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Articles

Predicting student understanding by modeling interactive exploration of evidence during an online science investigation

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Pages 821-833 | Received 27 Jan 2019, Accepted 01 Nov 2019, Published online: 15 Nov 2019

References

  • Atkinson, R., & Renkl, A. (2007). Interactive example-based learning environments: Using interactive elements to encourage effective processing of worked examples. Educational Psychology Review, 19(3), 375–386. doi:https://doi.org/10.1007/s10648-007-9055-2
  • Baker, R. S., Dmello, S. K., Rodrigo, M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4), 223–241. doi:https://doi.org/10.1016/j.ijhcs.2009.12.003
  • Baker, R., Walonoski, J., Heffernan, N., Roll, I., Corbett, A., & Koedinger, K. (2008). Why students engage in “gaming the system” behavior in interactive learning environments. Journal of Interactive Learning Research, 19(2), 185–224.
  • Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM Journal of Educational Data Mining, 1(1), 3–17.
  • Barrett, T. J., Stull, A. T., Hsu, T. M., & Hegarty, M. (2015). Constrained interactivity for relating multiple representations in science: When virtual is better than real. Computers & Education, 81, 69–81. doi:https://doi.org/10.1016/j.compedu.2014.09.009
  • Bodemer, D., Ploetzner, R., Feuerlein, I., & Spada, H. (2004). The active integration of information during learning with dynamic and interactive visualisations. Learning and Instruction, 14(3), 325–341. doi:https://doi.org/10.1016/j.learninstruc.2004.06.006
  • Butcher, K. R., & Aleven, V. (2013). Using student interactions to foster rule– diagram mapping during problem solving in an intelligent tutoring system. Journal of Educational Psychology, 105(4), 988–1009. doi:https://doi.org/10.1037/a0031756
  • Butcher, K. R., Hudson, M., & Runburg, M. (2018). Visualizations for deep learning: Using 3D models to promote scientific observation and reasoning during collaborative STEM inquiry. In R. Zheng (Ed.), Strategies for deep learning with digital technology: Theories and practices in education (pp. 111–136). Hauppauge, NY: Nova Science Publishers.
  • Butcher, K. R., Runburg, M., & Hudson, M. (2017). Using digitized objects to promote critical thinking and engagement in classrooms. Library Hi Tech News, 34(7), 12–15. doi:https://doi.org/10.1108/lhtn-06-2017-0039
  • Chi, M., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243. doi:https://doi.org/10.1080/00461520.2014.965823
  • Clarke-Midura, J., Code, J., Zap, N., & Dede, C. (2012). Assessing science inquiry: A case study of the virtual performance assessment project. In L. Lesia & N. Kimberely Fletcher (Eds.), Cases on inquiry through instructional technology in math and science (pp. 138–164). Hershey, PA, USA: IGI Global.
  • Eberbach, C., & Crowley, K. (2009). From everyday to scientific observation: How children learn to observe the biologist’s world. Review of Educational Research, 79(1), 39–68. doi:https://doi.org/10.3102/0034654308325899
  • Estabrooks, A., Jo, T., & Japkowicz, N. (2004). A multiple resampling method for learning from imbalanced data sets. Computational Intelligence, 20(1), 18–36. doi:https://doi.org/10.1111/j.0824-7935.2004.t01-1-00228.x
  • Gerard, L. F., Spitulnik, M., & Linn, M. C. (2010). Teacher use of evidence to customize inquiry science instruction. Journal of Research in Science Teaching, 47(9), 1037–1063. doi:https://doi.org/10.1002/tea.20367
  • Gobert, J. D., Pedro, M. S., Raziuddin, J., & Baker, R. S. (2013). From log files to assessment metrics: Measuring students’ science inquiry skills using educational data mining. Journal of the Learning Sciences, 22(4), 521–563. doi:https://doi.org/10.1080/10508406.2013.837391
  • Hegarty, M., Smallman, H. S., & Stull, A. T. (2012). Choosing and using geospatial displays: Effects of design on performance and metacognition. Journal of Experimental Psychology: Applied, 18(1), 1–17. doi:https://doi.org/10.1037/a0026625
  • Johnston, J. S. (2009). What does the skill of observation look like in young children? International Journal of Science Education, 31(18), 2511–2525. doi:https://doi.org/10.1080/09500690802644637
  • Ketelhut, D. J., Nelson, B., Sil, A., & Yates, A. (2013). Discovering what students know through datamining their problem-solving actions within the immersive virtual environment, SAVE science. Presentation at the annual meeting of the American Educational Research Association, San Francisco.
  • Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems (pp. 4765–4774).
  • Monteira, S. F., & Jiménez-Aleixandre, M. P. (2016). The practice of using evidence in kindergarten: The role of purposeful observation. Journal of Research in Science Teaching, 53(8), 1232–1258. doi:https://doi.org/10.1002/tea.21259
  • National Research Council (NRC). (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: National Academies Press.
  • Patro, S., & Sahu, K. K. (2015). Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462.
  • Poitras, E. G., Butcher, K. R., & Hudson, M. (2018). Subgroup mining of learner behaviors with interactive diagrams in research quest. Poster Presented at the 2018 American Educational Research Association Annual Meeting, New York, NY.
  • Quellmalz, E. S., Timms, M. J., Silberglitt, M. D., & Buckley, B. C. (2012). Science assessments for all: Integrating science simulations into balanced state science assessment systems. Journal of Research in Science Teaching, 49(3), 363–393. doi:https://doi.org/10.1002/tea.21005
  • Rasch, T., & Schnotz, W. (2009). Interactive and non-interactive pictures in multimedia learning environments: Effects on learning outcomes and learning efficiency. Learning and Instruction, 19(5), 411–422. doi:https://doi.org/10.1016/j.learninstruc.2009.02.008
  • Sampson, V., & Blanchard, M. R. (2012). Science teachers and scientific argumentation: Trends in views and practice. Journal of Research in Science Teaching, 49(9), 1122–1148. doi:https://doi.org/10.1002/tea.21037
  • Stull, A., & Hegarty, M. (2016). Model manipulation and learning: Fostering representational competence with virtual and concrete models. Journal of Educational Psychology, 108(4), 509–527. doi:https://doi.org/10.1037/edu0000077
  • Taub, M., Azevedo, R., Bradbury, A. E., Millar, G. C., & Lester, J. (2018). Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment. Learning and Instruction, 54, 93–103. doi:https://doi.org/10.1016/j.learninstruc.2017.08.005
  • Tolmie, A. K., Ghazali, Z., & Morris, S. (2016). Children's science learning: A core skills approach. British Journal of Educational Psychology, 86(3), 481–497. doi:https://doi.org/10.1111/bjep.12119
  • Tsang, M, Cheng, D, & Liu, Y. (2017). Detecting statistical interactions from neural network weights. arXiv preprint arXiv:1705.04977.
  • Zangori, L., Forbes, C. T., & Biggers, M. (2013). Fostering student sense making in elementary science learning environments: Elementary teachers’ use of science curriculum materials to promote explanation construction. Journal of Research in Science Teaching, 50(8), 989–1017. doi:https://doi.org/10.1002/tea.21104

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