• Gobert, J.D.(in press).
    Microworlds
    In Gunstone, R. Encyclopedia of Science Education. Springer.
  • Kim, B., Pathak, S., Jacobson, M., Zhang, B., & Gobert, J.D.(2015).
    Cycles of Exploration, Reflection, and Consolidation in Model-Based Learning of Genetics.
    Journal of Science Education and Technology. Journal of Science Education and Technology, 24(6), 789-02. DOI DOI 10.1007/s10956-015-9564-6
  • Gobert, J.D., Kim, Y.J, Sao Pedro, M.A.,Kennedy, M., & Betts, C.G. (in press).
    Using educational data mining to assess students’ skills at designing and conducting experiments within a complex systems microworld.
    Thinking Skills and Creativity. doi:10.1016/j.tsc.2015.04.008 (PDF)
  • Gobert, J. D., Baker, R. S., & Wixon, M. (2015).
    Operationalizing and Detecting Disengagement During On-Line Science Inquiry.
    In
    Educational Psychologist, 50:1, 43-57.. (PDF)
  • Gobert, J. D., Sao Pedro, M., Raziuddin, J., & Baker, R. S. (2013).
    From log files to assessment metrics: Measuring students' science inquiry skills using educational data mining.
    In
    Journal of the Learning Sciences, 22(4), 521-563.. [PDF]
  • Sao Pedro, M., Baker, R., & Gobert, J. (accepted).
    Improving Construct Validity Yields Better Models of Systematic Inquiry, Even with Less Information.
    In
    Proceedings of the 20th Conference on User Modeling, Adaptation, and Personalization (UMAP 2012). Montreal, QC, Canada (pp. 249-260). James Chen Best Student Paper Award [PDF]
  • Gobert, J., Sao Pedro, M., Baker, R.S., Toto, E., & Montalvo, O. (2012).
    Leveraging educational data mining for real time performance assessment of scientific inquiry skills within microworlds,
    Journal of Educational Data Mining, Article 15, Volume 4, 153-185.(PDF)
  • Gobert, J., Wild, S.C., and Rossi, L., (2012),
    Testing the effects of prior coursework and gender on geoscience learning with Google Earth,
    in Whitmeyer, S.J., Bailey,J.E., De Paor, D.G., and Ornduff, T., eds., Google Earth and Virtual Visualizations in Geoscience Education and Research: Geological Society of America SpecialPaper 492, p. 453-468, doi:10.1130/2012.2492(35). (PDF)
  • Sao Pedro, M., Gobert, J., & Baker, R. (2012, April 15).
    Assessing the Learning and Transfer of Data Collection Inquiry Skills Using Educational Data Mining on Students' Log Files.
    Paper presented at 
    The Annual Meeting of the American Educational Research Association. Vancouver, BC, CA: Retrieved April 15, 2012, from the AERA Online Paper Repository. [PDF]
  • Gobert, J., Raziuddin, J., & Sao Pedro, M. (2011).
    The Influence of Learner Characteristics on Conducting Scientific Inquiry Within Microworlds.
    To appear in L. Carlson, C. Hoelscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. (PDF)
  • Sao Pedro, M.A., Baker, R.S.J.d., Gobert, J., Montalvo, O. Nakama, A. (2011)
    Leveraging Machine-Learned Detectors of Systematic Inquiry Behavior to Estimate and Predict Transfer of Inquiry Skill.
    Journal of User Modeling and User-Adapted Interaction. (PDF)
  • Sao Pedro, M., Gobert, J., & Sebuwufu, P. (2011, April 10).
    The Effects of Quality Self-Explanations on Robust Understanding of the Control of Variables Strategy.
    Paper presented at 
    The Annual Meeting of the American Educational Research Association. New Orleans, LA: Retrieved April 18, 2011, from the AERA Online Paper Repository. [PDF]
  • Gobert, J., Sao Pedro, M., Toto, E., Montalvo, O., & Baker, R. (2011).
    Science ASSISTments: Assessing and scaffolding students' inquiry skills in real time.
    Paper presented at 
    The Annual Meeting of the American Educational Research Association, April, 2011, New Orleans, LA.
  • Gobert, J., Baker, R.,Sao Pedro,M.,Toto, E., & Montalvo, O. (2011).
    Science ASSISTments: Using student logs, machine learning, and data mining to determine when & how to scaffold students' science inquiry.
    Paper presented at 
    The Annual Meeting of the American Educational Research Association, April, 2011, New Orleans, LA
  • Gobert, J., O’Dwyer, L., Horwitz, P., Buckley, B., Levy, S.T. & Wilensky, U. (2011).
    Examining the relationship between students’ epistemologies of models and conceptual learning in three science domains: Biology, Physics, & Chemistry.
    International Journal of Science Education, 33(5), 653-684.
    (PDF)
  • Gobert, J.D, Pallant, A.R., & Daniels, J.T.M. (2010).
    Unpacking inquiry skills from content knowledge in Geoscience: A research perspective with implications for assessment design.
    International Journal of Learning Technologies, 5(3), 310-334.
    (PDF)
  • Bachmann, M, Gobert, J.D., & Beck, J. (2010).
    Tracking Students’ Inquiry Paths through Student Transition Analysis.

