Abstract
Adapting ITSs that promote the use of metacognitive strategies can sometimes lead to intense prompting, at least initially, to the point that there is a risk of it feeling counterproductive. In this paper, we examine the impact of different prompting strategies on self-reported agent-directed emotions in an ITS that scaffolds students’ use of self-regulated learning (SRL) strategies, taking into account students’ prior knowledge. Results indicate that more intense initial prompting can indeed lead to increased frustration, and sometimes boredom even toward pedagogical agents that are perceived as competent. When considering prior knowledge, results also show that this strategy induces a significantly different higher level of confusion in low prior knowledge students when compared to high prior knowledge students. This result is consistent with the fact that higher prior knowledge students tend to be better at self-regulating their learning, and it could also indicate that some low prior knowledge students may be on their path to a better understanding of the value of SRL.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
We selected only items among the 25 questions that were relative to the subgoals each participant set at the beginning of their learning session (as participants did not have time to explore all the learning material relative to each of the 7 subgoals available).
References
Ma, W., Adesope, O.O., Nesbit, J.C., Liu, Q.: Intelligent tutoring systems and learning outcomes: a meta-analysis. J. Educ. Psychol. 106, 901–918 (2014)
Bannert, M., Mengelkamp, C.: Scaffolding hypermedia learning through metacognitive prompts. In: Azevedo, R., Aleven, V. (eds.) International Handbook of Metacognition and Learning Technologies, vol. 28, pp. 171–186. Springer, New York (2013). https://doi.org/10.1007/978-1-4419-5546-3_12
Järvelä, S., Hadwin, A.F.: New frontiers: regulating learning in CSCL. Educ. Psychol. 48, 25–39 (2013)
Bouchet, F., Harley, J.M., Azevedo, R.: Can adaptive pedagogical agents’ prompting strategies improve students’ learning and self-regulation? In: Micarelli, A., Stamper, J., Panourgia, K. (eds.) ITS 2016. LNCS, vol. 9684, pp. 368–374. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39583-8_43
Pekrun, R., Perry, R.P.: Control-value theory of achievement emotions. In: Pekrun, R., Linnenbrink-Garcia, L. (eds.) International Handbook of Emotions in Education, pp. 120–141. Routledge, Abingdon (2014)
Taub, M., Azevedo, R., Bouchet, F., Khosravifar, B.: Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments? Comput. Hum. Behav. 39, 356–367 (2014)
D’Mello, S., Lehman, B., Pekrun, R., Graesser, A.: Confusion can be beneficial for learning. Learn. Instr. 29, 153–170 (2014)
Pekrun, R., Goetz, T., Titz, W., Perry, R.P.: Academic emotions in students’ self-regulated learning and achievement: a program of qualitative and quantitative research. Educ. Psychol. 37, 91–105 (2002)
Harley, J.M., Bouchet, F., Azevedo, R.: Examining how students’ typical studying emotions relate to those experienced while studying with an ITS. In: 14th International Conference on Intelligent Tutoring Systems. Springer International Publishing, Montreal (2018)
Jarrell, A., Harley, J.M., Lajoie, S., Naismith, L.: Success, failure and emotions: examining the relationship between performance feedback and emotions in diagnostic reasoning. Educ. Technol. Res. Dev. 65, 1263–1284 (2017)
Boekaerts, M.: The on-line motivation questionnaire: a self-report instrument to assess students’ context sensitivity. In: Pintrich, P.R., Maehr, M.M. (eds.) New Directions in Measures and Methods, pp. 77–120. Emerald Group Publishing Limited, Bingley (2002)
Jacovina, M.E., Tanner Jackson, G., Snow, E.L., McNamara, D.S.: Timing game-based practice in a reading comprehension strategy tutor. In: Micarelli, A., Stamper, J., Panourgia, K. (eds.) ITS 2016. LNCS, vol. 9684, pp. 59–68. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39583-8_6
Lajoie, S.P., Naismith, L., Poitras, E., Hong, Y.-J., Cruz-Panesso, I., Ranellucci, J., Mamane, S., Wiseman, J.: Technology-rich tools to support self-regulated learning and performance in medicine. In: Azevedo, R., Aleven, V. (eds.) International Handbook of Metacognition and Learning Technologies, pp. 229–242. Springer, New York, New York, NY (2013). https://doi.org/10.1007/978-1-4419-5546-3_16
Littlewort, G., Whitehill, J., Wu, T., Fasel, I., Frank, M., Movellan, J., Bartlett, M.: The computer expression recognition toolbox (CERT). Face Gesture 2011, 298–305 (2011)
Harley, J.M., Bouchet, F., Hussain, M.S., Azevedo, R., Calvo, R.A.: A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Comput. Hum. Behav. 48, 615–625 (2015)
D’Mello, S.K., Graesser, A.: Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Model. User-Adapt. Interact. 20, 147–187 (2010)
Harley, J.M., Carter, C.K., Papaioannou, N., Bouchet, F., Landis, R.S., Azevedo, R., Karabachian, L.: Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: towards emotionally-adaptive agent-based learning environments. User Model. User-Adapt. Interact. 26, 177–219 (2016)
Bouchet, F., Kinnebrew, J.S., Biswas, G., Azevedo, R.: Identifying students’ characteristic learning behaviors in an intelligent tutoring system fostering self-regulated learning. In: Yacef, K., Zaïane, O., Hershkovitz, A., Yudelson, M., Stamper, J. (eds.) Proceedings of 5th International Conference on Educational Data Mining, Chania, Greece, pp. 65–72 (2012)
Mudrick, N., Rowe, J.P., Taub, M., Lester, J.C., Azevedo, R.: Toward affect-sensitive virtual human tutors: the influence of facial expressions on learning and emotion. In: Busso, C., Epps, J. (eds.) Proceedings of 2017 7th International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 184–189. IEEE Computer Society, Washington, DC (2017)
Harley, J.M., Taub, M., Azevedo, R., Bouchet, F.: “Let’s set up some subgoals”: understanding human-pedagogical agent collaborations and their implications for learning and prompt and feedback compliance. IEEE Trans. Learn. Technol. 11, 54–66 (2018)
Harley, J.M., Lajoie, S.P., Frasson, C., Hall, N.C.: Developing emotion-aware, advanced learning technologies: a taxonomy of approaches and features. Int. J. Artif. Intell. Educ. 27, 268–297 (2017)
Acknowledgements
Research supported by funding from NSF (DRL 1008282, DRL1431552, DRL 1660878), SSHRC, and CRC program awarded to third author.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Bouchet, F., Harley, J.M., Azevedo, R. (2018). Evaluating Adaptive Pedagogical Agents’ Prompting Strategies Effect on Students’ Emotions. In: Nkambou, R., Azevedo, R., Vassileva, J. (eds) Intelligent Tutoring Systems. ITS 2018. Lecture Notes in Computer Science(), vol 10858. Springer, Cham. https://doi.org/10.1007/978-3-319-91464-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-91464-0_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91463-3
Online ISBN: 978-3-319-91464-0
eBook Packages: Computer ScienceComputer Science (R0)