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The Influence of Item Formats when Locating a Student on a Learning Progression in Science

Year 2016, Volume: 3 Issue: 2, 101 - 122, 01.07.2016
https://doi.org/10.21449/ijate.245196

Abstract

Learning progressions
are used to describe how students’ understanding of a topic progresses over
time. This study evaluates the effectiveness of different item formats for
placing students into levels along a learning progression for carbon cycling.
The item formats investigated were Constructed Response (CR) items and two
types of two-tier items: (1) Ordered Multiple-Choice (OMC) followed by CR items
and (2) Multiple True or False (MTF) followed by CR items. Our results suggest
that estimates of students’ learning progression level based on OMC and MTF
responses are moderately predictive of their level based on CR responses. With
few exceptions, CR items were effective for differentiating students among
learning progression levels. Based on the results, we discuss how to design and
best use items in each format to more accurately measure students’ level along
learning progressions in science. 

References

  • Adams, R.J., Wilson, M., & Wang, W-C. (1997). The multidimensional random coefficients multinomial logit model. Applied Psychological Measurement, 21, 1-23.
  • Alonzo, A.C., & Gotwals, A. G. (2012). Learning progressions in science. Rotterdam, The Netherlands: Sense Publishers.
  • Alonzo, A.C., & Steedle, J. T. (2008). Developing and assessing a force and motion learning progression. Published online in Wiley InterScience.
  • Anderson, C.W., Alonzo, A. C., Smith, C., & Wilson, M. (2007, August). NAEP pilot learning progression framework. Report to the National Assessment Governing Board.
  • Angoff, W.H. (1971). Scales, norms, and equivalent scores. In R. L. Thorndike (Ed.), Educational measurement (2nd ed., pp. 508-600). Washington, DC: American Council on Education.
  • Berlak, H. (1992). The need for a new science of assessment. In H. Berlak et al. (Eds.) Toward a new science of educational testing and assessment (pp. 1-22). Albany: State University of New York Press.
  • Briggs, D.C. & Alonzo, A.C. (2012). The psychometric modelling of ordered multiple-choice item responses for diagnostic assessment with a learning progression. In A.C. Alonzo & A.W. Gotwals (eds). Learning progressions in science: Current challenges and future directions. Rotterdam, The Netherlands: Sense Publishers.
  • Briggs, D.C., Alonzo, A.C., Schwab, C., & Wilson, M. (2006). Diagnostic assessment with ordered multiple-choice items. Educational Assessment, 11, 33 – 63.
  • Catley, K., Lehrer R., & Reiser, B. (2004). Tracing a prospective learning progression for developing understanding of evolution, Paper Commissioned by the National Academies Committee on Test Design for K–12 Science Achievement, Washington, DC: National Academy of Science, 67.
  • Chen, J. & Anderson, C.W. (2015). Comparing American and Chinese K-12 students’ learning progression on carbon cycling in socio-ecological systems. Science Education International. 27(4), 439-462.
  • Corcoran, T., Mosher, F. A., & Rogat, A. (2009, May). Learning progressions in science: An evidence based approach to reform (CPRE Research Report #RR-63). Philadelphia, PA: Consortium for Policy Research in Education.
  • Doherty, J., Draney, K., Shin, H., Kim, J., & Anderson, C. W. (2015). Validation of a learning progression-based monitoring assessment. Manuscript submitted for publication.
  • Downing, S. M., & Yudkowsky, R. (2009). Assessment in Health Professions Education. New York, NY Routledge.
  • Dunham, M. L. (2007) An investigation of the multiple true-false item for nursing licensure and potential sources of construct-irrelevant difficulty. http://proquest.umi.com/pqdlink?did=1232396481&Fmt=7&clientI d=79356&RQT=309&VName=PQD
  • Embretson, S. E. (1996). Item response theory models and inferential bias in multiple group comparisons. Applied Psychological Measurement, 20, 201-212.
  • Ercikan, K., Schwarz, R.D., Julian, M. W., Burket, G.R., Weber, M.M. & Link, V. (1998). Calibration and scoring of tests with multiple-choice and constructed-response item types. Journal of Educational Measurement, 35, 137-154.
  • Flowers, K., Bolton, C., Brindle, N. (2008). Chance guessing in a forced-choice recognition task and the detection of malingering. In: Neuropsychology, 22 (2), 273-277
  • Frisbie, D. A. (1992). The multiple true-false item format: A status review. Educational Measurement: Issues and Practice,11(4), 21–26.
  • Jin, H., & Anderson, C. W. (2012). A learning progression for energy in socio-ecological systems. Journal of Research in Science Teaching, 49(9), 1149–1180.
