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Real-Time AI-Driven Assessment and Scaffolding that Improves Students’ Mathematical Modeling during Science Investigations

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Artificial Intelligence in Education (AIED 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13916))

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Abstract

Developing models and using mathematics are two key practices in internationally recognized science education standards, such as the Next Generation Science Standards (NGSS) [1]. However, students often struggle at the intersection of these practices, i.e., developing mathematical models about scientific phenomena. In this paper, we present the design and initial classroom test of AI-scaffolded virtual labs that help students practice these competencies. The labs automatically assess fine-grained sub-components of students’ mathematical modeling competencies based on the actions they take to build their mathematical models within the labs. We describe how we leveraged underlying machine-learned and knowledge-engineered algorithms to trigger scaffolds, delivered proactively by a pedagogical agent, that address students’ individual difficulties as they work. Results show that students who received automated scaffolds for a given practice on their first virtual lab improved on that practice for the next virtual lab on the same science topic in a different scenario (a near-transfer task). These findings suggest that real-time automated scaffolds based on fine-grained assessment data can help students improve on mathematical modeling.

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References

  1. Next Generation Science Standards Lead States: Next Generation Science Standards: For States, By States. National Academies Press, Washington (2013)

    Google Scholar 

  2. National Science Board: Science and engineering indicators digest 2016 (NSB-2016-2). National Science Foundation, Arlington, VA (2016)

    Google Scholar 

  3. Gottfried, M.A., Bozick, R.: Supporting the STEM pipeline: linking applied STEM course-taking in high school to declaring a STEM major in college. Educ. Fin. Pol. 11, 177–202 (2016)

    Article  Google Scholar 

  4. Potgieter, M., Harding, A., Engelbrecht, J.: Transfer of algebraic and graphical thinking between mathematics and chemistry. J. Res. Sci. Teach. 45(2), 197–218 (2008)

    Article  Google Scholar 

  5. Sadler, P.M., Tai, R.H.: The two high-school pillars supporting college science. Sci. Educ. 85(2), 111–136 (2007)

    Article  Google Scholar 

  6. Glazer, N.: Challenges with graph interpretation: a review of the literature. Stud. Sci. Educ. 47, 183–210 (2011)

    Article  Google Scholar 

  7. Lai, K., Cabrera, J., Vitale, J.M., Madhok, J., Tinker, R., Linn, M.C.: Measuring graph comprehension, critique, and construction in science. J. Sci. Educ. Technol. 25(4), 665–681 (2016)

    Article  Google Scholar 

  8. Nixon, R. S., Godfrey, T. J., Mayhew, N. T., Wiegert, C. C.: Undergraduate student construction and interpretation of graphs in physics lab activities. Physical Review Physics Education Research 12(1), (2016).

    Google Scholar 

  9. Casey, S.A.: Examining student conceptions of covariation: a focus on the line of best fit. J. Stat. Educ. 23(1), 1–33 (2015)

    Article  MathSciNet  Google Scholar 

  10. De Bock, D., Neyens, D., Van Dooren, W.: Students’ ability to connect function properties to different types of elementary functions: an empirical study on the role of external representations. Int. J. Sci. Math. Educ. 15(5), 939–955 (2017)

    Article  Google Scholar 

  11. Penuel, W.R., Turner, M.L., Jacobs, J.K., Van Horne, K., Sumner, T.: Developing tasks to assess phenomenon-based science learning: challenges and lessons learned from building proximal transfer tasks. Sci. Educ. 103(6), 1367–1395 (2019)

    Article  Google Scholar 

  12. Furtak, E.M.: Confronting dilemmas posed by three-dimensional classroom assessment. Sci. Educ. 101(5), 854–867 (2017)

    Article  Google Scholar 

  13. Harris, C.J., Krajcik, J.S., Pellegrino, J.W., McElhaney, K.W.: Constructing Assessment Tasks that Blend Disciplinary Core Ideas, Crosscutting Concepts, and Science Practices for Classroom Formative Applications. SRI International, Menlo Park, CA (2016)

    Google Scholar 

  14. Gobert, J.D., Sao Pedro, M., Raziuddin, J., Baker, R.S.: From log files to assessment metrics: measuring students’ science inquiry skills using educational data mining. J. Learn. Sci. 22(4), 521–563 (2013)

    Article  Google Scholar 

  15. Dickler, R., et al.: Supporting students remotely: Integrating mathematics and sciences in virtual labs. In: International Conference of Learning Sciences, pp. 1013–1014. ISLS (2021)

    Google Scholar 

  16. Olsen, J., Adair, A., Gobert, J., Sao Pedro, M., O’Brien, M.: Using log data to validate performance assessments of mathematical modeling practices. In: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part II, pp. 488–491. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-11647-6_99

  17. Vanlehn, K., Wetzel, J., Grover, S., Sande, B.: Learning how to construct models of dynamic systems: an initial evaluation of the Dragoon intelligent tutoring system. IEEE Trans. Learn. Technol. 10(2), 154–167 (2016)

    Article  Google Scholar 

  18. Matuk, C., Zhang, J., Uk, I., Linn, M.C.: Qualitative graphing in an authentic inquiry context: how construction and critique help middle school students to reason about cancer. J. Res. Sci. Teach. 56(7), 905–936 (2019)

    Article  Google Scholar 

  19. VanLehn, K., et al.: The Andes physics tutoring system: lessons learned. Int. J. Artif. Intell. Educ. 15(3), 147–204 (2005)

