Impact of Argumentation Scripts on Socio-Cognitive Conflict Induction in Intelligent Tutoring System Environments
Corresponding Author: firstname.lastname@example.org
Socio-cognitive conflict is an internal contradictory state that the individual perceives in the social interaction. Previously, researchers have successfully proved that the socio-cognitive conflict had an active effect for learning by presenting contradictory information scrips in group learning environments, such as small group discussion or computer-mediated collaborative problem solving. However, the impact of argumentation scripts on group learning performance has not been investigated to the same degree, although the argumentation scripts foster critical and reflective activities in group learning by restricting the set of communicative possibilities. In this chapter, we first examine the internal mechanism of socio-cognitive conflict on learning gains, and then explore the relationship between socio-cognitive conflict and argumentation scripts in college students within a simulated group learning session in which the human learner and two virtual peer agents work. Findings show that confusion partially mediated the relationship between socio-cognitive conflict and learning gains, and argumentation scripts affect the impact of socio-cognitive conflict on learning gains.
Keywords: socio-cognitive conflict, argumentation scripts, intelligent tutoring systems
APA citation information
Long, Z., Gao, H., Dowell, N. & Hu, X., (2018). Impact of argumentation scripts on socio-cognitive conflict induction in intelligent tutoring system environments. In S. D. Craig (Ed.). Tutoring and Intelligent Tutoring Systems (pp. 299-320). New York, NY: Nova Science Publishers.
Aleven, V., & Koedinger, K. R. (2002). An effective meta-cognitive strategy: Learning by doing and explaining with a computer-based cognitive tutor. Cognition Science, 26, 147-17.
Baron, R. M., & Kenny, D. A. (1986). The moderator mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.
Berkowitz, M. W., & Gibbs, J. C. (1983). Measuring the developmental features of moral discussion. Merrill Palmer Quarterly, 29(4), 399-410.
Biswas, G., Jeong, H., Kinnebrew, J., Sulcer, B., & Roscoe, R. (2010). Measuring self-regulated learning skills through social interactions in a teachable agent environment. Research and Practice in Technology-Enhanced Learning, 5, 123-152.
Bjork, E. L., & Bjork, R. A. (2011). Making things hard on yourself, but in a good way: creating desirable difficulties to enhance learning. In M. A. Gernsbacher, R. W. Pew, L. M. Hough, & J. R. Pomerantz (Eds.), Psychology and the real world: Essays illustrating fundamental contributions to society (pp. 56-64). New York: Worth Publishers.
Brown, J., & VanLehn, K. (1980). Repair theory: a generative theory of bugs in procedural skills. Cognitive Science, 4, 379-426. doi:10.1016/S0364-0213(80)80010-3.
Chinn, C., & Brewer, W. (1993). The role of anomalous data in knowledge acquisition – A theoretical framework and implications for science instruction. Review of Educational Research, 63(1), 1-49. doi: 10.2307/1170558.
Craig, S., Graesser, A. C., Sullins, J., & Gholson, J. (2004). Affect and learning: an exploratory look into the role of affect in learning. Journal of Educational Media, 29, 241-250. doi:10.1080/1358165042000283101.
Craik, F. I. M., & Tulving, E. (1972). Depth of processing and the retention of words in episodic memory. Journal of Experimental Psychology: General, 104, 268-294. doi:10.1037//0096-34220.127.116.118.
Dansereau, D. F. (1988). Cooperative learning strategies. In C. E. Weinstein, E. T. Goetz, & P. A. Alexander (Eds.), Learning and study strategies: Issues in assessment, instruction, and evaluation (pp. 103-120). New York: Academic Press.
De Dreu, C. K. W., & Weingart, L. R. (2003). Task versus relationship conflict, team effectiveness, and team member satisfaction: A meta-analysis. Journal of Applied Psychology, 88, 741-749. doi:10.1037/0021-9010.88.4.741.
De Lisi, R., & Goldbeck, S. L. (1999). Implication of Piagetian theory for peer learning. In A. M. O’Donnell & A. King (Eds.), Cognitive perspectives on peer learning (pp. 3-37). Mahwah: Erlbaum.
De Wit F. R. C., Greer L. L., & Jehn K. A. (2012). The paradox of intragroup conflict: a meta-analysis. Journal of Applied Psychology, 2012, Vol. 97, No. 2, 360-390.
D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22, 145e157. doi:10.1016/j.learninstruc. 2011.10.001.
D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. C. (2014). When confusion can be beneficial for learning. Learning and Instruction, 29, 153-170.
Ellis, H. C., & Ashbrook, P. W. (1988). Resource allocation model of the effect of depressed mood states on memory. In K. Fiedler and J. Forgas (Ecs.), Affect, Cognition, and Social Behavior. Toronto: Hogrefe International.
Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press.
Graesser, A. C. (2016). Conversations with AutoTutor help students learn. International Journal of Artificial Intelligence in Education, 26(1), 124-132.
Graesser, A. C., D’Mello, S. K., Hu. X., Cai, Z., Olney, A., & Morgan, B. (2012). AutoTutor. In P. McCarthy and C. Boonthum-Denecke (Eds.), Applied natural language processing: Identification, investigation, and resolution (pp. 169-187). Hershey, PA: IGI Global.
Graesser, A. C., Forsyth, C. M., & Lehman, B. A. (2017). Two heads may be better than one: learning from computer agents in conversational trialogues. Teachers College Record, 119(3).
