The Research Behind Inq-ITS
Inq-ITS has been thoroughly researched and tested to ensure that it helps students, teachers, and schools in their science education. Our research was funded by both the National Science Foundation and US Department of Education.
Inq-ITS Rigorously Assesses Lab Work AND Writing
Cutting-edge assessment algorithms auto-grade how students execute science practices, instead of using multiple-choice or fill-in-the-blank. Algorithms score practices like forming questions, collecting data, interpreting data, and warranting claims with evidence. Our algorithms were tested across multiple inquiry activities and diverse students and match human hand-grading with high precision (ranging from 84% to 99%; Gobert et al. 2012, 2013; Sao Pedro et al. 2013a,b, 2014). Our natural language processing algorithms evaluate how students write about their lab work using the “Claim-Evidence-Reasoning” framework. Our algorithms correlate with human scoring between 84% and 97% (Li et al., 2017b).
Why Performance Assessment: Writing Alone Is Not Enough
New science standards require students to be highly proficient at both “doing” science and “writing about” science. Using students’ writing alone is not enough to determine what they know and can do. Between 30% and 50% of students are mis-assessed based on writing alone (Gobert, US News & World Report, 2016). Many students parrot shallow knowledge with no understanding, and many others can demonstrate science skills but cannot express what they know in words (Li et al. 2017a,b; Li et al. 2018a,c,d,e). Inq-ITS assesses and helps develop both competencies.
Inq-ITS AI Virtual Coach Helps Students Learn and Transfer Competencies
Our built-in artificial intelligence-driven virtual coach helps students get better at science practices. Several randomized controlled studies show that the virtual coach helps students learn science practices (e.g. Sao Pedro et al. 2013b; Moussavi et al. 2016) and also helps them transfer their knowledge of the practices to different science topics and domains (e.g. Sao Pedro et al. 2013b; Li et al. 2019), even after 180 days (Li et al. 2019).
Inq-Blotter Alerts Enable Teachers to Help More Students More Effectively
Inq-Blotter alerts teachers exactly when and exactly how and individual student (or the whole class) struggles at inquiry. In a randomized controlled pilot study, middle school teachers who used Inq-Blotter were able to help twice as many students than teachers who did not. The help given by teachers (triggered by Inq-Blotter) improved student performance on their next Inq-ITS lab, particularly for interpreting data and warranting claims competencies (Sao Pedro et al. 2019).
Research Shows
Our Spring 2020 students either maintained or significantly improved their science inquiry competencies!
Our Research Publications
Adair, A., Sao Pedro, M., Gobert, J., & Owens, J. A. (2023, June). Assessing students’ competencies with mathematical models in virtual science inquiry investigations. Presented at the 2023 International Conference of the Learning Sciences, Montréal, QC, Canada.
Adair, A., Segan, E., Gobert, J., & Sao Pedro, M. (2023, June). Real-time AI-driven assessment & scaffolding that improves students’ mathematical modeling during science inquiry. Presented at the 2023 International Conference on Artificial Intelligence in Education (AIEd), Tokyo, Japan.
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, J., & 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. https://doi.org/10.1016/B978-0-12-818630-5.10058-2.
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
Dickler, R., Sao Pedro, M., Adair, A., Gobert, J., Betts, C., Staudenraus, C., Kleban, J., Rowan, P., Olsen, J., & Lee., J. (2021). Supporting student scientists remotely: Integrating mathematics and science in virtual labs. Presented at the Proceedings of the International Conference of the Learning Sciences 2021. https://repository.isls.org//handle/1/7372
Dickler, R., O’Brien, M., Gobert, J., Olsen, J., Adair, H, & Hussain-Abidi, H. (2021).Analyzing student-teacher discourse prompted by a real-time alerting dashboard for science inquiry practices. Presented at the 2021 American Educational Research Association (AERA): Advanced Technologies for Learning. doi: 10.3102/1689057
Adair, A., Dickler, R., & Gobert, J. (2021). Intelligent tutoring system supports students maintaining their science inquiry competencies during remote learning due to COVID-19. Presented at 2021 American Educational Research Association (AERA): Learning and Instruction.