  • Lee, H.S., Liu, O.L. & Linn, M.C. (2011). Validating Measurement of Knowledge Integration Science Using Multiple-Choice and Explanation Items. Applied Measurement in Education, 24(2), 115-136.
  • Liu, O.L., Lee, H-S., Hofstedder, C. & Linn, M.C. (2008). Assessing knowledge integration in science: Construct, measures and evidence. Educational Assessment. 13, 33-55.
  • Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149-173.
  • Martinez, M. (1999). Cognition and the question of test item format. Educational Psychologists, 34, 207- 218.
  • Merritt, J. D., Krajcik, J. & Shwartz, Y. (2008). Development of a learning progression for the particle model of matter. In Proceedings of the 8th International Conference for the Learning Sciences (Vol. 2, pp. 75-81). Utrecht, The Netherlands: International Society of the Learning Sciences.
  • Mohan, L., Chen, J., & Anderson, C.W. (2009). Developing a multi-year learning progression for carbon cycling in socio-ecological systems. Journal of Research in Science Teaching. 46 (6), 675-698.
  • National Research Council. (2006). Systems for state science assessment. Washington, DC: The National Academies Press.
  • National Research Council. (2007). Taking science to school. Washington, DC: The National Academies Press.
  • National Research Council. (2014). Developing Assessments for the Next Generation Science Standards. Committee on Developing Assessments of Science Proficiency in K-12. Board on Testing and Assessment and Board on Science Education, J.W. Pellegrino, M.R. Wilson, J.A. Koenig, and A.S. Beatty, Editors. Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.
  • Plummer, J.D. & Maynard, L. (2014). Building a learning progression for celestial motion: An exploration of students’ reasoning about the seasons. Journal of Research in Science Teaching, 51(7), 902-929.
  • Rivet, A. & Kastens, K. (2012). Developing a construct-based assessment to examine students’ analogical reasoning around physical models in earth science. Journal of Research in Science Teaching, 49(6), 713-743.
  • Salinas, I. (2009, June). Learning progressions in science education: Two approaches for development. Paper presented at the Learning Progressions in Science (LeaPS) Conference, Iowa City, IA. Available from
  • http://www.education.uiowa.edu/projects/leaps/proceedings/
  • Schuwirth, L. & van der Vleuten, C. (2004). Different Written Assessment Methods: What can be said about their Strengths and Weaknesses? Medical Education 38,9: 974-979.
  • Songer, N.B. & Gotwals, A.W. (2012). Guiding explanation construction by children at the entry points of learning progressions. Journal for Research in Science Teaching, 49, 141-165.
  • Songer, N.B., Kelcey, B. & Gotwals, A.W. (2009). How and when does complex reasoning occur? Empirically driven development of a learning progression focused on complex reasoning in biodiversity. Journal of Research in Science Teaching. 46(6): 610-631.
  • Smith, C.L., Wiser, M., Anderson, C.W., & Krajcik, J. (2006). Implications on research on
children’s learning for standards and assessment: A proposed learning for matter and
the atomic molecular theory. Measurement: Interdisciplinary Research & Perspective, 4(1), 1-98.
  • 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.
  • Talento-Miller, E., Han, K. & Guo, F. (2011). Guess Again: The Effect of Correct Guesses on Scores in an Operational CAT Program. (Graduate Management Admission Council research report. No. RR-11-04). http://www.gmac.com/~/media/Files/gmac/Research/research-report- series/guessagaintheeffectofcorrect.pdf
  • Thissen, D., & Steinberg, L. (1997). A response model for multiple-choice items. In W. van der Linden & R. Hambleton (Eds.), Handbook of modern item response theory (pp. 52- 65). New York: Springer-Verlag.
  • van der Linen, W. J., & Hambleton, R. K. (Eds.). (1997). Handbook of modern item response theory. New York: Springer.
  • Wainer, H., & Thissen, D. (1993). Combining multiple-choice and constructed-response test scores: Toward a Marxist theory of test construction. Applied Measurement in Education, 6(2), 103-118.
  • Wilson, M., & Wang, W.C. (1995). Complex composites: Issues that arise in combining different modes of assessment. Applied Psychological Measurement, 19(1), 51-71.
  • Wright, B.D., Linacre, J.M., Gustafsson, J.E. & Martin-Loff, P. (1994). Reasonable mean- square fit values. Rasch Meas Trans 1994; 8: 370.
  • Wu, M.L., Adams, R.J. & Wilson, M.R. (1998). ACER Conquest: Generalised item response modelling software. Melbourne: ACER Press.
  • Wu, M.L., Adams, R.J., Wilson, M. R. & Haldane, S. A. (2007). ACER ConQuest Version 2.0: generalised item response modeling software. Camberwell, Australia: Australia Council for Educational Research.
  • Yao, L., & Boughton, K.A. (2009). Multidimensional linking for tests with mixed item types. Journal of Educational Measurement, 46 (2), 177–197.

