Abstract
Recent research has shown that differences in software design and content are associated with differences in how much students game the system and go off-task. In particular the design features of a tutor have found to predict substantial amounts of variance in gaming and off-task behavior. However, it is not yet understood how this influence takes place. In this paper we investigate the relationship between a student’s affective state, their tendency to engage in disengaged behavior, and the design aspects of the learning environments, towards understanding the role that affect plays in this process. To investigate this question, we integrate an existing taxonomy of the features of tutor lessons [3] with automated detectors of affect [8]. We find that confusion and frustration are significantly associated with lesson features which were found to be associated with disengaged behavior in past research. At the same time, we find that the affective state of engaged concentration is significantly associated with features associated with lower frequencies of disengaged behavior. This analysis suggests that simple re-designs of tutors along these lines may lead to both better affect and less disengaged behavior.
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
Preview
Unable to display preview. Download preview PDF.
References
Aleven, V., McLaren, B., Roll, I., Koedinger, K.R.: Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 227–239. Springer, Heidelberg (2004)
Arroyo, I., Woolf, B.P., Cooper, D., Burleson, W., Muldner, K., Christopherson, R.: Emotion Sensors Go To School. In: Proc. of the 14th International Conference on Artificial Intelligence in Education (2009)
Baker, R.S.J.d.: Differences Between Intelligent Tutor Lessons, and the Choice to Go Off-Task. In: Proc. of the 2nd Int’l. Conference on Educational Data Mining, pp. 11–20 (2009)
Baker, R.S.J.d.: Modeling and understanding students’ off-task behavior in intelligent tutoring systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1059–1068 (2007)
Baker, R.S.J.d., de Carvalho, A.M.J.A.: Labeling Student Behavior Faster and More Precisely with Text Replays. In: Proceedings of the 1st International Conference on Educational Data Mining, pp. 38–47 (2008)
Baker, R.S.J.d., de Carvalho, A.M.J.A., Raspat, J., Aleven, V., Corbett, A.T., Koedinger, K.R.: Educational Software Features that Encourage and Discourage “Gaming the System”. In: Proc. of the 14th Int’l. Conf. on Artificial Intelligence in Education, pp. 475–482 (2009)
Baker, R.S.J.d., D’Mello, S.K., Rodrigo, M.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. International Journal of Human-Computer Studies 68(4), 223–241 (2010)
Baker, R.S.J.d., Gowda, S.M., Wixon, M., Kalka, J., Wagner, A.Z., Salvi, A., Aleven, V., Kusbit, G., Ocumpaugh, J., Rossi, L.: Sensor-free automated detection of affect in a Cognitive Tutor for Algebra. In: Proceedings of the 5th International Conference on Educational Data Mining, pp. 126–133 (2012)
Baker, R.S.J.d., Moore, G.R., Wagner, A.Z., Kalka, J., Salvi, A., Karabinos, M., Ashe, C.A., Yaron, D.: The Dynamics Between Student Affect and Behavior Occuring Outside of Educational Software. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011, Part I. LNCS, vol. 6974, pp. 14–24. Springer, Heidelberg (2011)
Baker, R.S.J.d., Walonoski, J., Heffernan, N., Roll, I., Corbett, A., Koedinger, K.: Why Students Engage in “Gaming the System” Behavior in Interactive Learning Environments. Journal of Interactive Learning Research 19(2), 185–224 (2008)
Beal, C.R., Qu, L., Lee, H.: Mathematics motivation and achievement as predictors of high school students’ guessing and help-seeking with instructional software. Journal of Computer Assisted Learning 24(6), 507–514 (2008)
Beck, J.E., Chang, K.-M., Mostow, J., Corbett, A.: Does Help Help? Introducing the Bayesian Evaluation and Assessment Methodology. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 383–394. Springer, Heidelberg (2008)
Beck, J.: Engagement tracing: using response times to model student disengagement. In: Proc. of 12th Int’l Conference on Artificial Intelligence in Education, pp. 88–95 (2005)
Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 57, 289–300 (1995)
