Abstract
Many national science frameworks (e.g., Next Generation Science Standards) argue that developing mathematical modeling competencies is critical for students’ deep understanding of science. However, science teachers may be unprepared to assess these competencies. We are addressing this need by developing virtual lab performance assessments that assess these competencies in science inquiry contexts. Through our design processes, we developed a method for validating the assessments that takes advantage of the unique opportunities afforded by collecting log data. Here, we describe this method and demonstrate its utility by analyzing students’ competencies with one example sub-practice of mathematical modeling, plotting controlled data generated from a simulation.
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Acknowledgements
This material is based upon work supported by the U.S. Department of Education Institute of Education Sciences (Award Numbers: R305A210432 & 91990019C0037; Janice Gobert & Mike Sao Pedro) and an NSF Graduate Research Fellowship (DGE-1842213; Amy Adair). Any opinions, findings, and conclusions or recommendations expressed are those of the author(s) and do not necessarily reflect the views of either organization.
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Olsen, J., Adair, A., Gobert, J., Pedro, M.S., O’Brien, M. (2022). Using Log Data to Validate Performance Assessments of Mathematical Modeling Practices. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_99
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DOI: https://doi.org/10.1007/978-3-031-11647-6_99
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