The Influence of Item Formats when Locating a Student on a Learning Progression in Science

Year 2016, Volume: 3 Issue: 2, 101 - 122, 01.07.2016
https://doi.org/10.21449/ijate.245196

Abstract

Learning progressions are used to describe how students’ understanding of a topic progresses over time. This study evaluates the effectiveness of different item formats for placing students into levels along a learning progression for carbon cycling. The item formats investigated were Constructed Response (CR) items and two types of two-tier items: (1) Ordered Multiple-Choice (OMC) followed by CR items and (2) Multiple True or False (MTF) followed by CR items. Our results suggest that estimates of students’ learning progression level based on OMC and MTF responses are moderately predictive of their level based on CR responses. With few exceptions, CR items were effective for differentiating students among learning progression levels. Based on the results, we discuss how to design and best use items in each format to more accurately measure students’ level along learning progressions in science. 

References

  • Adams, R.J., Wilson, M., & Wang, W-C. (1997). The multidimensional random coefficients multinomial logit model. Applied Psychological Measurement, 21, 1-23.
  • Alonzo, A.C., & Gotwals, A. G. (2012). Learning progressions in science. Rotterdam, The Netherlands: Sense Publishers.
  • Alonzo, A.C., & Steedle, J. T. (2008). Developing and assessing a force and motion learning progression. Published online in Wiley InterScience.
  • Anderson, C.W., Alonzo, A. C., Smith, C., & Wilson, M. (2007, August). NAEP pilot learning progression framework. Report to the National Assessment Governing Board.
  • Angoff, W.H. (1971). Scales, norms, and equivalent scores. In R. L. Thorndike (Ed.), Educational measurement (2nd ed., pp. 508-600). Washington, DC: American Council on Education.
  • Berlak, H. (1992). The need for a new science of assessment. In H. Berlak et al. (Eds.) Toward a new science of educational testing and assessment (pp. 1-22). Albany: State University of New York Press.
  • Briggs, D.C. & Alonzo, A.C. (2012). The psychometric modelling of ordered multiple-choice item responses for diagnostic assessment with a learning progression. In A.C. Alonzo & A.W. Gotwals (eds). Learning progressions in science: Current challenges and future directions. Rotterdam, The Netherlands: Sense Publishers.
  • Briggs, D.C., Alonzo, A.C., Schwab, C., & Wilson, M. (2006). Diagnostic assessment with ordered multiple-choice items. Educational Assessment, 11, 33 – 63.
  • Catley, K., Lehrer R., & Reiser, B. (2004). Tracing a prospective learning progression for developing understanding of evolution, Paper Commissioned by the National Academies Committee on Test Design for K–12 Science Achievement, Washington, DC: National Academy of Science, 67.
  • Chen, J. & Anderson, C.W. (2015). Comparing American and Chinese K-12 students’ learning progression on carbon cycling in socio-ecological systems. Science Education International. 27(4), 439-462.
  • Corcoran, T., Mosher, F. A., & Rogat, A. (2009, May). Learning progressions in science: An evidence based approach to reform (CPRE Research Report #RR-63). Philadelphia, PA: Consortium for Policy Research in Education.
  • Doherty, J., Draney, K., Shin, H., Kim, J., & Anderson, C. W. (2015). Validation of a learning progression-based monitoring assessment. Manuscript submitted for publication.
  • Downing, S. M., & Yudkowsky, R. (2009). Assessment in Health Professions Education. New York, NY Routledge.
  • Dunham, M. L. (2007) An investigation of the multiple true-false item for nursing licensure and potential sources of construct-irrelevant difficulty. http://proquest.umi.com/pqdlink?did=1232396481&Fmt=7&clientI d=79356&RQT=309&VName=PQD
  • Embretson, S. E. (1996). Item response theory models and inferential bias in multiple group comparisons. Applied Psychological Measurement, 20, 201-212.
