Abstract
This study developed and validated an assessment that measures interdisciplinary understanding using the topic of carbon cycling, the Interdisciplinary Science Assessment for Carbon Cycling (ISACC). This work was motivated by the need to assess interdisciplinary understanding, defined as the ability to solve problems requiring knowledge and skills from multiple disciplines. Levels of a construct map for interdisciplinary understanding were generated and refined in the construct-modeling design process (Wilson 2005). Key concepts in carbon cycling to be assessed were determined based on experts’ concept maps and analysis of the Next Generation Science Standards. The final version of the ISACC includes 11 multiple-choice (MC) items and eight constructed-response (CR) items covering nine key concepts. Of these 19 items, six are single-disciplinary items and 13 are interdisciplinary items. Four hundred fifty-four students in grades 9–16 were recruited and administered the ISACC. For the CR items, scoring rubrics were developed and used by a group of evaluators to code student responses. Two item response theory models, a two-parameter logistic model and a generalized partial credit model, provided evidence of the construct validity of the items. All items reflected unidimensionality and local independence and showed moderate internal consistency (Cronbach’s alpha = 0.782). All except one were a good fit to the models. The findings suggest that the ISACC is a promising tool to assess interdisciplinary understanding of carbon cycling. Future directions include research into test fairness across gender and ethnic/racial groups and further development to cover typical high school students’ knowledge more thoroughly.
Similar content being viewed by others
References
American Association for the Advancement of Science (AAAS). (1989). Science for all Americans. Washington, DC: American Association for the Advancement of Science.
American Association for the Advancement of Science (AAAS). (2009). Benchmarks for science literacy on-line. Retrieved from http://www.project2061.org/publications/bsl/online.
Australian Curriculum, Assessment and Reporting Authority (ACARA). (2012), The shape of the Australian Curriculum (version 4.0), Retrieved from http://www.acara.edu.au/verve/_resources/The_Shape_of_the_Australian_Curriculum_v4.pdf.
Beane, J. A. (1995). Curriculum integration and the disciplines of knowledge. Phi Delta Kappan, 616–622.
Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee’s ability. In F. M. Lord & M. R. Novick (Eds.), Statistical theories of mental test scores. MA: Addison-Wesley.
Boix Mansilla, V. (2005). Assessing student work at disciplinary crossroads. Change: The Magazine of Higher Learning, 37(1), 14–21.
Boix Mansilla, V., & Duraisingh, E. D. (2007). Targeted assessment of students’ interdisciplinary work: an empirically grounded framework proposed. The Journal of Higher Education, 78(2), 215–237.
Brown, N. J., & Wilson, M. (2011). A model of cognition: the missing cornerstone of assessment. Educational Psychology Review, 23(2), 221–234.
Cai, L., Thissen, D., & du Toit, S. (2016). IRTPRO 3 for Windows [Computer Software]. Skokie, IL: Scientific Software International, Inc.
California Department of Education. (1990). The California framework for science instruction. Sacramento: California Department of Education.
Chandramohan, B., & Fallows, S. J. (2009). Interdisciplinary learning and teaching in higher education: theory and practice. New York: Routledge.
Chen, W. H., & Thissen, D. (1997). Local dependence indexes for item pairs using item response theory. Journal of Educational and Behavioral Statistics, 22(3), 265–289.
Chi, M. T. H., & Ceci, S. J. (1987). Content knowledge: Its role, representation, and restructuring in memory development. In H. W. Reese (Ed.), Advances in child development and behavior (Vol. 20, pp. 91–142). New York: Academic Press.
Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98–104.
Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. New York: CBS College Publishing.
Dewey, J. (1938). Experience and education. New York: The Macmillan company.
Dorsey, D. W., Campbell, G. E., Foster, L. L., & Miles, D. E. (1999). Assessing knowledge structures: relations with experience and posttraining performance. Human Performance, 12(1), 31–57.
Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. New York: Psychology Press.
Fleiss, J. L. (1986). Reliability of measurement. The design and analysis of clinical experiments, 1–32.
Golding, C. (2009). Integrating the disciplines: successful interdisciplinary subjects. Melbourn: Centre for the Study of Higher Education, University of Melbourne.
Hambelton, R. K. (1989). Principles and selected applications of item response theory. In R. L. Linn (Ed.), Educational measurement (3rd ed., pp. 147–200). New York: Macmillan.
Hartley, L. M., Momsen, J., Maskiewicz, A., & D’Avanzo, C. (2012). Energy and matter: differences in discourse in physical and biological sciences can be confusing for introductory biology students. BioScience, 62(5), 488–496.