    Proceedings of the 3rd International Conference on Educational Data Mining (Pages 269-270). (PDF)
  • Buckley, B.C., Gobert, J., Horwitz, P. & O’Dwyer, L. (2010).
    Looking inside the black box: Assessments and decision-making in BioLogica.
    Int. Journal of Learning Technology,5(2), 166 – 190
    (PDF)
  • Cobern, W., Schuster, D., Adams, B., Undreiu, A., Applegate, B., Skjold, B., Loving, C. & Gobert, J. (2010).
    Experimental Comparison of Inquiry and Direct Instruction in Science.
    Research in Science and Technological Education, 28(1), 81-96.
    (PDF)
  • Horwitz, P., Gobert, J., & Buckley, B., & O’Dwyer, L. (2010).
    Learning Genetics With Dragons: From Computer-Based Manipulatives to Hypermodels.
    In Jacobson, M. J., & Reimann, P. (Eds.). Designs for learning environments of the future: International perspectives from the learning sciences. Springer Publishers, pp.61-87.
    (PDF)
  • Montalvo,O.,Baker, R.S.J.d.,Sao Pedro, M.A.,Nakama, A. & Gobert, J.D.(2010).
    Identifying Students’ Inquiry Planning Using Machine Learning.

    Proceedings of the 3rd International Conference on Educational Data Mining (Pages 141-150). (PDF)
  • Sao Pedro, M.A., Baker, R.S.J.d, Montalvo,O., Nakama, A. & Gobert, J.D.(2010).
    Using Text Replay Tagging to Produce Detectors of Systematic Experimentation Behavior Pattern.

    Proceedings of the 3rd International Conference on Educational Data Mining (Pages 181-190). (PDF)
  • Sao Pedro, M., Gobert, J., & Raziuddin, J. (2010).
    Comparing Pedagogical Approaches for the Acquisition and Long-Term Robustness of the Control of Variables Strategy.
    To appear in the Proceedings of the International Conference of the Learning Sciences, Chicago, IL, June, 2010. (PDF)
  • Sao Pedro, M., Gobert, J., Heffernan, N., & Beck, J. (2009).
    Comparing Pedagogical Approaches for Teaching the Control of Variables Strategy.
    N.A. Taatgen & H. vanRijn (Eds.), Proceedings of the 31st Annual Meeting of the Cognitive Science Society (pp. 1294-1299). Austin, TX: Cognitive Science Society. (PDF)
  • Zalles, D., Gobert, J., Pallant, A., Quellmalz, E. (2007).
    Building Data Literacy, Visualization, and Inquiry in Geoscience Education.
    In the Proceedings of the Environmental Systems Research Institute (ESRI) Education User Conference. Environmental Systems Research Institute, Inc. (PDF)
  • Buckley, B.C, Gobert, J.D.,Horwitz, P. (2006)
    Using Log Files To Track Students’ Model-based Inquiry.
    Proceedings of the Seventh International Conference of the Learning Sciences (ICLS), Mahwah, NJ: Erlbaum, p.57-63. (PDF)
  • Gobert, J. (2005).
    The effects of different learning tasks on conceptual understanding in science: teasing out representational modality of diagramming versus explaining.
    Journal of Geoscience Education, 53(4), 444-455. (PDF)
  • Gobert, J. (2005).
    Leveraging technology and cognitive theory on visualization to promote students’ science learning and literacy.
    In Visualization in Science Education, J. Gilbert (Ed.), pp. 73-90. Springer-Verlag Publishers, Dordrecht, The Netherlands. ISBN 10-1-4020-3612-4. (PDF)
  • Buckley, B.C., Gobert, J.D., Kindfield, A., Horwitz, P., Tinker, R., Gerlits, B., Wilensky, U., Dede, C., & Willett, J. (2004).
    Model-based Teaching and Learning with BioLogica™: What do they learn? How do they learn? How do we know?
    Journal of Science Education and Technology. Vol 13(1), 23-41. (PDF)
  • Gobert, J.D., & Pallant, A., (2004).
    Fostering students’ epistemologies of models via authentic model-based tasks.
    Journal of Science Education and Technology. Vol 13(1), 7-22. (PDF)
  • Gobert, J.D., & R. Tinker (2004).
    Introduction to the Issue.
    Journal of Science Education and Technology. Vol 13(1), 1-6. (PDF)
  • Gilbert, J.K., Treagust, D., & Gobert, J. (2003).
    Science Education: from the past, through the present, to the future.
    International Journal of Science Education, 25 (6), 643-644. (PDF)
  • Gobert, J. & Buckley, B. (2000).
    Special issue editorial: Introduction to model-based teaching and learning.
    International Journal of Science Education, 22(9), 891-894. (PDF)
  • Gobert, J. (2000).
    A typology of models for plate tectonics: Inferential power and barriers to understanding.
    International Journal of Science Education, 22(9), 937-977. (PDF)
  • Gobert, J. & Clement, J. (1999).
    Effects of student-generated diagrams versus student-generated summaries on conceptual understanding of causal and dynamic knowledge in plate tectonics.
    Journal of Research in Science Teaching, 36(1), 39-53. (PDF)
  • Gobert, J. (1999).
    Expertise in the comprehension of architectural plans: Contribution of representation and domain knowledge.
    In Visual And Spatial Reasoning In Design ’99, John S. Gero and B. Tversky (Eds.), Key Centre of Design Computing and Cognition, University of Sydney, AU. (PDF)
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