Developing Computer Model-Based Assessment of Chemical Reasoning: A Feasibility Study

Liu, Xiufeng; Waight, Noemi; Gregorius, Roberto; Smith, Erica; Park, Mihwa

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Developing Computer Model-Based Assessment of Chemical Reasoning: A Feasibility Study

Liu, X., Waight, N., Gregorius, R., Smith, E. & Park, M. (2012). Developing Computer Model-Based Assessment of Chemical Reasoning: A Feasibility Study. Journal of Computers in Mathematics and Science Teaching, 31(3), 259-281. Chesapeake, VA: AACE.
Retrieved from http://www.editlib.org/p/39224.

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Journal Information

Journal of Computers in Mathematics and Science Teaching
ISSN 0731-9258
Volume 31, Issue 3, July 2012
Association for the Advancement of Computing in Education (AACE) Chesapeake, VA

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Authors

Xiufeng Liu, Noemi Waight, State University of New York at Buffalo, United States; Roberto Gregorius, Canisius College, United States; Erica Smith, Mihwa Park, State University of New York at Buffalo, United States

Abstract

This paper reports a feasibility study on developing computer model-based assessments of chemical reasoning at the high school level. Computer models are flash and NetLogo environments to make simultaneously available three domains in chemistry: macroscopic, submicroscopic, and symbolic. Students interact with computer models to answer assessment questions. Student responses provide an indication of student understanding of two big ideas in chemistry: matter-energy and models. Teachers incorporate computer models during chemistry units of instruction and give the computer model-based assessments at the end of unit instruction. Multi-dimensional Rasch modeling was applied to student responses to the assessment items. Results suggest that most assessment items have good technical quality and most assessments have adequate construct validity and reliability. Results also indicate specific areas of improvement for computer models, computer model-based assessments, and integration of the models and assessments in high school chemistry courses. We conclude that computer model-based assessment of learning progression is feasible and promising.

References

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Ardac, D., & Akaygun, S. (2004). Effectiveness of multimedia-based instruction that emphasizes molecular representations on students’ understanding of chemical change. Journal of Research in Science Teaching, 41(4), 317-337. Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences (second edition). Mahwah, nJ: Lawrence erlbaum.
Chang, H.-Y., Quintana, C., & Krajcik, J. S. (2010). The impact of designing and evaluating molecular animations on how well middle school students understand the particulate nature of matter. Science Education, 94(73-94). Claesgens, J., scalise, K., Wilson, M., & Stacy, A. (2009). Mapping student understanding in chemistry: the perspectives of chemists. Science Educa-
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Rasch, G. (1960/1980). Probabilistic models for some intelligence and attainment tests. Chicago: university of chicago Press/ Danmarks Paedogogiske institut.
Steedle, J. T., & Shavelson, R. J. (2009). Supporting valid interpretations of learning progression level diagnoses. Journal of Research in Science Teaching, 46(6), 699-715.
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These references have been extracted automatically and may have some errors. If you see a mistake in the references above, please contact info@editlib.org.

[1] Alonzo, A., & Steedle, J. T. (2009). Developing and assessing a force and motion learning progression. Science Education, 93(3), 389-421.

[2] Ardac, D., & Akaygun, S. (2004). Effectiveness of multimedia-based instruction that emphasizes molecular representations on students’ understanding of chemical change. Journal of Research in Science Teaching, 41(4), 317-337. Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences (second edition). Mahwah, nJ: Lawrence erlbaum.

[3] Chang, H.-Y., Quintana, C., & Krajcik, J. S. (2010). The impact of designing and evaluating molecular animations on how well middle school students understand the particulate nature of matter. Science Education, 94(73-94). Claesgens, J., scalise, K., Wilson, M., & Stacy, A. (2009). Mapping student understanding in chemistry: the perspectives of chemists. Science Educa-

[4] Feynman, R. P. (1963). Six easy pieces: Essentials of physics explained by its most brilliant teacher. Cambridge, ma: Perseus books.

[5] Gabel, D. (1999). Improving teaching and learning through chemistry education research: a look to the future. Journal of Chemical Education, 76(4), 7. Gilbert, J. K., & Boutler, C. (1998). Learning science through models and modeling. In B. Fraser & K. Tobin (eds.), International handbook of science education (pp. 53-66). Dordrecht, the netherlands: kluwer academic publishers.

[6] Johnstone, A. H. (1993). The development of chemistry teaching: a changing response to changing demand. Journal of Chemical Education, 70(9), 701705.

[7] Kozma, R. B., & Russell, J. (1997). Multimedia and understanding: expert and novice responses to different representations of chemical phenomena. Journal of Research in Science Teaching, 34(9), 20.

[8] Liu, X. (2010). Using and developing measurement instruments in science education: A Rasch modeling approach. Charlotte, nc iaP Press

[9] National Research council (nRc). (2001). Knowing what students know. Washington, Dc: national academic Press.

[10] National Research council (2011). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, Dc: the national academies Press.

[11] Ozmen, H. (2011). Effect of animation enhanced conceptual change texts on 6th grade students’ understanding of the particulate nature of matter and transformation during phase changes. Computer & Education, 57, 1114-1126.

[12] Rasch, G. (1960/1980). Probabilistic models for some intelligence and attainment tests. Chicago: university of chicago Press/ Danmarks Paedogogiske institut.

[13] Steedle, J. T., & Shavelson, R. J. (2009). Supporting valid interpretations of learning progression level diagnoses. Journal of Research in Science Teaching, 46(6), 699-715.

[14] Stieff, M. (2004). Connected chemistry -- a novel modeling environment for the chemistry classroom. Journal of Chemical Education, 82(3), 489-493.

[15] Wiggins, G. P., & Mctighe, J. (2005). Understanding by design (2nd edition). Upper saddle River, nJ: Prentice hall.

[16] Wilson, M. (2005). Constructing measures: An item response modeling approach.

Developing Computer Model-Based Assessment of Chemical Reasoning: A Feasibility Study

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