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
Dickler, R. & Gobert, J. (2020). Comparing robustness of teacher support patterns in response to dashboard alerts using epistemic network analyses. Presented at American Educational Research Association (AERA): Advanced Technologies for Learning, San Francisco, CA. doi: 10.3102/1576680
Dickler, R., Gobert, J., Staudenraus, C., & Levy-Cohen, R. (2020).
Exploring the transfer of students’ competencies from virtual inquiry to hands-on inquiry. Presented at American Educational Research Association (AERA): Learning and Instruction, San Francisco, CA. doi: 10.3102/1576716
Adair, A. & Dickler, R. (2020). Supporting teachers supporting students: evidence-based TIPS in a dashboard to guide inquiry scaffolding. In, Proceedings of the International Conference of the Learning Sciences 2020 (pp.1769-1770). ISLS. Link to PDF
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
Dickler, R., Li, H., & Gobert, J. (2019). A data-driven approach for automated assessment of scientific explanations in science inquiry. In Proceedings of the Twelfth International Conference on Educational Data Mining (pp. 536-539). Montreal, Canada: EDM Society.
Dickler, R. (2019). An intelligent tutoring system and teacher dashboard to support mathematizing during science inquiry. In S. Isotani, E.
Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Artificial Intelligence in Education (pp.332-338). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-23207-8_61
Li, H., Gobert, J., & Dickler, R. (2019). Evaluating the transfer of scaffolded inquiry: What sticks and does it last? In S. Isotani, E. Millán, A.
Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Artificial Intelligence in Education (pp. 163-168). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-23207-8_31
Li, H., Gobert, J., & Dickler, R. (2019). Scaffolding during science inquiry. In J.C. Mitchell, K. Porayska-Pomsta, & D. Joyner (Eds.), Proceedings of the Sixth Annual ACM Conference on Learning at Scale (pp. 1-10). New York, NY: ACM. doi: 10.1145/3330430.3333628
Li, H., Gobert, J., & Dickler, R. (2019). Testing the robustness of inquiry practices once scaffolding is removed. In A. Coy, Y. Hayashi, & M.
Chang, Proceedings of the Fifteenth International Conference on Intelligent Tutoring Systems (pp. 204-213). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-22244-4_25
Dickler, R., Li, H., & Gobert, J. (2019). Teacher scaffolds mediated by a science inquiry dashboard. Presented at the European
Science Education Research Association, Bologna, Italy.
Li, H., Gobert, J., & Dickler, R. (2019). Assessing students’ science inquiry practices and dusting off the messy middle. Presented at the European Science Education Research Association, Bologna, Italy.
Dickler, R., Gobert, J., & Sao Pedro, M. (2019). Using epistemic network analysis to characterize teacher discourse in response to an alerting
dashboard. Presented at Society for Text and Discourse, New York, NY.
Li, H., Gobert, J., & Dickler, R. (2019). Scientific explanations: Does practice make perfect? Presented at Society for Text and Discourse, New York, NY. Link to PDF
Dickler, R., & Gobert, J. (2019). Examining teacher discourse in response to dashboard alerts using epistemic network analyses. Presented at the Annual Subway Summit, New York, NY.
Gobert, J., Dickler, R., Staudenraus, C., & Levy-Cohen, R. (2019).
Examining transfer of inquiry practices from Inq-ITS virtual labs to hands on inquiry. Presented at the Annual Subway Summit, New York, NY.
Gobert, J., Li, H., & Dickler, R. (2019). Dusting off the messy middle: Comparing students’ science investigation competencies with their
writing competencies. Presented at the 3rd International Conference on AI + Adaptive Education, Beijing, China.
Dickler, R., Li, H., & Gobert, J. (2019). Examining the generalizability of an automated scoring method and identifying student difficulties
with scientific explanations. Presented at American Educational Research Association (AERA): Learning and Instruction, Toronto, Canada. doi: 10.13140/RG.2.2.33179.52006
Li, H., Gobert, J., & Dickler, R. (2019).
