Abstract
Student agency has long been viewed as a critical element in game-based learning. Agency refers to the degree of freedom and control that a student has to perform meaningful actions in a learning environment. While long postulated to be central to student self-regulation, there is limited evidence on the design of game-based learning environments that promote student agency and its effect on learning. This paper reports on an experiment to investigate the impact of student agency on learning and problem-solving behavior in a game-based learning environment for microbiology. Students interacted with one of three versions of the system. In the High Agency condition, students could freely navigate the game’s 3D open-world environment and perform problem-solving actions in any order they chose. In the Low Agency condition, students were required to traverse the environment and solve the mystery in a prescribed partially ordered sequence. In the No Agency condition, students watched a video of an expert playing the game by following an “ideal path” for solving the problem scenario. Results indicate that students in the Low Agency condition achieved greater learning gains than students in both the High Agency and No Agency conditions, but exhibited more unproductive behaviors, suggesting that artfully striking a balance between high and low agency best supports learning.
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Ventura, M., Shute, V., Kim, Y.: Assessment and learning of qualitative physics in newton s playground Newton’s Playground. J. Ed. Res. 106, 423–430 (2013)
Easterday, M.W., Aleven, V., Scheines, R., Carver, S.M.: Using tutors to improve educational games: a cognitive game for policy argument. J. Learn. Sci. pp. 1–51 (2016)
Wouters, P., van Nimwegen, C., van Oostendorp, H., van der Spek, E.D.: A meta-analysis of the cognitive and motivational effects of serious games. J. Educ. Psychol. 105, 249–265 (2013)
Sao Pedro, M., Baker, R., Gobert, J., Montalvo, O., Nakama, A.: Leveraging machine-learned detectors of systematic inquiry behavior to estimate and predict transfer of inquiry skill. User Model. User-Adap. Inter. 23, 1–39 (2013)
VanLehn, K.: The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ. Psychol. 46, 197–221 (2011)
Koedinger, K., Aleven, V.: An interview reflection on “intelligent Tutoring Goes to School in the Big City”. Int. J. Artif. Intell. Educ. 26, 13–24 (2016)
Clark, D., Tanner-Smith, E., Killingsworth, S.: Digital games, design, and learning: a systematic review and meta-analysis. Rev. Ed. Res. 86, 79–122 (2016)
Mayer, R.E.: Computer games for learning: an evidence-based approach (2014)
Long, Y., Aleven, V.: Gamification of joint student/system control over problem selection in a linear equation tutor. In: Proceedings of the 12th International Conference on Intelligent Tutoring Systems, pp. 378–387 (2014)
Malone, T., Lepper, M.: Making learning fun: a taxonomy of intrinsic motivations for learning. Aptit. learn. instr. 3, 223–253 (1987)
Ryan, R., Deci, E.: Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55, 68–78 (2000)
Winne, P., Hadwin, A.: 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. Taylor & Francis, New York (2008)
Wardrip-Fruin, N., Mateas, M., Dow, S., Sali, S.: Agency Reconsidered. Breaking New Ground: Innovation in Games, Play, Practice and Theory (2009)
Snow, E., Allen, L., Jacovina, M., McNamara, D.: Does agency matter?: exploring the impact of controlled behaviors within a game-based environment. Comput. Educ. 82, 378–392 (2015)
Rowe, J., Shores, L., Mott, B., Lester, J.: Integrating learning, problem solving, and engagement in narrative-centered learning environments. Int. J. Artif. Intell. Educ. 21, 115–133 (2011)
Winne, P., Azevedo, R.: Metacognition. In: Sawyer, K. (ed.) Cambridge Handbook of the Learning Sciences, pp. 63–87. Cambridge University Press, Cambridge (2014)
Mayer, R.: Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. Am. Psychol. 59, 14–19 (2004)
Kirschner, P., Sweller, J., Clark, R.: Why minimal guidance during instruction does not work: an analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educ. Psychol. 41, 75–86 (2006)
Harp, S., Mayer, R.: How seductive details do their damage: a theory of cognitive interest in science learning. J. Educ. Psychol. 90, 414–434 (1998)
Baker, R., Moore, G., Wagner, A., Kalka, J., Salvi, A., Karabinos, M., Ashe, C., Yaron, D.: The dynamics between student affect and behavior occuring outside of educational software. In: Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction, pp. 14–24 (2011)
Rowe, J., McQuiggan, S., Robison, J., Lester, J.: Off-task behavior in narrative-centered learning environments. In: 14th International Conference on Artificial Intelligence in Education, pp. 99–106. IOS Press, Brighton (2009)
Cordova, D., Lepper, M.: Intrinsic motivation and the process of learning: beneficial effects of contextualization, personalization, and choice. J. Educ. Psychol. 88, 715–730 (1996)
Barab, S., Thomas, M., Dodge, T., Carteaux, R., Tuzun, H.: Making learning fun: quest atlantis, a game without guns. Educ. Technol. Res. Dev. 53, 86–107 (2005)
Baker, R., Clarke-Midura, J., Ocumpaugh, J.: Towards general models of effective science inquiry in virtual performance assessments. J. Comput. Assist. Learn. 32, 267–280 (2016)
Azevedo, R.: Defining and measuring engagement and learning in science: conceptual, theoretical, methodological, and analytical issues. Ed. Psychol. 50, 84–94 (2015)
Acknowledgments
We would like to thank our collaborators in the Center for Educational Informatics and the SMART Lab at N.C. State University. This study was supported by funding from the Social Sciences and Humanities Research Council of Canada. Any conclusions expressed in this material do not necessarily reflect the views of SSHRC.
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Sawyer, R., Smith, A., Rowe, J., Azevedo, R., Lester, J. (2017). Is More Agency Better? The Impact of Student Agency on Game-Based Learning. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds) Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science(), vol 10331. Springer, Cham. https://doi.org/10.1007/978-3-319-61425-0_28
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