Graesser, A. C., Lu, S., Jackson, G. T., Mitchell, H., Ventura, M., Olney, A., & Louwerse, M. M. (2004). AutoTutor: A tutor with dialogue in natural language. Behavioral Research Methods, Instruments, and Computers, 36, 180-193.
Graesser, A., Lu, S., Olde, B., Cooper-Pye, E., & Whitten, S. (2005). Question asking and eye tracking during cognitive disequilibrium: comprehending illustrated texts on devices when the devices break down. Memory and Cognition, 33, 1235-1247. doi:10.3758/BF03193225.
Jehn, K. A. (1995). A multimethod examination of the benefits and detriments of intragroup conflict. Administrative Science Quarterly, 40, 256-282.
Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success story: Social interdependence theory and cooperative learning. Educational Researcher, 38(5), 365–379. doi:10.3102/0013189X09339057.
Kagan, J. (2009). Categories of novelty and states of uncertainty. Review of General Psychology, 13(4), 290-301.
Laird, J. E., Newell, A., & Rosenbloom, P. S. (1987). Soar – An architecture for general intelligence. Artificial Intelligence, 33(1), 1-64. doi: 10.1016/0004-3702(87)90050-6.
Limón, M. (2001). On the cognitive conflict as an instructional strategy for conceptual change: A critical appraisal. Learning and Instruction, 11(4-5), 357–380.
Mandler, G. (1990). Interruption (discrepancy) theory: Review and extensions. In S. Fisher & C. L. Cooper (Eds.), On the Move: The Psychology of Change and Transition (pp. 13-32). Chichester: Wiley.
McQuiggan, S., Mott, B., & Lester, J. (2008). Modeling self-efficacy in intelligent tutoring systems: an inductive approach. User Modeling and User-Adapted Interaction, 18, 81-123.
Millis, K., Forsyth, C., Butler, H., Wallace, P., Graesser, A. C., & Halpern, D. (2011). Operation ARIES! A serious game for teaching scientific inquiry. In M. Ma, A. Oikonomou & J. Lakhmi (Eds.) Serious games and edutainment applications (pp.169-196). London, UK: Springer-Verlag.
Mugny, G., & Doise, W. (1978). Socio-cognitive conflict and structure of individual and collective performances. European Journal of Social Psychology, 8(2), 181-192.
Nersessian, N. (2008). Mental modeling in conceptual change. In S. Vosniadou (Ed.), International handbook of research on conceptual change (pp. 391-416). New York: Routledge.
Nye B. D., Graesser A. C., Hu X. (2014). AutoTutor and family: A review of 17 years of natural language tutoring. International Journal of Artificial Intelligence in Education, 2014, 24(4): 427-469.
Pekrun, R., & Stephens, E. J. (2012). Academic emotions. In K. Harris, S. Graham, T. Urdan, S. Graham, J. Royer, & M. Zeidner (Eds.), Individual differences and cultural and contextual factors. APA educational psychology handbook, Vol. 2 (pp. 3-31). Washington, DC: American Psychological Association.
Pfister, H. R., & Oehl, M. (2009). The impact of goal focus, task type and group size on synchronous net based collaborative learning discourses. Journal of Computer Assisted Learning, 25(2), 161-176.
Piaget, J. (1952). The origins of intelligence. New York: International University Press.
Rus, V., D’Mello, S., Hu, X., & Graesser, A. C. (2013). Recent advances in intelligent systems with conversational dialogue. AI Magazine, 34, 42-54.
Schank, R. C, & Abelson, R. P. (1977). Scripts, plans, goals and understandings. Hillsdale, NJ: Erlbaum.
Schellens, T., Van Keer, H., De Wever, B., & Valcke, M. (2009). Tagging thinking types in asynchronous discussion groups: Effects on critical thinking. Interactive Learning Environments, 17(1), 77-94.
Siegler, R., & Jenkins, E. (1989). Strategy discovery and strategy generalization. Hillsdale, NJ: Lawrence Erlbaum Associates.
Sottilare R. A., Graesser A. C., Hu X., & Holden H. (2012). Design Recommendations for Intelligent Tutoring Systems: Volume 1-Learner Modeling. US Army Research Laboratory, 2013.
Stegmann K., Weinberger A., & Fischer F. (2007). Facilitating argumentative knowledge construction with computer-supported collaboration scripts. International journal of computer-supported collaborative learning, 2007, 2(4): 421-447.
Stein, N., Hernandez, M., & Trabasso, T. (2008). Advances in modeling emotions and thought: the importance of developmental, online, and multilevel analysis. In M. Lewis, J. M. Haviland-Jones, & L. F. Barrett (Eds.), Handbook of emotions (3rd ed.). (pp. 574-586) New York: Guilford Press.
Toulmin, S. (1958). The uses of argument. Cambridge: Cambridge University Press.
VanLehn K., Jones R. M., & Chi M. T. H. (1992). A model of the selfexplanation effect. Learning Science, 2, 1-60.
VanLehn, K., Siler, S., Murray, C., Yamauchi, T., & Baggett, W. (2003). Why do only some events cause learning during human tutoring? Cognition and Instruction, 21(3), 209-249. doi: 10.1207/S1532690XCI2103_01.
Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46(1), 71-95.
Wen, Z. L., Zhang, L., Hou, J. T., & Liu, H. Y. (2004). Testing and application of the mediating effects. Acta Psychologica Sinica, 36(5), 614-620.