Hirsh, S. (2011). Building professional development to support new student assessment systems. Retrieved from https://learningforward.org/docs/pdf/stephanie_hirsh-building_professional_development.pdf.
Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.
Jho, H., Hong, O., & Song, J. (2016). An analysis of STEM/STEAM teacher education in Korea with a case study of two schools from a community of practice perspective. Eurasia Journal of Mathematics, Science & Technology Education, 12(7).
Johnson-Laird, P. N. (1980). Mental models in cognitive science. Cognitive Science, 4(1), 71–115.
Klein, J. T. (1990). Interdisciplinarity: history, theory, and practice. Detroit: Wayne State University Press.
Kline, R. (2015). Principles and practice of structural equation modeling (4th ed.). New York: Guilford Publications.
Krajcik, J. S., McNeill, K. L., & Reiser, B. J. (2008). Learning-goals-driven design model: developing curriculum materials that align with national standards and incorporate project-based pedagogy. Science Education, 92(1), 1–32. https://doi.org/10.1002/sce.20240.
Labaree, D. F. (2005). Progressivism, schools and schools of education: an American romance. Paedagogica Historica, 41(1–2), 275–288.
Lead States, N. G. S. S. (2013). Next Generation Science Standards: for states, by states. Washington, DC: National Academy Press.
Linn, M. C. (2006). The knowledge integration perspective on learning and instruction. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 243–264). New York: Cambridge University Progressivism, schools and schools of education: an American romance. Press.
Liu, X. (2010). Using and developing measurement instruments in science education: a Rasch modeling approach. Charlotte: Information Age Pub.
Liu, O. L., Lee, H. S., Hofstetter, C., & Linn, M. C. (2008). Assessing knowledge integration in science: construct, measures, and evidence. Educational Assessment, 13(1), 33–55. https://doi.org/10.1080/10627190801968224.
Lord, F. M. (1980). Applications of item response theory to practical testing problems. Hillsdale: Lawrence Erlbaum Associates.
Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382–386.
Maskiewicz, A. C., Griscom, H. P., & Welch, N. T. (2012). Using targeted active-learning exercises and diagnostic question clusters to improve students’ understanding of carbon cycling in ecosystems. CBE-Life Sciences Education, 11(1), 58–67.
McComas, W. F., & Wang, H. A. (1998). Blended science: the rewards and challenges of integrating the science disciplines for instruction. School Science and Mathematics, 98(6), 340–348.
Metz, K. E. (1995). Reassessment of developmental constraints on children’s science instruction. Review of Educational Research, 65(2), 93–127.
Muraki, E. (1992). A generalized partial credit model: application of an EM algorithm. Applied Psychological Measurement, 16(2), 159–176.
Muthén, L. K., & Muthén, B. O. (1998-2015). Mplus User’s Guide. (7 ed.). Los Angeles, CA: Muthén & Muthén.
National Academy of Sciences. (2005). Facilitating interdisciplinary research. Washington, DC: National Academies.
National Research Council. (1996). National science education standards. Washington, DC: National Academy Press.
National Research Council. (2012). A framework for K-12 science education: practices, crosscutting concepts, and core ideas. Washington, DC: National Academy Press.
National Science Teachers Association. (1964). Theory into action in science curriculum development. Washington, DC: National Science Teachers Association.
Neurath, O. (1996). Unified science as encyclopedic integration. Logical empiricism at its peak: Schlick, Carnap, and Neurath, 309-335.
Orlando, M., & Thissen, D. (2000). New item fit indices for dichotomous item response theory models. Applied Psychological Measurement, 24, 50–64.
Orlando, M., & Thissen, D. (2003). Further investigation of the performance of S-X2: an item fit index for use with dichotomous item response theory models. Applied Psychological Measurement, 27, 289–298.
Pellegrino, J. W., Krajcik, J. S., Stevens, S. Y., Swarat, S., Shin, N., & Delgado, C. (2008). Using construct-centered design to align curriculum, instruction, and assessment development in emerging science. In V. Kanselaar, V. Jonker, P. A. Kirschner, & F. Prins (Eds.), ICLS’ 08: international perspectives in the learning sciences: creating a learning world (Vol. 3, pp. 314–321). Utrecht: International Society of the Learning Sciences.
Piaget, J. (1978). The development of thought: equilibration of cognitive structures. New York: Viking Press.
Reckase, M. D. (1979). Unifactor latent trait models applied to multifactor tests: results and implications. Journal of Educational and Behavioral Statistics, 4(3), 207–230.