    Google Scholar 

  20. Koedinger, K.R., Anderson, J.R.: The early evolution of a Cognitive Tutor for algebra symbolization. Interact. Learn. Environ. 5(1), 161–179 (1998)

    Article  Google Scholar 

  21. Aleven, V., McLaren, B.M., Roll, I., Koedinger, K.R.: Help helps, but only so much: research on help seeking with intelligent tutoring systems. Int. J. Artif. Intell. Educ. 26(1), 205–223 (2016)

    Article  Google Scholar 

  22. Fretz, E.B., Wu, H.K., Zhang, B., Davis, E.A., Krajcik, J.S., Soloway, E.: An investigation of software scaffolds supporting modeling practices. Res. Sci. Educ. 32(4), 567–589 (2002)

    Article  Google Scholar 

  23. Sao Pedro, M., Baker, R., Gobert, J., Montalvo, O., Nakama, A.: Leveraging machine-learned detectors of systematic inquiry behavior to estimate and predict transfer of inquiry skill. User Model. User-Adap. Inter. 23, 1–39 (2013)

    Article  Google Scholar 

  24. Bassok, M., Holyoak, K.J.: Interdomain transfer between isomorphic topics in algebra and physics. J. Exp. Psychol. 15(1), 153–166 (1989)

    Google Scholar 

  25. Bransford, J.D., Schwartz, D.L.: Rethinking transfer: a simple proposal with multiple implications. Rev. Res. Educ. 24(1), 61–100 (1999)

    Article  Google Scholar 

  26. Siler, S., Klahr, D., Matlen, B.: Conceptual change when learning experimental design. In: International Handbook of Research on Conceptual Change, pp.138–158. Routledge (2013)

    Google Scholar 

  27. Koedinger, K.R., Baker, R.S., Corbett, A.T.: Toward a model of learning data representations. In: Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, pp. 45–50. Erlbaum, Mahwah, NJ (2001)

    Google Scholar 

  28. Uhden, O., Karam, R., Pietrocola, M., Pospiech, G.: Modelling mathematical reasoning in physics education. Sci. Educ. 21(4), 485–506 (2012)

    Article  Google Scholar 

  29. Jin, H., Delgado, C., Bauer, M., Wylie, E., Cisterna, D., Llort, K.: A hypothetical learning progression for quantifying phenomena in science. Sci. Educ. 28(9), 1181–1208 (2019)

    Article  Google Scholar 

  30. Aleven, V., Koedinger, K.R.: Limitations of student control: do students know when they need help? In: Gauthier, G., Frasson, C., VanLehn, K. (eds.) Intelligent Tutoring Systems, pp. 292–303. Springer Berlin Heidelberg, Berlin, Heidelberg (2000). https://doi.org/10.1007/3-540-45108-0_33

    Chapter  Google Scholar 

  31. Sao Pedro, M., Baker, R., Gobert, J.: Incorporating scaffolding and tutor context into Bayesian knowledge tracing to predict inquiry skill acquisition. In: Educational Data Mining, pp. 185–192 (2013)

    Google Scholar 

  32. Li, H., Gobert, J., Dickler, R.: Evaluating the transfer of scaffolded inquiry: what sticks and does it last? In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds.) AIED 2019. LNCS (LNAI), vol. 11626, pp. 163–168. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23207-8_31

    Chapter  Google Scholar 

  33. Wood, H., Wood, D.: Help seeking, learning and contingent tutoring. Comput. Educ. 33, 153–169 (1999)

    Article  Google Scholar 

  34. Rebello, N.S., Cui, L., Bennett, A.G., Zollman, D.A., Ozimek, D.J.: Transfer of learning in problem solving in the context of mathematics and physics. In: Learning to Solve Complex Scientific Problems, pp. 223–246. Routledge, New York (2017)

    Google Scholar 

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Acknowledgements

This material is based upon work supported by an NSF Graduate Research Fellowship (DGE-1842213; Amy Adair) and the U.S. Department of Education Institute of Education Sciences (R305A210432; Janice Gobert & Michael Sao Pedro). Any opinions, findings, and conclusions or recommendations expressed are those of the author(s) and do not necessarily reflect the views of either organization.

Author information

Authors and Affiliations

  1. Rutgers University, New Brunswick, NJ, 08901, USA

    Amy Adair, Janice Gobert & Ellie Segan

  2. Apprendis, Berlin, MA, 01503, USA

    Michael Sao Pedro & Janice Gobert

Authors
  1. Amy Adair
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  2. Michael Sao Pedro
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  3. Janice Gobert
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  4. Ellie Segan
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Corresponding author

Correspondence to Amy Adair .

Editor information

Editors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    Ning Wang

  2. University of British Columbia, Vancouver, BC, Canada

    Genaro Rebolledo-Mendez

  3. North Carolina State University, Raleigh, NC, USA

    Noboru Matsuda

  4. Despacho 3.01, UNED-Grupo de Investigación aDeNu, Madrid, Spain

    Olga C. Santos

  5. University of Leeds, Leeds, UK

    Vania Dimitrova

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Adair, A., Pedro, M.S., Gobert, J., Segan, E. (2023). Real-Time AI-Driven Assessment and Scaffolding that Improves Students’ Mathematical Modeling during Science Investigations. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2023. Lecture Notes in Computer Science(), vol 13916. Springer, Cham. https://doi.org/10.1007/978-3-031-36272-9_17

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  • DOI: https://doi.org/10.1007/978-3-031-36272-9_17

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