Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning
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Published
Dec 26, 2018
Michelle Taub
Roger Azevedo
Abstract
Self-regulated learning conducted through metacognitive monitoring and scientific inquiry can be influenced by many factors, such as emotions and motivation, and are necessary skills needed to engage in efficient hypothesis testing during game-based learning. Although many studies have investigated metacognitive monitoring and scientific inquiry skills during game-based learning, few studies have investigated how the sequence of behaviors involved during hypothesis testing with game-based learning differ based on both efficiency level and emotions during gameplay. For this study, we analyzed 59 undergraduate students’ (59% female) metacognitive monitoring and hypothesis testing behavior during learning and gameplay with CRYSTAL ISLAND, a game-based learning environment that teaches students about microbiology. Specifically, we used sequential pattern mining and differential sequence mining to determine if there were sequences of hypothesis testing behaviors and to determine if the frequencies of occurrence of these sequences differed between high or low levels of efficiency at finishing the game and high or low levels of facial expressions of emotions during gameplay. Results revealed that students with low levels of efficiency and high levels of facial expressions of emotions had the most sequences of testing behaviors overall, specifically engaging in more sequences that were indicative of less strategic hypothesis testing behavior than the other students, where students who were more efficient with both levels of emotions demonstrated strategic testing behavior. These results have implications for the strengths of using educational data mining techniques for determining the processes underlying patterns of engaging in self-regulated learning conducted through hypothesis testing as they unfold over time; for training students on how to engage in the self-regulation, scientific inquiry, and emotion regulation processes that can result in efficient gameplay; and for developing adaptive game-based learning environments that foster effective and efficient self-regulation and scientific inquiry during learning.
How to Cite
Taub, M., & Azevedo, R. (2018). Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning. Journal of Educational Data Mining, 10(3), 1–26. https://doi.org/10.5281/zenodo.3554711
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Keywords
efficiency, emotions, game-based learning, scientific inquiry, self-regulated learning, sequence mining
References
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AZEVEDO, R., HARLEY, J., TREVORS, G., FEYZI-BEHNAGH, R., DUFFY, M., BOUCHET, F., AND LANDIS, R. S. 2013. Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In R. Azevedo AND V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 427–449). Amsterdam, The Netherlands: Springer.
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BOUCHET, F., HARLEY, J., TREVORS, G., AND AZEVEDO, R. 2013. Clustering and profiling students according to their interactions with an intelligent tutoring system fostering self-regulated learning. Journal of Educational Data Mining, 5, 104–146.
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Papadourakis, AND K. Panourgia (Eds.), Proceedings of the 11th International Conference on Intelligent Tutoring Systems—Lecture Notes in Computer Science 7315 (pp. 59–64). Amsterdam, The Netherlands: Springer.
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AZEVEDO, R. 2009. Theoretical, methodological, and analytical challenges in the research on metacognition and self-regulation: A commentary. Metacognition & Learning, 4, 87–95.
AZEVEDO, R., HARLEY, J., TREVORS, G., FEYZI-BEHNAGH, R., DUFFY, M., BOUCHET, F., AND LANDIS, R. S. 2013. Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In R. Azevedo AND V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 427–449). Amsterdam, The Netherlands: Springer.
AZEVEDO, R., TAUB, M., AND MUDRICK, N. V. 2018. Using multi-channel trace data to infer and foster self-regulated learning between humans and advanced learning technologies. In D. Schunk AND J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed.). (pp. 254-270). New York, NY: Routledge.
BAKER, R. S., AND CORBETT, A. T. 2014. Assessment of robust learning with educational data mining. Research & Practice in Assessment, 9, 38–50.
BAKER, R. S. J. D., AND YACEF, K. 2009. The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1, 3–16.
BOUCHET, F., HARLEY, J., TREVORS, G., AND AZEVEDO, R. 2013. Clustering and profiling students according to their interactions with an intelligent tutoring system fostering self-regulated learning. Journal of Educational Data Mining, 5, 104–146.
CHANG, K. E., WU, L. J., WENG, S. E., AND SUNG, Y. T. 2012. Embedding game-based problemsolving phase into problem-posing system for mathematics learning. Computers & Education, 58, 775–786.
CHEN, L. X., AND SUN, C. T. 2016. Self-regulation influence on game play flow state. Computers in Human Behavior, 54, 341–350.
CHENG, M. T., SHE, H. C., AND ANNETTA, L. A. 2014. Game immersion experience: Its hierarchical structure and impact on game-based science learning. Journal of Computer Assisted Learning, 31, 232–253.
CLARK, D. B., TANNER-SMITH, E. E., AND KILLINGSWORTH, S. S. 2016. Digital games, design, and learning: A systematic review and meta-analysis. Review of Educational Research, 86, 79– 122.
CONNOLLY, T. M., BOYLE, E. A., MACARTHUR, E., HAINEY, T., AND BOYLE, J. M. 2012. A systematic literature review of empirical evidence on computer games and serious games. Computers & Education, 59, 661-686.