Cocea, M., Hershkovitz, A., Baker, R.S.J.d.: The Impact of Off-task and Gaming Behaviors on Learning: Immediate or Aggregate? In: Proceedings of the 14th International Conference on Artificial Intelligence in Education, pp. 507–514 (2009)
Craig, S.D., Graesser, A.C., Sullins, J., Gholson, B.: Affect and learning: an exploratory look into the role of affect in learning. J. of Educational Media 29, 241–250 (2004)
Csikszentmihalyi, M., Schneider, B.: Becoming Adult. Basic Books, New York (2001)
D’Mello, S.K., Graesser, A.C.: Multimodal semiautomated affect detection from conversational cues, gross body language, and facial features. User Modeling and User-adapted Interaction 20(2), 147–187 (2010)
D’Mello, S.K., Taylor, R., Graesser, A.C.: Monitoring Affective Trajectories during Complex Learning. In: Proc. of the 29th Annual Conf. of the Cognitive Science Society, pp. 203–208 (2007)
Harp, S.F., Mayer, R.E.: How seductive details do their damage: a theory of cognitive interest in science learning. Journal of Educational Psychology 90, 414–434 (1998)
Hastings, P., Arnott-Hill, E., Allbritton, D.: Squeezing out gaming behavior in a dialog-based ITS. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part I. LNCS, vol. 6094, pp. 204–213. Springer, Heidelberg (2010)
Hershkovitz, A., Baker, R.S.J.d., Gobert, J., Nakama, A.: A Data-driven Path Model of Student Attributes, Affect, and Engagement in a Computer-based Science Inquiry Microworld. In: Proceedings of the International Conference on the Learning Sciences (2012)
Hidi, S., Anderson, V.: Situational interest and its impact on reading and expository writing. In: Renninger, K.A., Hidi, S., Krapp, A. (eds.) The Role of Interest in Learning and Development, pp. 215–238. Erlbaum, Hillsdale (1992)
Koedinger, K., Corbett, A.: Cognitive Tutors: Technology bringing learning science to the classroom. In: Sawyer, K. (ed.) The Cambridge Handbook of the Learning Sciences, pp. 61–78. Cambridge University Press (2006)
Krapp, A.: Structural and dynamic aspects of interest development: theoretical considerations from an ontogenetic perspective. Learning and Instruction 12(4), 383–409 (2002)
Lee, D.M.C., Rodrigo, M.M.T., Baker, R.S.J.d., Sugay, J.O., Coronel, A.: Exploring the Relationship between Novice Programmer Confusion and Achievement. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011, Part I. LNCS, vol. 6974, pp. 175–184. Springer, Heidelberg (2011)
Litman, D.J., Forbes-Riley, K.: Recognizing Student Emotions and Attitudes on the Basis of Utterances in Spoken Tutoring Dialogues with both Human and Computer Tutors. Speech Communication 48(5), 559–590 (2006)
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: Yale: rapid prototyping for complex data mining tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 935–940 (2006)
Murray, R.C., VanLehn, K.: Effects of dissuading unnecessary help requests while providing proactive help. In: Proc. of the Int’l Conf. on Artificial Intelligence in Education (2005)
Pardos, Z., Baker, R.S.J.d., San Pedro, M.O.Z., Gowda, S.M., Gowda, S.: Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes. In: To Appear in Proceedings of the 3rd International Conference on Learning Analytics and Knowledge (in press)
Perneger, T.V.: What’s wrong with Bonferroni adjustments. British Medical Journal 316, 1236–1238 (1998)
Sabourin, J., Rowe, J.P., Mott, B.W., Lester, J.C.: When Off-Task is On-Task: The Affective Role of Off-Task Behavior in Narrative-Centered Learning Environments. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds.) AIED 2011. LNCS, vol. 6738, pp. 534–536. Springer, Heidelberg (2011)
Storey, J.D., Taylor, J.E., Siegmund, D.: Strong control, conservative point estimation, and simultaneous conservative consistency of false discovery rates: A unified approach. Journal of the Royal Statistical Society, Series B 66(1), 187–205 (2004)
Van Duyne, D.K., Landay, J.A., Hong, J.I.: The design of sites patterns for creating winning web sites, Upper Saddle River, NY (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Doddannara, L.S., Gowda, S.M., Baker, R.S.J.d., Gowda, S.M., de Carvalho, A.M.J.B. (2013). Exploring the Relationships between Design, Students’ Affective States, and Disengaged Behaviors within an ITS. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-39112-5_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39111-8
Online ISBN: 978-3-642-39112-5
eBook Packages: Computer ScienceComputer Science (R0)