Janice D. Gobert

Janice Gobert, Ph.D., Professor of Educational Psychology develops technology for learning that makes use of AI, including data mining and eye tracking. With seven patents for her technology, her cutting edge work focuses on personalized learning of science and assessment to replace traditional tests, which are unable to assess students’ competencies at authentic science tasks. In essence, the technology makes use of students performing a complex task and tracking metrics on how well they perform science as opposed to reciting rote facts on multiple choice tests. In working on virtual experiments, students can be evaluated in what is called real-time performance assessment. The results and metrics obtained from this technology is then compared to what students write and used to provide each student with the personalized help they need to succeed in the science classroom. By using AI-based, big data techniques, Gobert is able to obtain personalized learning metrics to learn exactly what and how students are learning, which can then trigger the system to provide personalized help via a digital agent named Rex, as well as alert the teacher in real-time so that they can offer support on the spot. Gobert is a strong believer in tailoring content to the student so as to maximize understanding, especially if a student, and one who is underrepresented, is falling behind. This technology is currently in 50 states, with some being research sites and others trial sites.

Gobert studied psychology during her undergraduate years with a deep focus on experimental and cognitive psychology. Propelled by her interest in learning how people understand visually rich diagrams, she went on to pursue her master’s and Doctorate in Cognitive Science. Gobert believes that education has the potential to be a great equalizer and aims to further her goal of deeply investing in future citizens. She currently teaches Psychology of Learning and Inquiry II and constantly looks to bring her students to the cutting edge of what technology can offer them for their research and future career goals. Gobert considers teachers’ love and implementation of her technology to be one of her most significant professional achievements.

• Expertise & Research Interest

  Science Inquiry

  Intelligent Tutoring Systems for Science

  Performance Assessment for Science

• Patents

  Gobert, J. & Toto, E. (September, 2019). An Instruction System with Eyetracking-based Adaptive Scaffolding. Canada Patent no. 2864166 (issued).

  Gobert, J., Sao Pedro, M., Betts, C., & Baker, R.S. (January, 2019). Inquiry skills tutoring system (child patent for additional claims to Inq-ITS). US Patent no. 10,186,168 (issued).

  Gobert, J., Sao Pedro, M., Betts, C., & Baker, R.S. (February, 2017). Inquiry skills tutoring system (child patent for alerting system). US Patent no. 9,564,057 (issued).

  Gobert, J.D., Baker, R.S., & Sao Pedro, M.A. (June, 2016). Inquiry skills tutoring system. US Patent no. 9,373,082 (issued).

  Gobert, J. & Toto, E. (February 22, 2013). An Instruction System with Eyetracking-based Adaptive Scaffolding. US Patent no. 9,230,221 (issued).

  Gobert, J. & Toto, E. (February 22, 2013). An Instruction System with Eyetracking-based Adaptive Scaffolding (child patent for additional use cases). US Patent no. 9,317,115 (issued).

• Recent & Selected Publications

  Gobert, J. D., Sao Pedro, M.A., Betts, C.G. (2023). An AI-Based Teacher Dashboard to Support Students’ Inquiry: Design Principles, Features, and Technological Specifications. In N. Lederman, D. Zeidler, & J. Lederman (Eds.), Handbook of Research on Science Education, (Vol. 3, pp. 1011-1044). Routledge. https://doi.org/10.4324/9780367855758

  Gobert, J.D., Sao Pedro, M.A., Li, H., & Lott, C. (2023). Intelligent Tutoring systems: a history and an example of an ITS for science. In R.Tierney, F. Rizvi, K. Ercikan, & G.  Smith, (Eds.), International Encyclopaedia of Education (Vol. 4, pp. 460-470), Elsevier. Link to PDF

  Dickler, R., Gobert, J., & Sao Pedro, M. (2021). Using Innovative Methods to Explore the Potential of an Alerting Dashboard for Science Inquiry. Journal of Learning Analytics, 8(2), 105-122. https://doi.org/10.18608/jla.2021.7153

