Abstract
Cognitive, affective, metacognitive, and motivational (CAMM) processes are critical components of self-regulated learning (SRL) essential for learning and problem solving. Currently, ITSs are designed to foster cognitive, affective, and metacognitive (CAM) strategies and processes, presenting major gaps in the research since motivation is a key component of SRL and influences the remaining CAM processes. In our study, students interacted with MetaTutor, a hypermedia-based ITS, to investigate how 190 undergraduate students’ proportional learning gain (PLG) related to sub-goals set, cognitive strategy use and metacognitive processes differed based on self-reported achievement goal orientation. Results indicated differences between approach, avoidance, and students who adopted both approach and avoidance goal orientations, but no differences between mastery, performance and students who adopted both mastery and performance goal orientations on PLG for content related to sub-goal 1. Conversely, no differences were found between goal orientation groups on PLG for sub-goal 2, revealing possible changes in goal orientation following sub-goal 1. Analyses indicated no differences between goal orientation groups on metacognitive processes and cognitive strategy use. Thus, we suggest turning away from self-report data, where future studies aim to incorporate multi-channel data over durations of tasks as students interact with ITSs to measure motivation and its tendency to fluctuate in real-time. Implications for using multiple data channels to measure motivation could contribute to adaptive ITS design based on all CAMM processes.
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References
Azevedo, R., Taub, M., Mudrick, N.V.: Understanding and reasoning about real-time cognitive, affective, and metacognitive processes to foster self-regulation with advanced learning technologies. In: Schunk, D.H., Greene, J.A. (eds.) Handbook on Self-Regulation of Learning and Performance, 2nd edn, pp. 254–270. Routledge, New York (2018)
Winne, P.H., Hadwin, A.F.: The weave of motivation and self-regulated learning. In: Schunk, D., Zimmerman, B. (eds.) Motivation and Self-Regulated Learning: Theory, Research, and Applications, pp. 297–314. Erlbaum, Mahwah (2008)
Azevedo, R., Harley, J., Trevors, G., Duffy, M., Feyzi-Behnagh, R., Bouchet, F., Landis, R.: Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In: Azevedo, R., Aleven, V. (eds.) International Handbook of Metacognition and Learning Technologies. SIHE, vol. 28, pp. 427–449. Springer, New York (2013). https://doi.org/10.1007/978-1-4419-5546-3_28
Zimmerman, B.J., Schunk, D.H.: An essential dimension of self-regulated learning. In: Schunk, D.H., Zimmerman, B.J. (eds.) Motivation and Self-Regulated Learning: Theory, Research, and Applications, pp. 1–30. Taylor & Francis Group, LLC, New York (2008)
Elliot, A.J., Murayama, K.: On the measurement of achievement goals: critique, illustration, and application. J. Educ. Psychol. 100(3), 613–628 (2008)
Coutinho, S.A., Neuman, G.: A model of metacognition, achievement goal orientation, learning style and self-efficacy. Learn. Environ. Res. 11(2), 131–151 (2008)
Vaessen, B., Prins, F., Jeuring, J.: University students’ achievement goals and help seeking strategies in an intelligent tutoring system. Comput. Educ. 72(31), 196–208 (2014)
Witherspoon, A.M., Azevedo, R., D’Mello, S.: The dynamics of self-regulatory processes within self-and externally regulated learning episodes during complex science learning with hypermedia. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 260–269. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69132-7_30
Winne, P.H.: Cognition and metacognition in self-regulated learning. In: Schunk, D., Greene, J. (eds.) Handbook of Self-Regulation of Learning and Performance, pp. 36–48. New Routledge, New York (2017)
Du Boulay, B., del Soldato, T.: Implementation of motivational tactics in tutoring systems: 20 years on. Int. J. Artif. Intell. Educ. 26(1), 170–182 (2016)
Acknowledgements
This research was supported by funding from the National Science Foundation (DRL#1431552; DRL#1660878, DRL#1661202) and the Social Sciences and Humanities Research Council of Canada (SSHRC 895-2011-1006). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or Social Sciences and Humanities Research Council of Canada. The authors would like to thank the members from the SMART Lab at NCSU for their assistance with data collection.
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Cloude, E.B., Taub, M., Azevedo, R. (2018). Investigating the Role of Goal Orientation: Metacognitive and Cognitive Strategy Use and Learning with Intelligent Tutoring Systems. 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_5
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