Comparison of automated scoring methods for scientific explanations. Presented at American Educational Research Association (AERA): Sig-Cognition and Assessment, Toronto, Canada. doi: 10.13140/RG.2.2.36534.96325
Sao Pedro, M. A., Gobert, J., & Dickler, R. (2019). Can an alerting teacher dashboard improve how teachers help their students learn science inquiry practices? Presented at American Educational Research Association (AERA): Learning and Instruction. Toronto, Canada. doi: 10.3102/1433374
Dickler, R., Li, H., & Gobert, J. (2018). False positives and false negatives in inquiry assessment: Investigating log and open response data. Presented at the European Association of Research on Learning and Instruction Sig 20 and
Sig 26 2018 Meeting, The Hebrew University of Jerusalem, Jerusalem, Israel.
Li, H., Gobert, J., & Dickler, R. (2018). Automatically assessing scientific explanations in online inquiry. Presented at EARLI (European Association for Research on Learning and Instruction): SIG 20 and 26 Conference, Jerusalem, Israel.
Li, H., Gobert, J., Dickler, R., & Moussavi, R. (2018). The impact of multiple real-time scaffolding experiences on science inquiry practices. In Lecture
Notes in Computer Science (pp. 99-109). Springer. doi: 10.1007/978-3-319-91464-0_10
Li, H., Gobert, J., & Dickler, R. (2018). The relationship between scientific explanations and the proficiencies of content, inquiry, and writing. In, Proceedings of the Fifth Annual ACM Conference on Learning at Scale (p. 12-22). ACM. doi: 10.1145/3231644.3231660
Li, H., Gobert, J., & Dickler, R. (2018). Unpacking why student writing does not match their science inquiry experimentation in Inq-ITS. In J. Kay & R. Luckin (Eds.), Rethinking learning in the digital age: Making the learning sciences count, 13th International Conference of the Learning Sciences (ICLS) 2018: (Vol. 3, pp. 1465–1466). London, UK: International Society of the Learning Science. https://repository.isls.org//handle/1/681
Li, H., Gobert, J., Dickler, R., & Morad, N. (2018). Students’ academic language use when constructing scientific explanations in an intelligent
tutoring system. In C. P. Rosé, R. Martínez-Maldonado, H. U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren, & B. du Boulay (Eds.), Artificial Intelligence in Education (AIED): 19th International Conference, AIED 2018 (Vol. 1, pp. 267–281). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-93843-1_20
Li, H., Gobert, J., Graesser, A., & Dickler, R. (2018). Advanced Educational Technology for Science Inquiry Assessment. Policy Insights from the
Behavioral and Brain Sciences, 5(2), 171-178. doi: 10.1177/2372732218790017
Moussavi, R. (2018). Design, development, and evaluation of scaffolds for data interpretation practices during inquiry. Doctoral dissertation. Worcester, MA: Worcester Polytechnic Institute.
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
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
Li, H., Gobert, J., & Dickler, R. (2017). Dusting Off the Messy Middle: Assessing Students’ Inquiry Skills Through Doing and Writing. In
Proceedings of the 18th International Conference on Artificial Intelligence in Education. Wuhan, China (pp. 175-187). doi: 10.1007/978-3-319-61425-0_15
Li, H., Gobert, J., & Dickler, R. (2017). Automated Assessment for Scientific Explanations in On-line Science Inquiry. In Proceedings of the 10th International Conference on Educational Data Mining. Wuhan, China (pp. 214-219). Link to PDF
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
Moussavi, R., Gobert, J., and Sao Pedro, M. (2016). The Effect of Scaffolding on the Immediate Transfer of Students' Data Interpretation Skills within
Science Topics. In Proceedings of the 12th International Conference of the Learning Sciences. Singapore. (pp. 1002-1006). https://repository.isls.org//handle/1/364
Moussavi, R. and Gobert, J. (2016). Iterative Design, Development, and Evaluation of Scaffolds for Data Interpretation Practices during
Inquiry. Presented as part of the Doctoral Consortium. In Proceedings of the 12th International Conference of the Learning Sciences, (p. 1404). Singapore. Link to PDF
Gobert, J.D., Kim, Y.J, Sao Pedro, M.A.,Kennedy, M., and 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, 1-10. https://doi.org/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. S., and Wixon, M. (2015). Operationalizing and Detecting Disengagement During On-Line Science Inquiry. In Educational Psychologist, 50:1, 43-57. doi: 10.1080/00461520.2014.999919
Sao Pedro, M., Gobert, J., Toto, E., & Paquette, L. (April, 2015). Assessing Transfer of Students’ Data Analysis Skills across Physical Science Simulations. Presented as part of Bejar, I. et al.’s symposium on The State of the Art in Automated Scoring of Science Inquiry Tasks at the Annual Meeting of the American Education Research Association. Chicago, IL.