  Nicolay, B., Krieger, F., Stadler, M., Gobert, J., & Grieff, S. (2021). Lost in transition – Learning analytics on the transfer from knowledge acquisition to knowledge application in complex problem solving. Computers in Human Behavior, 115. https://doi.org/10.1016/j.chb.2020.106594

  Mislevy, R., Yan, D., Gobert, J., & Sao Pedro, M. (2020). Automated Scoring with Intelligent Tutoring Systems. In Yan, D., Rupp, A. & Foltz, P. (Eds.), Handbook of Automated Scoring: Theory into Practice. Chapman and Hall, London, UK. https://doi.org/10.1201/9781351264808

  Luan, Hui, Geczy Peter, Lai Hollis, Gobert, Janice, Yang Stephen J. H., Ogata Hiroaki, Baltes, Jacky, Guerra Rodrigo, Li Ping, Tsai Chin-Chung. (2020). Challenges and Future Directions of Big Data and Artificial Intelligence in Education. Frontiers in Psychology, 11, 1-11. https://doi.org/10.3389/fpsyg.2020.580820

  Gobert, J., Moussavi, R., Li, H., Sao Pedro, M., & Dickler, R. (2018). Real-time scaffolding of students’ online data interpretation during inquiry with inq-its using educational data mining. Invited book chapter in Abul K.M. Azad, Michael Auer, Arthur Edwards, and Ton de Jong (Eds), Cyber-Physical Laboratories in Engineering and Science Education. Springer. doi: 0.1007/978-3-319-76935-6_8

  Li, H., Graesser, A.C., & Gobert, J. (2017). Where is embodiment hidden in the intelligent tutoring system? Journal of South China Normal University, 3, 79-91. Link to PDF

  Gobert, J.D., & Sao Pedro, M.A. (2017). Digital Assessment Environments for Scientific Inquiry Practices. In Rupp, A.A. & Leighton, J.P (Eds.) The Wiley Handbook of Cognition and Assessment: Frameworks, Methodologies, and
  Applications. West Sussex, UK. 508-534. Link to PDF

  Gobert, J.D., Kim, Y.J, Sao Pedro, M.A., Kennedy, M., & Betts, C.G. (2015). Using educational data mining to assess students’ skills at designing and conducting experiments within a complex systems microworld. Thinking Skills and Creativity, 18, 81-90. doi: 10.1016/j.tsc.2015.04.008

  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), 789-802. doi: 10.1007/s10956-015-9564-6

  Gobert, J.D., Baker, R., & Wixon, M. (2015). Operationalizing and Detecting Disengagement Within On-Line Science Microworlds. Educational Psychologist, 50(1), 43-57. https://doi.org/10.1080/00461520.2014.999919

  ​​Gobert, J., Sao Pedro, M., Raziuddin, J., and Baker, R. S., (2013). From log files to assessment metrics for science inquiry using educational data mining. Journal of the Learning Sciences, 22(4), 521-563. doi: 10.1080/10508406.2013.837391

  Hershkovitz, A., Baker, R.S.J.d., Gobert, J., Wixon, M., Sao Pedro, M. (2013). Discovery with models: A case study on carelessness in computer-based science inquiry. American Behavioral Scientist, 57 (10), 1479-1498. doi:10.1177/0002764213479365

  Gobert, J., Wild, S., & Rossi, L. (2012). Examining Geoscience Learning with Google Earth: Testing the Effects of Prior Coursework and Gender. Special issue on Google Earth and Virtual Visualizations in Geoscience Education and Research. Geological Society of America, Special Paper 492, 453-468. doi:10.1130/2012.2492(35)

  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. https://doi.org/10.5281/zenodo.3554645

  Timms, Michael, Clements, Douglas H., Gobert, Janice, Ketelhut, Diane J., Lester, James, Reese, Debbie D., and Wiebe, Eric. (2012). New measurement paradigms. Report to the National Science Foundation. http://research.acer.edu.au/ar_misc/9. 