DAVIES, J. J., AND HEMINGWAY, T. J. 2014. Guitar hero or zero? Fantasy, self-esteem, and deficient self-regulation in rhythm-based music video games. Journal of Media Psychology, 26, 189– 201. D’MELLO, S. K., AND GRAESSER, A. C. 2012. Dynamics of affective states during complex learning. Learning and Instruction, 22, 145–157.
EDENS, K. M. 2008. The integration of pedagogical approach, gender, self-regulation, and goal orientation using student response system technology. Journal of Research on Technology in Education, 41, 161–177.
EKMAN, P. 1973. Darwin and facial expression: A century of research in review. New York, NY: Academic Press.
ELLIOT, A. J., AND MURAYAMA, K. 2008. On the measurement of achievement goals: Critique, illustration, and application. Journal of Educational Psychology, 100, 613–628.
ESLINGER, E., WHITE, B., FREDERIKSEN, J., AND BROBST, J. 2008. Supporting inquiry processes with an interactive learning environment: Inquiry Island. Journal of Science Education and Technology, 17, 610–617.
FENG, C. Y., AND CHEN, M. P. 2014. The effects of goal specificity on programming performance and self-regulation in game design. British Journal of Educational Technology, 45, 285–302.
FOURNIER-VIGER, P., GOMARIZ, A., CAMPOS, M., AND THOMAS, R. 2014. Fast vertical mining of sequential patterns using co-occurrence information. In V.S. Tseng, T.B. Ho, Z. Zhou, A.L.P.
Chen, AND H. Kao (Eds). Proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 40-52). Cham, Switzerland: Springer.
GIRARD, C., ESCALLE, J., AND MAGNAN, A. 2012. Serious games as new educational tools: How effective are they? A meta-analysis of recent studies. Journal of Computer Assisted Learning, 29, 207–219.
GOBERT, J. D., SAO PEDRO, M. A., BAKER, R. S. J. D., TOTO, E., AND MONTALVO, O. 2012. Leveraging educational data mining for real-time performance assessment of scientific inquiry skills within Microworlds. Journal of Educational Data Mining, 4, 111–143.
GOBERT, J. D., SAN PEDRO, M., RAZIUDDIN, J., AND BAKER, R. 2013. From log files to assessment metrics: Measuring students’ science inquiry skills using educational data mining. Journal of the Learning Sciences, 22, 521–563.
GRAESSER, A. C. 2013. Evolution of advanced learning technologies in the 21st century. Theory into Practice, 52, 93–101.
GRAFSGAARD, J. F. 2014. Multimodal affect modeling in task-oriented tutorial dialogue. (Doctoral dissertation). Retrieved from ProQuest. (3690271).
GROSS, J. J. 2015. The extended process model of emotion regulation: Elaborations, applications, and future directions. Psychological Inquiry, 26, 130–137.
IMOTIONS ATTENTION TOOL (VERSION 6.0) [COMPUTER SOFTWARE] 2016. Boston, MA: iMotions Inc.
KE, F. 2008. Computer games application within alternative classroom goal structures: Cognitive, metacognitive, and affective evaluation. Educational Technology Research and Development, 56, 539–556.
KIM, B., PARK, H., AND BAEK, Y. 2009. Not just fun, but serious strategies: Using meta-cognitive strategies in game-based learning. Computers & Education, 52, 800–810.
KINNEBREW, J. S., LORETZ, K. M., AND BISWAS, G. 2013. A contextualized, differential sequence mining method to derive students’ learning behavior patterns. Journal of Educational Data Mining, 5, 190–219.
LESTER, J. C., Rowe, J. P., and Mott, B. W. 2013. Narrative-centered learning environments: A story-centric approach to educational games. In C. Mouza AND N. Lavigne (Eds.), Emerging Technologies for the Classroom: A Learning Sciences Perspective (pp. 223-238). New York, NY: Springer US.
MARONE, V., STAPLES, C., AND GREENBERG, K. H. 2016. Learning how to learn by solving bizarre problems: A playful approach to developing creative and strategic thinking. On the Horizon, 24, 112–120.
MAYER, R. E. (ED.) 2014. Computer games for learning: An evidence-based approach. Cambridge, MA: MIT Press.
MILLIS, K., FORSYTH, C., BUTLER, H., WALLACE, P., GRAESSER, A., AND HALPERN, D. 2011. Operation ARIES!: A serious game for teaching scientific inquiry. In M. Ma, A. Oikonomou, AND L. Jain (Eds.), Serious games and edutainment applications (pp. 169–196). London, UK: Springer-Verlag.
NIETFELD, J. L., SHORES, L. R., AND HOFFMANN, K. F. 2014. Learning environment self-regulation and gender within a game-based learning environment. Journal of Educational Psychology, 106, 961–973.
OCUMPAUGH, J., BAKER, R., GOWDA, S., HEFFERNAN, N., AND HEFFERNAN, C. 2014. Population validity for educational data mining models: A case study in affect detection. British Journal of Educational Technology, 45, 487–501.
