Joseph M. Reilly
ABSTRACT
To scaffold students' investigations of an inquiry-based immersive virtual world for science education without undercutting the affordances an open-ended activity provides, this study explores ways time-stamped log files of groups' actions may enable the automatic generation of formative supports. Groups' logged actions in the virtual world are filtered via principal component analysis to provide a time series trajectory showing the rate of their investigative activities over time. This technique functions well in open-ended environments and examines the entire course of their experience in the virtual world instead of specific subsequences. Groups' trajectories are grouped via k-means clustering to identify different typical pathways taken through the immersive virtual world. These different approaches are then correlated with learning gains across several survey constructs (affective dimensions, ecosystem science content, understanding of causality, and experimental methods) to see how various trends are associated with different outcomes. Differences by teacher and school are explored to see how best to support inclusion and success of a diverse array of learners.
- Fayer, S., Lacey, A., and Watson, A. 2017. STEM Occupations: Past, Present, And Future. Washington, DC: U.S. Bureau of Labor Statistics.Google Scholar
- Langdon, D., McKittrick, G., Beede, D., Khan, B., and Doms, M. 2012. STEM: Good jobs now and for the future. Washington, DC: US Department of Commerce Economics and Statistics Administration.Google Scholar
- National Research Council. 2011. Successful K-12 STEM education: Identifying effective approaches in science, technology, engineering, and mathematics. National Academies Press.Google Scholar
- National Research Council, 1996. National Science Education Standards. National Academies Press.Google Scholar
- Sawyer, R., Rowe, J., Azevedo, R., and Lester, J. 2018. Filtered Time Series Analyses of Student Problem-Solving Behaviors in Game-based Learning. In Proceedings of the 11th International Conference on Educational Data Mining, K.E. Boyer & M. Yudelson (Eds.), 247--257.Google Scholar
- Clarke-Midura, J. and Dede, C., 2010. Assessment, technology, and change. Journal of Research on Technology in Education, 42(3), 309--328.Google ScholarCross Ref
- Shute, V. J. 2011. Stealth assessment in computer-based games to support learning. Computer Games and Instruction, 55, 2, 503--524.Google Scholar
- Bransford, J. D., Brown, A. L., and Cocking, R. R. 2000. How people learn: Brain, mind, experience, and school (Rev. ed.). Washington, DC: National Academies Press.Google Scholar
- Koedinger, K. R., Brunskill, E., Baker, R. Sj., McLaughlin, E. A., and Stamper, J. 2013. New potentials for data-driven intelligent tutoring system development and optimization. AI Magazine, 34, 3, 27--41.Google ScholarDigital Library
- Rivers, K., and Koedinger, K. R. 2013. Automatic generation of programming feedback: A data-driven approach. In The First Workshop on AI-supported Education for Computer Science (AIEDCS 2013) (Vol. 50).Google Scholar
- Sao Pedro, M., Baker, R. Sj., Montalvo, O., Nakama, A., and Gobert, J. D. 2010. Using text replay tagging to produce detectors of systematic experimentation behavior patterns. In Proceedings of the Third International Conference on Educational Data Mining.Google Scholar
- Gross, S., Zhu, X., Hammer, B., and Pinkwart, N. 2012. Cluster based feedback provision strategies in intelligent tutoring systems. In International Conference on Intelligent Tutoring Systems, 699--700. Google ScholarDigital Library
- Andersen, E., et al. (2012). The impact of tutorials on games of varying complexity. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 59--68. Google ScholarDigital Library
- Lee, S. J., Liu, Y.-E., & Popovic, Z. (2014). Learning individual behavior in an educational game: a data-driven approach. In Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), 114--121.Google Scholar
- Baker, R. S., and Clarke-Midura, J. 2013. Predicting successful inquiry learning in a virtual performance assessment for science. In International Conference on User Modeling, Adaptation, and Personalization, 203--214.Google Scholar
- Wijesooriya, C., Heales, J., and Clutterbuck, P. 2015. Forms of formative assessment in virtual learning environments. In 21st Americas Conference on Information Systems (AMCIS 2015), 1--16.Google Scholar
- Bauer, A., and Popović, Z. 2017. Collaborative Problem Solving in an Open-Ended Scientific Discovery Game. In Proceedings of the ACM on Human-Computer Interaction (CSCW), 1--21 Google ScholarDigital Library
- Sawyer, R., Smith, A., Rowe, J., Azevedo, R. and Lester, J. 2017. Enhancing student models in game-based learning with facial expression recognition. Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, 192--201. Google ScholarDigital Library
- Kinnebrew, J., Segedy, J. and Biswas, G. 2014. Analyzing the temporal evolution of students' behaviors in open-ended learning environments. Metacognition and Learning. 9, 2, 187--215.Google ScholarCross Ref
- Beal, C., Mitra, S. and Cohen, P. 2007. Modeling learning patterns of students with a tutoring system using hidden Markov models. Proceedings of the 13th Int. Conference on Artificial Intelligence in Education, 238--245. Google ScholarDigital Library
- Martinez, R., Yacef, K. and Kay, J. 2011. Analysing frequent sequential patterns of collaborative learning activity around an interactive tabletop. Proceedings of the 4th Int. Educational Data Mining, 111--120.Google Scholar
- Quigley, D., Ostwald, J. and Sumner, T., 2017. Scientific modeling: using learning analytics to examine student practices and classroom variation. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 329--338. Google ScholarDigital Library
- Grotzer, T.A., Metcalf, S.J., Tutwiler, M.S., Kamarainen, A.M., Thompson, M. and Dede, C., 2017. Teaching the systems aspects of epistemologically authentic experimentation in ecosystems through immersive worlds. National Association for Research in Science Teaching, San Antonio, TX.Google Scholar
- Dede, C., Grotzer, T.A., Kamarainen, A. and Metcalf, S., 2017. EcoXPT: Designing for Deeper Learning through Experimentation in an Immersive Virtual Ecosystem. Journal of Educational Technology & Society, 20(4), 166--178.Google Scholar
- Berland, M., Martin, T., Benton, T., Petrick Smith, C., & Davis, D. (2013). Using learning analytics to understand the learning pathways of novice programmers. Journal of the Learning Sciences, 22(4), 564--599.Google ScholarCross Ref
- Thompson, M., Tutwiler, M.S., Kamarainen, A., Metcalf, S., Grotzer, T. and Dede, C., 2016. A Blended assessment strategy for EcoXPT: An Experimentation-driven ecosystems science-based multi-user virtual environment. American Educational Research Association (AERA), Washington DC.Google Scholar
- Reilly, J. and Dede, C., 2018. Filtered Time Series Analysis of Scientific Inquiry in an Immersive Virtual World. Education Technology and Computational Psychometrics Symposium. Iowa City, IAGoogle Scholar
Index Terms
- Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World
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