  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. doi:10.1080/09500691003720671

  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. doi:10.1504/IJLT.2010.037309

  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. doi:10.1080/02635140903513599

  Buckley, B.C., Gobert, J., Horwitz, P. & O’Dwyer, L. (2010). Looking inside the black box: Assessments and decision-making in BioLogica. International Journal of Learning Technologies, 5(2), 166 – 190. doi:10.1504/IJLT.2010.034548

  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.  Link to 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. Link to 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. doi: 10.1007/1-4020-3613-2_6

  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. Link to 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.  doi:10.1023/B:JOST.0000019635.70068.6f

  Gobert, J.D., & R. Tinker (2004). Introduction to the Issue. Journal of Science Education and Technology, Vol 13(1), 1-6.

  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. 643-644. doi:10.1080/09500690305019

  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. https://doi.org/10.1080/095006900416857

  Gobert, J. & Buckley, B. (2000). Special issue editorial: Introduction to model-based teaching and learning. International Journal of Science Education, 22(9), 891-894. Link to article 

  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. Link to 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. https://doi.org/10.1002/(SICI)1098-2736(199901)36:1<39::AID-TEA4>3.0.CO;2-I

• Honors & Awards

  Gobert, J.D. (2022, August 4). Panelist for the White House Office of Science and Technology Policy and the U.S. Department of Education to advise on the future of Artificial Intelligence in Education.

  Gobert, J.D. (2022). Expert panelist, AI and the Future of Skills, OECD.

  Gobert, J.D. 2021 Nominee, Grawemeyer Award in Education. http://grawemeyer.org/education/ (competition postponed due to COVID-19)

  Honorable Mention, AERA Cognition and Assessment SIG, 2015

  James Chen Best Student Paper Award, Michael Sao Pedro (advisee), 20^th Conference on User Modeling, Adaptation, and Personalization, 2012

  Best Student Paper Award, Michael Sao Pedro (advisee), AERA SIG on Advanced Learning Technologies, 2012

  Best Interactive Event, 10^th International Conference on Intelligent Tutoring Systems, 2010

  Young Researchers’ Award, Michael Sao Pedro (advisee), 10^th International Conference on Intelligent Tutoring Systems, 2010

  Research Development Award, Western Michigan University, 1997

  Dean’s Appreciation Award, Western Michigan University, 1996

• Recent & Selected Grants

  Developing & Testing Real-time Assessment & Scaffolding for Mathematics Use & Modeling During Science Inquiry (R305A210432). Gobert, J. (Principal Investigator) & Sao Pedro, M. (Co- Investigator). Awarded May, 2021 by the U.S. Dept. of Education, $1,905,787.00

  Inq-Blotter: Designing Supports for Teachers’ Real Time Instruction (NSF-IIS- 1902647). Gobert, J. (Principal Investigator), Awarded July 17, 2019 from the National Science Foundation, $299,999.00

  Inq-Blotter – A Real Time Alerting Tool to Transform Teachers’ Assessment of Science Inquiry Practices, (NSF-IIS-1629045). Gobert, J. (Principal Investigator), M. Sao Pedro (Co-Principal Investigator). Awarded August 2016 from the National Science Foundation, $550,000.00

  Testing the Effects of Real-time Scaffolding of Science Inquiry Driven by Automated Performance Assessment, (NSF-DRL-1252477), Gobert, J., Principal Investigator, Awarded September 2013 from the National Science Foundation, $1,499,346.00

  The Development of an Intelligent Pedagogical Agent for Physical Science Inquiry Driven by Educational Data Mining. Gobert, J. & Baker, R. Proposal (R305A120778) Awarded May, 2012 by the U.S. Dept. of Education, $1,499,588.00

  Empirical Research: Emerging Research: Using Automated Detectors to Examine the Relationships Between Learner Attributes and Behaviors During Inquiry in Science (NSF-DRL# 1008649). Janice Gobert, Principal Investigator, Ryan Baker, Co-Principal Investigator. Awarded July 1, 2010 from the National Science Foundation; $986,111.00