Moussavi, R., Kennedy, M., Sao Pedro, M.A., Gobert, J.D. (April, 2015).
Evaluating a Scaffolding Design to Automatically Support Students’ Data Interpretation within a Simulation-Based Inquiry Environment. Presented at the Annual Meeting of the American Educational Research Association, April 2015, Chicago, IL
Sao Pedro, M., Jiang, Y., Paquette, L., Baker, R.S. & Gobert, J. (2014).
Identifying Transfer of Inquiry Skills across Physical Science Simulations using Educational Data Mining. In Proceedings of the 11th International Conference of the Learning Sciences. Boulder, CO (pp. 222-229). https://repository.isls.org//handle/1/1116
Sao Pedro, M.A., Gobert, J.D., & Betts, C.G. (2014). Towards Scalable Assessment of Performance-Based Skills: Generalizing a Detector of Systematic Science Inquiry to a Simulation with a Complex Structure. In Proceedings of the 12th International Conference on Intelligent Tutoring Systems. Honolulu, HI (pp. 591-600). doi: 10.1007/978-3-319-07221-0_75
Paquette, L., Baker, R.S., Sao Pedro, M.A., Gobert, J.D., Rossi, L., Nakama, A., Kauffman-Rogoff, Z. (2014). Sensor-Free Affect Detection for a Simulation-Based Science Inquiry Learning Environment. In Proceedings of the 12th International Conference on Intelligent Tutoring Systems. Honolulu, HI (pp. 1-10). doi: 10.1007/978-3-319-07221-0_1
Sao Pedro, M.A., Gobert, J.D., & Baker, R.S. (2014). Impacts of Automatic Scaffolding on Students’ Acquisition of Data Collection Inquiry Skills. Presented at the Annual Meeting of the American Education Research Association. Philadelphia, PA. Link to PDF
Gobert, J., Sao Pedro, M., & Betts, C. (April, 2014). Using Educational Data Mining to Assess Students’ Experimentation Skills During Inquiry Within
Complex Systems. Presented at The Annual Meeting of the American Educational Research Association, Philadelphia, PA, April, 2014. https://doi.org/10.1016/j.tsc.2015.04.008
Gobert, J. D., Sao Pedro, M., Raziuddin, J., and 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: 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
Sao Pedro, M.A., Baker, R.S.J.d., Gobert, J., Montalvo, and O. Nakama, A. (2013). Leveraging Machine-Learned Detectors of Systematic Inquiry Behavior to Estimate and Predict Transfer of Inquiry Skill. User Modeling and User-Adapted Interaction, 23, 1-39. doi: 10.1007/s11257-011-9101-0
Sao Pedro, M., Baker, R., & Gobert, J. (2013). Incorporating Scaffolding and Tutor Context into Bayesian Knowledge Tracing to Predict Inquiry Skill Acquisition. In S.K. D'Mello, R.A. Calvo, & A. Olney (Eds.) Proceedings of the 6th International Conference on Educational Data Mining, (pp. 185-192). Memphis, TN. Link to PDF
Gobert, J., Sao Pedro, M., Raziuddin, J., & Baker, R. (2013). Developing and Validating EDM (Educational Data Mining)-Based Assessment Measures for
Measuring Science Inquiry Skill Acquisition and Transfer Across Science Topics. Presented at The Annual Meeting of the American Educational Research Association. San Francisco, CA.
