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An Intelligent Tutoring System and Teacher Dashboard to Support Mathematizing During Science Inquiry

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Artificial Intelligence in Education (AIED 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11626))

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Abstract

Using mathematics is critical to science inquiry at the high school level and is predictive of students’ later success in STEM college majors and careers. Inq-ITS (Inquiry Intelligent Tutoring System) has recently added mathematizing functionalities in order to support students in the mathematical practices needed for scientific inquiry, and our teacher alerting dashboard, Inq-Blotter, is being extended to alert teachers in real-time to students’ difficulties with this practice. Mathematizing in science can be challenging as students must attend to multiple sources of information (i.e., graphs, data tables), as well as do graphing and modeling. In the present paper, I describe three studies on the use of Inq-Blotter to support students on mathematizing in which I: (1) explore students’ eye-movements and think-aloud protocols while mathematizing in Inq-ITS to identify the proportion of mathematizing difficulties that are related to knowledge acquisition processes versus other mathematical competencies (e.g., graph building and modeling), (2) examine if alerts within Inq-Blotter permit teachers to identify students who need help most (relative to teachers without access to alerts), and (3) identify whether teacher support based on alerts leads to improvements on students’ next opportunity to engage in mathematizing and examine the corresponding teacher discourse associated with students’ gains. These studies will indicate how to support students on mathematizing with intelligent technologies.

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References

  1. Bybee, R.W., Fuchs, B.: Preparing the 21st century workforce: a new reform in science and technology education. J. Res. Sci. Teach. 43(4), 349–352 (2006)

    Article  Google Scholar 

  2. States, N.G.S.S.L.: Next Generation Science Standards: For States, by States. National Academies Press, Washington (2013)

    Google Scholar 

  3. Basson, I.: Physics and mathematics as interrelated fields of thought development using acceleration as an example. Int. J. Math. Educ. Sci. Technol. 33(5), 679–690 (2002)

    Article  Google Scholar 

  4. Wilkerson, M., Fenwick, M.: The practice of using mathematics and computational thinking. In: Schwarz, C.V., Passmore, C., Reiser, B.J. (eds.) Helping Students Make Sense of the World Using Next Generation Science and Engineering Practices. National Science Teachers’ Association Press, Arlington (2016)

    Google Scholar 

  5. Hansson, L., Hansson, Ö., Juter, K., Redfors, A.: Reality–theoretical models–mathematics: a ternary perspective on physics lessons in upper-secondary school. Sci. Educ. 24, 615–644 (2015)

    Article  Google Scholar 

  6. Hudson, H.T., Rottmann, R.M.: Correlation between performance in physics and prior mathematics knowledge. J. Res. Sci. Teach. 18(4), 291–294 (1981)

    Article  Google Scholar 

  7. McDermott, L.C., Rosenquist, M.L., Van Zee, E.H.: Student difficulties in connecting graphs and physics: examples from kinematics. Am. J. Phys. 55(6), 503–513 (1987)

    Article  Google Scholar 

  8. 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 

  9. Sadler, P.M., Tai, R.H.: Success in introductory college physics: the role of high school preparation. Sci. Educ. 85(2), 111–136 (2001)

    Article  Google Scholar 

  10. 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. Financ. Policy 11, 177–202 (2016)

    Article  Google Scholar 

  11. Hoban, R.A., Finlayson, O.E., Nolan, B.C.: Transfer in chemistry: a study of students’ abilities in transferring mathematical knowledge to chemistry. Int. J. Math. Educ. Sci. Technol. 44(1), 14–35 (2013)

    Article  Google Scholar 

  12. Sadler, P.M., Tai, R.H.: The two high-school pillars supporting college science. Science 317, 457–458 (2007)

    Article  Google Scholar 

  13. Sadler, P.M., Sonnert, G., Hazari, Z., Thi, R.: The role of advanced high school coursework in increasing STEM career interest. Sci. Educ. 23(1), 1 (2014)

    Article  Google Scholar 

  14. Quinn, D.M., Cooc, N.: Science achievement gaps by gender and race/ethnicity in elementary and middle school: trends and predictors. Educ. Res. 44(6), 336–346 (2015)

    Article  Google Scholar 

  15. Gobert, J., Moussavi, R., Li, H.: Sao Pedro, M., Dickler, R.: Real-time scaffolding of students’ online data interpretation during inquiry with Inq-ITS using educational data mining. In: Auer, M., Azad, A., Edwards, A., de Jong, T. (eds.) Cyber-Physical Laboratories In Engineering And Science Education. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76935-6_8

    Chapter  Google Scholar 

  16. 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, 521–563 (2013)

    Article  Google Scholar 

  17. Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, R.: Cognitive tutors: lessons learned. J. Learn. Sci. 4(2), 167–207 (1995)