Reiska, P., Soika, K., & Cañas, A. J. (2018). Using concept mapping to measure changes in interdisciplinary learning during high school. Knowledge Management & E-Learning: An International Journal (KM&EL), 10(1), 1–24.
Reynolds, C. R., & Kaiser, S. M. (1990). Bias in assessment of aptitude. In C. R. Reynolds & R. W. Kamphaus (Eds.), Handbook of psychological and educational assessment of children: intelligence and achievement (pp. 611–653). New York: Guilford.
Rice, J., & Neureither, B. (2006). An integrated physical, earth, and life science course for pre-service K-8 teachers. Journal of Geoscience Education, 54(3), 255–261.
Rowntree, D. (1982). A dictionary of education. Totowa, NJ: Barnes & Noble Books.
Sabbag, A. G., & Zieffler, A. (2015). Assessing learning outcomes: an analysis of the goals-2 instrument. Statistics Education Research Journal, 14(2), 93–116.
Schaal, S., Bogner, F. X., & Girwidz, R. (2010). Concept mapping assessment of media assisted learning in interdisciplinary science education. Research in Science Education, 40(3), 339–352.
Scottish Government (2008), Curriculum for Excellence: building the curriculum 3, Retrieved from http://www.scotland.gov.uk/Resource/Doc/226155/0061245.pdf.
Shen, J., Liu, O. L., & Sung, S. (2014). Designing interdisciplinary assessments in sciences for college students: an example on osmosis. International Journal of Science Education, 36(11), 1773–1793. https://doi.org/10.1080/09500693.2013.879224.
Shin, N., Stevens, S. Y., & Krajcik, J. S. (2010). Tracking student learning over time using construct-centred design. In S. Rodrigues (Ed.), Using analytical frameworks for classroom research: collecting data and analysing narrative (pp. 38–58). London: Routledge.
Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.
Sokolowski, J. A., & Banks, C. M. (2010). Modeling and simulation fundamentals: theoretical underpinnings and practical domains. Hoboken: John Wiley & Sons, Inc.
Spelt, E. J., Biemans, H. J., Tobi, H., Luning, P. A., & Mulder, M. (2009). Teaching and learning in interdisciplinary higher education: a systematic review. Educational Psychology Review, 21(4), 365–378.
Stichweh, R. (2003). Differentiation of scientific disciplines: causes and consequences. Unity of Knowledge in Transdisciplinary Research for Sustainability, 1, 1–8.
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 1–24.
Tanner, K., & Allen, D. (2005). Approaches to biology teaching and learning: understanding the wrong answers—teaching toward conceptual change. Cell Biology Education, 4(2), 112–117.
Teresi, J. A., Ocepek-Welikson, K., Ramirez, M., Kleinman, M., Ornstein, K., & Siu, A. (2015). Evaluation of measurement equivalence of the family satisfaction with the end-of-life care in an ethnically diverse cohort: tests of differential item functioning. Palliative Medicine, 29(1), 83–96. https://doi.org/10.1177/0269216314545802.
Van Merriënboer, J. J. G. (1997). Training complex cognitive skills: a four-component instructional design model for technical training. Englewood Cliffs: Educational Technology.
Versprille, A. N. (2014). General chemistry students’ understanding of the chemistry underlying climate science (Unpublished doctoral dissertation). Purdue University, West Lafayette, IN.
Weingart, P. (2010). A short history of knowledge formations. In R. Frodemann, K. J. Thomson, & C. Mitcham (Eds.), The Oxford handbook of interdisciplinarity (pp. 3–14). Oxford: Oxford University Press.
Wiggins, G. P., & McTighe, J. (1998). Understanding by design. Alexandria: Association for Supervision and Curriculum.
Wilson, M. (2005). Constructing measures: an item response modeling approach. Mahwah: Lawrence Erlbaum Associates.
You, H. S. (2016). Toward interdisciplinary science learning: Development of an assessment for interdisciplinary understanding of ‘carbon cycling’ (Unpublished doctoral dissertation). The University of Texas at Austin, Austin, Texas.
You, H. S., Marshall, J. A., & Delgado, C. (2018). Assessing students' disciplinary and interdisciplinary understanding of global carbon cycling. Journal of Research in Science Teaching, 55(3), 377–398.
Yu, C. Y. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes (Unpublished doctoral dissertation). University of California, Los Angeles, CA.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
You, H.S., Marshall, J.A. & Delgado, C. Toward Interdisciplinary Learning: Development and Validation of an Assessment for Interdisciplinary Understanding of Global Carbon Cycling. Res Sci Educ 51, 1197–1221 (2021). https://doi.org/10.1007/s11165-019-9836-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11165-019-9836-x