, C., Bittencourt, I. I., and Isotani, S. (2013). Rule-based expert systems to support step-by-step guidance in algebraic problem solving: The case of the tutor PAT2math. Expert Systems with Applications, 40(14):5456–5465.
Karumbaiah, S., Baker, R. B., Ocumpaugh, J., and Andres, A. (2021). A re-analysis and synthesis of data on affect dynamics in learning. IEEE TAC.
Kostyuk, V., Almeda, M. V., and Baker, R. S. (2018). Correlating affect and behavior in reasoning mind with state test achievement. In LAK, pages 26–30.
Matayoshi, J., Karumbaiah, S., et al. (2020). Adjusting the l statistic when self-transitions are excluded in affect dynamics. J. of EDM, 12(4):1–23.
Morais, F. and Jaques, P. A. (2023). The dynamics of brazilian students’ emotions in digital learning systems. IJAIED.
Morais, F., Kautzmann, T. R., Bittencourt, I. I., and Jaques, P. A. (2019). EmAP-ML: A protocol of Emotions and behaviors Annotation for Machine Learning labels. In EC-TEL, pages 495–509, Netherlands. Springer.
Pardos, Z. A., Baker, R. S., San Pedro, M. O., Gowda, S. M., and Gowda, S. M. (2014). Affective states and state tests: Investigating how affect and engagement during the school year predict end-of-year learning outcomes. JLA, 1(1):107–128.
Pekrun, R. (2014). Emotions and learning. Technical Report Educational Practices Series24, UNESCO International Bureau of Education.
Perkins, H. V. (1965). Classroom behavior and underachievement. American Educational Research Journal, 2(1):1–12.
Rodrigo, M. M. T., Baker, R. S., Jadud, M. C., Amarra, A. C. M., Dy, T., Espejo-Lahoz, M. B. V., Lim, S. A. L., Pascua, S. A., Sugay, J. O., and Tabanao, E. S. (2009). Affective and behavioral predictors of novice programmer achievement. In ACM SIGCSE Conf. on Innovation and technology in CSE, pages 156–160.
Sabourin, J., Rowe, J. P., Mott, B. W., and Lester, J. C. (2011). When off-task is on-task: The affective role of off-task behavior in narrative-centered learning environments. In AIED, pages 534–536. Springer.
Zeidner, M. (1998). Test Anxiety: The State of the Art. Springer, New York.
Baker, R. S., D’Mello, S. K., Rodrigo, M. M. T., and Graesser, A. C. (2010). Better to be frustrated than bored: ... Int. J. of HCS, 68(4):223–241.
Baker, R. S., Moore, G. R., Wagner, A. Z., Kalka, J., Salvi, A., Karabinos, M., Ashe, C. A., and Yaron, D. (2011). The dynamics between student affect and behavior occurring outside of educational software. In ACII, pages 14–24. Springer.
Baker, R. S., Rodrigo, M. M. T., and Xolocotzin, U. E. (2007). The dynamics of affective transitions in simulation problem-solving environments. In ACII, pages 666–677.
D’Mello, S., Graesser, A., and Taylor, R. S. (2007). Monitoring affective trajectories during complex learning. In Annu. Meet. Cogn. Sci. Soc., volume 29.
D’Mello, S. K. (2020). Big data in the science of learning. In Big data in psychological research, pages 203–225. APA.
D’Mello, S. and Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2):145–157.
Fancsali, S. (2014). Causal discovery with models: behavior, affect, and learning in cognitive tutor algebra. In Educational Data Mining 2014. EDMS.
Fredrickson, B. L. (1998). What good are positive emotions? Review of General Psychology, 2(3):300–319.
Frenzel, A. C., Pekrun, R., and Goetz, T. (2007). Girls and mathematics—a “hopeless” issue? a control-value approach to gender differences in emotions towards mathematics. European Journal of Psychology of Education, 22(4):497–514.
Graesser, A. and D’Mello, S. K. (2011). Theoretical perspectives on affect and deep learning. In New Perspectives on Affect and Learning Tec., pages 11–21. Springer, NY.
Graesser, A. C., D’Mello, S. K., and Strain, A. C. (2014). Emotions in advanced learning technologies. In International handbook of emotions in education, pages 483–503. Routledge, New York.
Hembree, R. (1988). Correlates, causes, effects, and treatment of test anxiety. Review of Educational Research, 58(1):47–77.
Jaques, P. A., Seffrin, H., Rubi, G., Morais, F., Ghilardi, C., Bittencourt, I. I., and Isotani, S. (2013). Rule-based expert systems to support step-by-step guidance in algebraic problem solving: The case of the tutor PAT2math. Expert Systems with Applications, 40(14):5456–5465.
Karumbaiah, S., Baker, R. B., Ocumpaugh, J., and Andres, A. (2021). A re-analysis and synthesis of data on affect dynamics in learning. IEEE TAC.
Kostyuk, V., Almeda, M. V., and Baker, R. S. (2018). Correlating affect and behavior in reasoning mind with state test achievement. In LAK, pages 26–30.
Matayoshi, J., Karumbaiah, S., et al. (2020). Adjusting the l statistic when self-transitions are excluded in affect dynamics. J. of EDM, 12(4):1–23.
Morais, F. and Jaques, P. A. (2023). The dynamics of brazilian students’ emotions in digital learning systems. IJAIED.
Morais, F., Kautzmann, T. R., Bittencourt, I. I., and Jaques, P. A. (2019). EmAP-ML: A protocol of Emotions and behaviors Annotation for Machine Learning labels. In EC-TEL, pages 495–509, Netherlands. Springer.
Pardos, Z. A., Baker, R. S., San Pedro, M. O., Gowda, S. M., and Gowda, S. M. (2014). Affective states and state tests: Investigating how affect and engagement during the school year predict end-of-year learning outcomes. JLA, 1(1):107–128.
Pekrun, R. (2014). Emotions and learning. Technical Report Educational Practices Series24, UNESCO International Bureau of Education.
Perkins, H. V. (1965). Classroom behavior and underachievement. American Educational Research Journal, 2(1):1–12.
Rodrigo, M. M. T., Baker, R. S., Jadud, M. C., Amarra, A. C. M., Dy, T., Espejo-Lahoz, M. B. V., Lim, S. A. L., Pascua, S. A., Sugay, J. O., and Tabanao, E. S. (2009). Affective and behavioral predictors of novice programmer achievement. In ACM SIGCSE Conf. on Innovation and technology in CSE, pages 156–160.
Sabourin, J., Rowe, J. P., Mott, B. W., and Lester, J. C. (2011). When off-task is on-task: The affective role of off-task behavior in narrative-centered learning environments. In AIED, pages 534–536. Springer.
Zeidner, M. (1998). Test Anxiety: The State of the Art. Springer, New York.
Published
2024-11-04
How to Cite
JAQUES, Patricia A.; MORAIS, Felipe de; KAUTZMANN, Tiago R..
Affect Dynamics and Behavioral Patterns in Intelligent Learning Environments. In: BRAZILIAN SYMPOSIUM ON COMPUTERS IN EDUCATION (SBIE), 35. , 2024, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 1452-1465.
DOI: https://doi.org/10.5753/sbie.2024.241613.
