Project Activities
Structured Abstract
Setting
Sample
Research design and methods
Control condition
Key measures
Data analytic strategy
People and institutions involved
IES program contact(s)
Products and publications
ERIC Citations: Find available citations in ERIC for this award here.
Project Website: https://www.inqits.com/
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Journal articles
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.
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.
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
Pedro, M., Baker, R.D., Gobert, J.D., Montalvo, O., and 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): 1-39. https://doi.org/10.1007/s11257-011-9101-0
Proceedings
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).
Davenport, J., Quellmalz, E., Clarke-Midura, J., Dede, C., Gobert, J., Koedinger, K., McCall, M., & Timms, M. (2012). The Future of Assessment: Measuring Science Reasoning and Inquiry Skills Using Simulations and Immersive Environments. In van Aalst, J., Thompson, K., Jacobson, M. J., & Reimann, P. (Eds.), The Future of Learning: Proceedings of the 10th International Conference of the Learning Sciences (ICLS 2012) Volume 2, Short Papers, Symposia, and Abstracts (pp. 110-117). Sydney, NSW, AUSTRALIA: International Society of the Learning Sciences.
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.
Gobert, J., 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.
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.
Gobert, J., Sao Pedro, M., Montalvo, O., Toto, E., Bachmann, M. & Baker, R. (2011).The Science Assistments Project: Intelligent tutoring for scientific inquiry skills. In L. Carlson, C. Hoelscher, & T. (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
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, pp. 141-150.
Roll, I., Aleven, V., Koedinger, K., Berland, M., Martin, T., Benton, T., Petrick, C., Hershkovitz, A., Wixon, M, Baker, R., Gobert, J., Sao Pedro, M., Sherin, B., Blikstein, P, Worsley, M., & Pea, R. (2012). Building (timely) bridges between learning analytics, educational data mining, and core learning sciences perspectives. In The Future of Learning: Proceedings of the 10th International Conference of the Learning Sciences, pp. 131-141. Sydney, NSW, AUSTRALIA: International Society of the Learning Sciences.
Sao Pedro, M. A., Gobert, J. D., & Raziuddin, J. (2010). Comparing Pedagogical Approaches for the Acquisition and Long-Term Robustness of the Control of Variables Strategy. 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 1, Full Papers (pp. 1024-1031). Chicago, IL: International Society of the Learning Sciences.
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).
Sao Pedro, M., Baker, R., & Gobert, J. (2013). Incorporating Scaffolding and Tutor Context into Bayesian Knowledge Tracing to Predict Inquiry Skill Acquisition. In Proceedings of the 6th International Conference on Educational Data Mining. Memphis, TN, pp 185-192.
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. 190-194. Leuven, Belgium.
Sao Pedro, M., Gobert, J., & Baker, R. (2012). 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.
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.
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).
Sao Pedro, M.A., Gobert, J.D., Betts, C. (2014). Towards Scalable Assessment of Performance-Based Skills: Generalizing a Detector of Systematic Science Inquiry to a Simulation with a Complex Structure. In the Proceedings of the International Conference on Intelligent Tutoring Systems,[pp. 591-600]. Honolulu, HI, Springer: Berlin.
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Supplemental information
Co-Principal Investigators: Heffernan III, Neil T.; Beck, Joseph; Koedinger, Kenneth R.
A major goal of this effort was to develop interactive formative assessments that could assess students' inquiry skills in real-time that could run seamlessly over the web so they also were scalableto large numbers of learners. In addition to the technological infrastructure needed, key development activities include creating formative units that rely heavily on the use of microworlds (i.e., computer-based manipulative models to assess students' science inquiry skills.
The team developed the following assessment materials:
- The Sim Cell (v 1.0 and 2.0) formative assessment unit and corresponding pre- and post-tests — all designed to target inquiry skills in the context of cell structure and function
- The Bug's Life formative assessment unit and corresponding pre- and post-tests— all designed to target inquiry skills in the context of Mendelian genetics
- The EcoLife formative assessment unit and corresponding pre- and post-tests— all designed to target inquiry skills in the context of energy transfer in a food web
For earth science, the team developed the following:
- The Seasons formative assessment unit and corresponding pre- and post-tests— all designed to target inquiry skills in the context of weather systems
- The Plate Tectonics formative assessment unit and corresponding pre- and post-tests— all designed to target inquiry skills in the context of geological phenomena
The team also developed a standardized-style inquiry skills test to be used for assessing students' baseline skills.
The researchers conducted exploratory analyses of students' logfiles to track students' inquiry exploration paths using Markov chain modeling and K-means clustering. They analyzed classroom implementations of the formative assessment units for SimCell, Bug's Life, Ecosystems, Seasons, and Plate Tectonics using t-tests and multivariate analyses of variance (MANOVAs). They used t-tests to analyze classroom implementations of the domain-general inquiry test, and they tested for generalizability of assessment algorithms (from physical science to Ecosystems, and from physical science to Seasons) by doing text replay tagging of students' logfiles (for Ecosystems and for Seasons) and comparing these results to those for physical science. They also used student think-aloud protocols to analyze the pilot tests of SimCell(1.0), Bug's Life, EcoLife, Seasons, and Plate Tectonics.
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