Gobert, J., Raziuddin, J., & Koedinger, K. (2013). Auto-scoring discovery and confirmation bias during data interpretation in a science microworld. Artificial Intelligence in Education Lecture Notes in Computer Science, Volume 7926, pp 770-773. doi:10.1007/978-3-642-39112-5_109
Sao Pedro, M., Baker, R., & Gobert, J. (2013). What Different Kinds of Stratification Can Reveal about the Generalizability of Data-Mined Skill
Assessment Models. In Proceedings of the 3rd Conference on Learning Analytics and Knowledge. Leuven, Belgium. https://doi.org/10.1145/2460296.2460334
Gobert, J., Toto, E., Brigham, M., & Sao Pedro, M. (2013). Searching for Predictors of Learning Outcomes in Non Abstract Eye Movement Logs. In H.C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.) Proceedings of the 16th International Conference on Artificial Intelligence in Education, (pp. 799-802). Memphis, TN. doi: 10.1007/978-3-642-39112-5_116
Gobert, J., Sao Pedro, M., Baker, R.S., 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, Article 15, Volume 4, 153-185. https://doi.org/10.5281/zenodo.3554645
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)
Hershkovitz, A, Baker, R.S.J.d., Gobert, J., & Nakama, A. (2012). A data-driven path model of student attributes, affect, and engagement in a computer-based science inquiry microworld. In the Proceedings of the Tenth International Conference of the Learning Sciences, 2-6 July, Sydney, Australia. Link to Article
Sao Pedro, M., Baker, R., & Gobert, J. (2012). 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. https://doi.org/10.1007/978-3-642-31454-4_21
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. Link to article
Timms, M., Clements, D. H., Gobert, J., Ketelhut, D. J., Lester, J., Reese, D. D., & Wiebe, E. (2012). New measurement paradigms. Report to the National Science Foundation. Link to PDF
Wixon, M., Baker. S., Gobert, J., Ocumpaugh, J, & Bachmann, M. (2012). WTF? Detecting students who are not thinking fastidiously. In Lecture Notes in Computer Science, LNCS, Volume 7379, pp. 286-296. Link to PDF
Gobert, J., Raziuddin, J., and Sao Pedro, M. (2011). The Influence of Learner Characteristics on Conducting Scientific Inquiry Within Microworlds.
In L. Carlson, C. Hoelscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. Link to article
Sao Pedro, M., Gobert, J., and Sebuwufu, P. (2011, April 10). The Effects of Quality Self-Explanations on Robust Understanding of the Control of Variables
Strategy. Presented at The Annual Meeting of the American Educational Research Association. New Orleans, LA: Retrieved April 18, 2011, from the AERA Online Paper Repository. Link to 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. User Modeling and User-Adapted Interaction. doi: 10.1007/s11257-011-9101-0
Gobert, J., O’Dwyer, L., Horwitz, P., Buckley, B., Levy, S.T. & Wilensky, U. (2011). Examining the relationship between students’ understanding of the nature of models and conceptual learning in Biology, Physics, & Chemistry. International Journal of Science Education, 33(5), 653-684. doi:10.1080/09500691003720671
Gobert, J., Sao Pedro, M., Toto, E., Montalvo, O., and Baker, R. (2011).
Science ASSISTments: Assessing and scaffolding students' inquiry skills in real time. 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., and Montalvo, O. (2011).
Science ASSISTments: Using student logs, machine learning, and Data Mining to determine when & how to scaffold students' science inquiry. Presented at The Annual Meeting of the American Educational Research Association, April, 2011, New Orleans, LA.