PAPASTERGIOU, M. 2009. Digital game-based learning in high school computer science education: Impact on educational effectiveness and student motivation. Computers & Education, 52, 1–12.
PEKRUN, R., GOETZ, T., FRENZEL, A., BARCHFELD, P., AND PERRY, R. 2011. Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36, 36–48.
PINTRICH, P. R. 2000. The role of goal orientation in self-regulated learning. In M. Boekaerts, P. Pintrich, AND M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). San Diego, CA: Academic Press.
ROWE, J., SHORES, L., MOTT, B., AND LESTER, J. 2011. Integrating learning, problem solving, and engagement in narrative-centered learning environments. International Journal of Artificial Intelligence in Education, 21, 115–133.
SABOURIN, J. L., AND LESTER, J. C. 2014. Affect and engagement in game-based learning environments. IEEE Transactions on Affective Computing, 5, 45-56.
SABOURIN, J., ROWE, J., MOTT, B., AND LESTER, J. 2012. Exploring inquiry-based problemsolving strategies in game-based learning environments. In S. A. Cerri, W. J. Clancey, G.
Papadourakis, AND K. Panourgia (Eds.), Proceedings of the 11th International Conference on Intelligent Tutoring Systems—Lecture Notes in Computer Science 7315 (pp. 59–64). Amsterdam, The Netherlands: Springer.
SABOURIN, J. L., SHORES, L. R., MOTT, B. W., AND LESTER, J. C. 2013. Understanding and predicting student self-regulated learning strategies in game-based learning environments. International Journal of Artificial Intelligence in Education, 23, 94–114.
SCHERER, K. 2009. Emotions are emergent processes: They require a dynamic computational architecture. Philosophical Transactions of the Royal Society, 364, 3459–3474.
SCHRAW, G., BRUNING, R., AND SVOBODA, C. 1995. Sources of situational interest. Journal of Literacy Research, 27, 1–17.
SHUTE, V. J., D’MELLO, S., BAKER, R., CHO, K., BOSCH, N., OCUMPAUGH, J., VENTURA, M., AND ALMEDA, V. 2015. Modeling how incoming knowledge, persistence, affective states, and game progress influence student learning from an educational game. Computers & Education, 85, 224–235.
SHUTE, V., AND VENTURA, M. 2013. Measuring and supporting learning in video games: Stealth assessment. Cambridge, MA: The MIT Press.
SHUTE, V. J., VENTURA, M., AND KIM, Y. J. 2013. Assessment and learning of qualitative physics in Newton’s playground. The Journal of Educational Research, 106, 423–430. SMI EXPERIMENT CENTER 3.4.165 [APPARATUS AND SOFTWARE]. 2014. Boston, Massachusetts, USA: SensoMotoric Instruments.
SNOW, E. L. 2016. Promoting self-regulation and metacognition through the use of online trace data within a game-based environment. (Doctoral dissertation). Retrieved from Arizona State University Libraries, ASU Electronic Dissertations and Theses.
SNOW, E. L., MCNAMARA, D. S., JACOVINA, M. E., ALLEN, L. K., JOHNSON, A. M., PERRET, C. A., DAI, J., JACKSON, G. T., LIKENS, A. D., RUSSELL, D. G., AND WESTON, J. L. 2015. In C. Conati, N. Heffernan, A. Mitrovic, AND M. F. Verdejo (Eds.), Proceedings of the 17th International Conference on Artificial Intelligence in Education—Lecture Notes in Computer Science 9112 (pp. 786–789). Basel, Switzerland: Springer International Publishing.
SONNENBERG, C., AND BANNERT, M. 2015. Discovering the effects of metacognitive prompts on the sequential structure of SRL-processes using process mining techniques. Journal of Learning Analytics, 2, 72–100.
SPIRES, H. A. 2015. Digital game-based learning: What’s literacy got to do with it? Journal of Adolescent & Adult Literacy, 59, 125–130.
SPIRES, H. A., ROWE, J. P., MOTT, B. W., AND LESTER, J. C. 2011. Problem solving and game-based learning: Effects of middle grade students’ hypothesis testing strategies on learning outcomes. Journal of Educational Computing Research, 44, 453–472.
TSAI, M. J., HUANG, L. J., HOU, H. T., HSU, C. Y., AND CHIOU, G. L. 2016. Visual behaviour, flow and achievement in game-based learning. Computers & Education, 98, 115–129.
VOS, N., VAN DER MEIJDEN, H., AND DENESSEN, E. 2011. Effects of constructing versus playing an educational game on student motivation and deep learning strategy use. Computers & Education, 56, 127–137.
WHITE, B., FREDERIKSEN, J., AND COLLINS, A. 2009. The interplay of scientific inquiry and metacognition: More than a marriage of convenience. In D. Hacker, J. Dunlosky, & A. Graesser (Eds.), Handbook of metacognition in education (pp. 175–205). New York, NY: Routledge.
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