    Article  Google Scholar 

  18. Aleven, V., Roll, I., McLaren, B.M., 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 

  19. Xhakaj, F., Aleven, V., McLaren, B.M.: Effects of a teacher dashboard for an intelligent tutoring system on teacher knowledge, lesson planning, lessons and student learning. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) EC-TEL 2017. LNCS, vol. 10474, pp. 315–329. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66610-5_23

    Chapter  Google Scholar 

  20. Acosta, A., Slotta, J.D.: CKBiology: an active learning curriculum design for secondary biology. In: Frontiers in Education, p. 52. Frontiers (2018)

    Google Scholar 

  21. Matuk, C.F., Linn, M.C., Eylon, B.S.: Technology to support teachers using evidence from student work to customize technology-enhanced inquiry units. Instr. Sci. 43(2), 229–257 (2015)

    Article  Google Scholar 

  22. Vitale, J., Linn, M., Gerard, L.: Visualizing data from automated scores to help teachers guide inquiry with scientific visualizations in diverse classes. Presented at: 12th Annual International Conference of the Learning Sciences as Part of a Symposium, Real-Time Visualization of Student Activities to Support Classroom Orchestration, Singapore (2016)

    Google Scholar 

  23. Sao Pedro, M.A., Gobert, J., Dickler, R.: Can an alerting teacher dashboard improve how teachers help their students learn science inquiry practices? Paper presentation at: American Educational Research Association (AERA): Learning and Instruction, Toronto, Canada (2019)

    Google Scholar 

  24. Sao Pedro, M., Gobert, J., Dickler, R.: Improving science teaching and learning through rigorous and relevant education technology interventions. Paper presentation at: National Association of Research on Science Teaching, Baltimore, MD (2019)

    Google Scholar 

  25. Dickler, R., Gobert, J., Sao Pedro, M., Li, H.: Teacher scaffolds mediated by a science inquiry dashboard. Paper presentation at: European Association of Research on Science Education, Bologna, Italy (2019)

    Google Scholar 

  26. Gobert, J.D.: Leveraging technology and cognitive theory on visualization to promote students’ science. In: Gilbert, J.K. (ed.) Visualization in Science Education, pp. 73–90. Springer, Dordrecht (2005). https://doi.org/10.1007/1-4020-3613-2_6

    Chapter  Google Scholar 

  27. Dickler, R., Gobert, J., Yasar, O.: Exploring the use of eye-tracking as a method to capture student knowledge acquisition in a virtual science inquiry investigation. Presented at: Rutgers STEM Community Outreach Symposium, New Brunswick, NJ (2018)

    Google Scholar 

  28. Van Gog, T., Paas, F., Van Merriënboer, J.J.: Uncovering expertise-related differences in troubleshooting performance: combining eye movement and concurrent verbal protocol data. Appl. Cogn. Psychol. 19(2), 205–221 (2005)

    Article  Google Scholar 

  29. Gobert, J.D., Baker, R.S., Sao Pedro, M.A.: Inquiry skills tutoring system. U.S. Patent No. 9,373,082. U.S. Patent and Trademark Office, Washington, DC (2014)

    Google Scholar 

  30. Gobert, J., Toto, E.: Instruction system with eyetracking-based adaptive scaffolding. U.S. Patent No. 9,230,022. U.S. Patent and Trademark Office, Washington, DC (2014)

    Google Scholar 

  31. Mason, L., Pluchino, P., Tornatora, M.C., Ariasi, N.: An eye-tracking study of learning from science text with concrete and abstract illustrations. J. Exp. Educ. 81(3), 356–384 (2013)

    Article  Google Scholar 

  32. Gobert, J., Sao Pedro, M., Betts, C., Baker, R.S. Inquiry skills tutoring system (child patent for alerting system). U.S. Patent No. 9,564,057. U.S. Patent and Trademark Office, Washington, DC (2016)

    Google Scholar 

  33. Gobert, J., Sao Pedro, M.: Inq-Blotter: a real time alerting tool to transform teachers’ assessment of science inquiry practices (NSF-IIS-1629045). Awarded from the National Science Foundation (2016)

    Google Scholar 

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Author information

Authors and Affiliations

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

    Rachel Dickler

Authors
  1. Rachel Dickler
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Correspondence to Rachel Dickler .

Editor information

Editors and Affiliations

  1. University of Sao Paulo, Sao Paulo, Brazil

    Seiji Isotani

  2. University of Malaga, Málaga, Spain

    Eva Millán

  3. Carnegie Mellon University, Pittsburgh, PA, USA

    Amy Ogan

  4. DePaul University, Chicago, IL, USA

    Peter Hastings

  5. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  6. University College London, London, UK

    Rose Luckin

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Dickler, R. (2019). An Intelligent Tutoring System and Teacher Dashboard to Support Mathematizing During Science Inquiry. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11626. Springer, Cham. https://doi.org/10.1007/978-3-030-23207-8_61

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  • DOI: https://doi.org/10.1007/978-3-030-23207-8_61

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23206-1

  • Online ISBN: 978-3-030-23207-8

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