Hershkovitz, A., Wixon, M., Baker, R.S.J.d., Gobert, J., & Sao Pedro, M. (2011). Carelessness and Goal Orientation in a Science Microworld. In G. Biswas et al. (Eds.): AIED 2011, LNAI 6738 (pp. 462-465). Heidelberg, Germany: Springer. doi:10.1007/978-3-642-21869-9_70
Hershkovitz, A., Baker R.S.J.d., Gobert, J., & Wixon, M. (2011). Goal Orientation and Changes of Carelessness over Consecutive Trials in Science Inquiry. In the Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011), pp. 315-316, Eindhoven, The Netherlands. Link to article
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
Bachmann, M., Gobert, J.D., and Beck, J. (2010). Tracking Students’ Inquiry Paths through Student Transition Analysis. Proceedings of the 3rd International Conference on Educational Data Mining (Pages 269-270). Link to article
Gobert, J., Montalvo, O., Toto, E., Sao Pedro, M., & Baker, R. (2010). The Science Assistments Project: Scaffolding scientific inquiry skills. In Aleven, V., Kay, J. & Mostow, J. (Eds.) Intelligent Tutoring Systems Conference (6095), p. 445, Springer Berlin / Heidelberg. doi:10.1007/978-3-642-13437-1_102
Gobert, J., Sao Pedro, M. Raziuddin, J., & the Science Assistments Team (2010). Studying the interaction between learner characteristics and inquiry skills in microworlds. In K. Gomez, L. Lyons, & J. Radinsky (Ed.), Learning in the Disciplines: Proceedings of the 9th International Conference of the Learning Sciences (ICLS 2010) - Volume 2 (p. 46). Chicago, IL: International Society of the Learning Sciences.
Montalvo, O., Baker, R.S.J.d., Sao Pedro, M.A., Nakama, A. and Gobert, J.D. (2010). Identifying Students’ Inquiry Planning Using Machine Learning.
Proceedings of the 3rd International Conference on Educational Data Mining (Pages 141-150). Link to article
Sao Pedro, M.A., Baker, R.S.J.d, Montalvo, O., Nakama, A. and 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). Link to article
Sao Pedro, M., Gobert, J., and Raziuddin, J. (2010). Comparing Pedagogical Approaches for the Acquisition and Long-Term Robustness of the Control
of Variables Strategy. Proceedings of the International Conference of the Learning Sciences, Chicago. https://repository.isls.org//handle/1/2640
Sao Pedro, M., Gobert, J., & Raziuddin, J. (2010). Long-term Benefits of Direct Instruction with Reification for Learning the Control of Variables Strategy. In Aleven, V., Kay, J. & Mostow, J. (Eds.) Intelligent Tutoring Systems Conference (6095), pp. 257-259. Springer Berlin/ Heidelberg. doi:10.1007/978-3-642-13437-1_37
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
Pence, N., Weisbrot, E., Whitmeyer, S., De Paor, D. & Gobert, J. (2010). Using Google Earth for Advanced Learning in the Geosciences. Geological Society of America Abstracts with Programs, Vol. 42, No. 1, p. 115.
De Paor, D.G., Whitmeyer, S., and Gobert, J. (2009). Development, Deployment, and Assessment of Dynamic Geological and Geophysical Models Using the Google Earth APP & API: Implications for Undergraduate Education in the Earth and Planetary Sciences. Eos Trans. AGU, 90(52), Fall Meet. Suppl., Abstract ED53E-07.
Selkin, P.A., De Paor, D.G., Gobert, J., Kirk, K.B., Kluge, S., Richard, G.A., and Whitmeyer, S.J. (2009). Emerging Digital Technologies for Geoscience Education and Outreach. GSA Abstracts with Programs, v. 41, no.6.
Rai, D., Heffernan, N., Gobert, J., & Beck, J. (2009). Mily’s World: Math game involving authentic activities in visual cover story. In the Proceedings of 14th Annual Conference of Artificial Intelligence in Education, pp. 125- 128, July 6-10, Brighton, UK.
