Abstract
This paper investigates the effect of affect-aware support on learning tasks that differ in their cognitive demands. We conducted a study with the iTalk2learn platform where students are undertaking fractions tasks of varying difficulty and assigned in one of two groups; one group used the iTalk2learn platform that included the affect-aware support, whereas in the other group the affect-aware support was switched off and support was provided based on students’ performance only. We investigated the hypothesis that affect-aware support has a more pronounced effect when the cognitive demands of the tasks are higher. The results suggest that students that undertook the more challenging tasks were significantly more in-flow and less confused in the group where affect-aware support was provided than students who were supported based on their performance only.
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References
Baker, R.S.J.d., D’Mello, S.K., Rodrigo, M.T., Graesser, A.C.: 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. Int. J. Hum. Comput. Stud. 68(4), 223–241 (2010)
D’Mello, S.K., Lehman, B., Pekrun, R., Graesser, A.C.: Confusion can be beneficial for learning. Learn. Instruc. 29(1), 153–170 (2014)
Grawemeyer, B., Mavrikis, M., Holmes, W., Gutiérrez-Santos, S., Wiedmann, M., Rummel, N.: Affective learning: improving engagement and enhancing learning with affect-aware feedback. User Model. User Adap. Inter. 27, 119–158 (2017). Special Issue on Impact of Learner Modeling
Kort, B., Reilly, R., Picard, R.: An affective model of the interplay between emotions and learning. In: IEEE International Conference on Advanced Learning Technologies, pp. 43–46 (2001)
Ocumpaugh, J., Baker, R., Rodrigo, M.: Baker-Rodrigo observation method protocol (BROMP) 1.0. training manual version 1.0. Technical report. EdLab, New York. Ateneo Laboratory for the Learning Sciences, Manila (2012)
Porayska-Pomsta, K., Mavrikis, M., Pain, H.: Diagnosing and acting on student affect: the tutors perspective. User Model. User Adap. Inter. 18(1), 125–173 (2008)
Acknowledgments
This research was funded by the European Union in the Seventh Framework Programme (FP7/2007-2013) in the iTalk-2Learn project (318051).
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Grawemeyer, B., Mavrikis, M., Mazziotti, C., van Leeuwen, A., Rummel, N. (2018). The Impact of Affect-Aware Support on Learning Tasks that Differ in Their Cognitive Demands. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_22
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DOI: https://doi.org/10.1007/978-3-319-93846-2_22
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