Sao Pedro, M., Gobert, J., Heffernan, N., & Beck, J. (2009). Comparing Pedagogical Approaches for Teaching the Control of Variables Strategy. In 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. Link to article
DePaor, D., Whitmeyer, S. & Gobert, J. (2008). Emergent Models for Teaching Geology and Geophysics Using Google Earth , Eos Trans. AGU, 89(53). Link to article
Quellmalz, E.S., DeBarger, A.H., Haertel, G., Schank, P., Buckley, B., Gobert, J., Horwitz, P., & Ayala, C. (2008). Exploring the Role of Technology-Based Simulations in Science Assessment: The Calipers Project. In Science Assessment: Research And Practical Approaches, pp. 191-202. Washington, DC: NSTA. Link to 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. Link to PDF
Buckley, B., Gobert, J., Horwitz, P., & Mansfield, A. (2006). Using Log files to Track Students’ Model-based Inquiry. In the Proceedings of the Seventh International Conference of the Learning Sciences (ICLS), Mawah: NJ: Erlbaum, pp.57-63. 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
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., Horwitz, P., Buckley, B., Mansfield, A., Burke, E., & Markman, D. (2005). Logging Students’ Model-Based Learning and Inquiry Skills in Science. In the Proceedings of the American Association of Artificial Intelligence Technical Report WS-05-02, p. 67. AAAI Press, Menlo Park, CA. Link to article
Manduca, C.A., J. Gobert, P. Laws, D.W. Mogk, and S.J. Reynolds (2005). Observing and Assessing Student Learning: A Workshop Report. Geological Society of America Abstracts with Programs, 37(7): 283. Link to 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. 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.
Gobert, J. (2003). Collaborative Model-Building and Peer Critique Online. In the Proceedings of the Twenty-fifth Annual Meeting of the Cognitive Science Society, July 31-August 2, Boston, MA. Link to 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. doi:10.1080/09500690305019
Gobert, J., Horwitz, P., Tinker, B., Buckley, B., Wilensky, U., Levy, S., and Dede, C. (2003). Modeling Across the Curriculum: Scaling up Modeling Using Technology. In the Proceedings of the Twenty-fifth Annual Meeting of the Cognitive Science Society, July 31-August 2, Boston, MA. Link to PDF
Gobert, J., Slotta, J., & Pallant, A. (2002). Collaborative Model-Building and Peer Critique via the Internet. In P. Bell, R. Stevens, & T. Satwicz (Eds.), Keeping Learning Complex: The Proceedings of the Fifth International Conference of the Learning Sciences (ICLS), pp. 536-537. Mawah: NJ: Erlbaum.
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. (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. (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
Gobert, J. (1997). Summarizing, Explaining, and Diagramming: The Differential Effects on Text-Base Representations and Mental Models. Proceedings of the Nineteenth Annual Meeting of the Cognitive Science Society. Stanford University. August 7-10, Palo Alto, CA.
Gobert, J. (1994). Reasoning and inference-making with architectural plans. In N.H. Narayanan (Ed.), Reasoning with Diagrammatic Representations. American Association of Artificial Intelligence Technical Report SS-92-02, AAAI Press, Menlo Park, CA.
Gobert, J. (1993). The comprehension of complex graphics: facilitating effects of text on integration and inference-making. Proceedings of the Fifteenth Annual Meeting of the Cognitive Science Society. University of Colorado at Boulder, June 18-21, Boulder, CO.
Gobert, J. & Coleman, E.B. (1993). Using diagrammatic representations and causal explanations to investigate children's models of continental drift. Proceedings of the Society of Research in Child Development. March 25-28, New Orleans, LA.
Gobert, J. (1992). Reasoning and inference-making with architectural plans. Working notes of the Spring Symposium on Reasoning with Diagrammatic Representations. American Association for Artificial Intelligence. Stanford University, March 25-27, Stanford, CA.
Gobert, J. (1991). The use of textual organizers to enhance the comprehension of graphic information. Canadian Psychology, 32(2a), p. 381.
Gobert, J. & Frederiksen, C. (1988). The comprehension of architectural plans by expert and sub-expert architects. Proceedings of the Tenth Annual Meeting of the Cognitive Science Society. Montreal, Canada, August 17-19, Hillsdale, NJ.: Lawrence Erlbaum.