  • Ercikan, K., Schwarz, R.D., Julian, M. W., Burket, G.R., Weber, M.M. & Link, V. (1998). Calibration and scoring of tests with multiple-choice and constructed-response item types. Journal of Educational Measurement, 35, 137-154.
  • Flowers, K., Bolton, C., Brindle, N. (2008). Chance guessing in a forced-choice recognition task and the detection of malingering. In: Neuropsychology, 22 (2), 273-277
  • Frisbie, D. A. (1992). The multiple true-false item format: A status review. Educational Measurement: Issues and Practice,11(4), 21–26.
  • Jin, H., & Anderson, C. W. (2012). A learning progression for energy in socio-ecological systems. Journal of Research in Science Teaching, 49(9), 1149–1180.
  • Lee, H.S., Liu, O.L. & Linn, M.C. (2011). Validating Measurement of Knowledge Integration Science Using Multiple-Choice and Explanation Items. Applied Measurement in Education, 24(2), 115-136.
  • Liu, O.L., Lee, H-S., Hofstedder, C. & Linn, M.C. (2008). Assessing knowledge integration in science: Construct, measures and evidence. Educational Assessment. 13, 33-55.
  • Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149-173.
  • Martinez, M. (1999). Cognition and the question of test item format. Educational Psychologists, 34, 207- 218.
  • Merritt, J. D., Krajcik, J. & Shwartz, Y. (2008). Development of a learning progression for the particle model of matter. In Proceedings of the 8th International Conference for the Learning Sciences (Vol. 2, pp. 75-81). Utrecht, The Netherlands: International Society of the Learning Sciences.
  • Mohan, L., Chen, J., & Anderson, C.W. (2009). Developing a multi-year learning progression for carbon cycling in socio-ecological systems. Journal of Research in Science Teaching. 46 (6), 675-698.
  • National Research Council. (2006). Systems for state science assessment. Washington, DC: The National Academies Press.
  • National Research Council. (2007). Taking science to school. Washington, DC: The National Academies Press.
  • National Research Council. (2014). Developing Assessments for the Next Generation Science Standards. Committee on Developing Assessments of Science Proficiency in K-12. Board on Testing and Assessment and Board on Science Education, J.W. Pellegrino, M.R. Wilson, J.A. Koenig, and A.S. Beatty, Editors. Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.
  • Plummer, J.D. & Maynard, L. (2014). Building a learning progression for celestial motion: An exploration of students’ reasoning about the seasons. Journal of Research in Science Teaching, 51(7), 902-929.
  • Rivet, A. & Kastens, K. (2012). Developing a construct-based assessment to examine students’ analogical reasoning around physical models in earth science. Journal of Research in Science Teaching, 49(6), 713-743.
  • Salinas, I. (2009, June). Learning progressions in science education: Two approaches for development. Paper presented at the Learning Progressions in Science (LeaPS) Conference, Iowa City, IA. Available from
  • http://www.education.uiowa.edu/projects/leaps/proceedings/
  • Schuwirth, L. & van der Vleuten, C. (2004). Different Written Assessment Methods: What can be said about their Strengths and Weaknesses? Medical Education 38,9: 974-979.
  • Songer, N.B. & Gotwals, A.W. (2012). Guiding explanation construction by children at the entry points of learning progressions. Journal for Research in Science Teaching, 49, 141-165.
  • Songer, N.B., Kelcey, B. & Gotwals, A.W. (2009). How and when does complex reasoning occur? Empirically driven development of a learning progression focused on complex reasoning in biodiversity. Journal of Research in Science Teaching. 46(6): 610-631.
  • Smith, C.L., Wiser, M., Anderson, C.W., & Krajcik, J. (2006). Implications on research on
children’s learning for standards and assessment: A proposed learning for matter and
the atomic molecular theory. Measurement: Interdisciplinary Research & Perspective, 4(1), 1-98.
  • 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.
  • Talento-Miller, E., Han, K. & Guo, F. (2011). Guess Again: The Effect of Correct Guesses on Scores in an Operational CAT Program. (Graduate Management Admission Council research report. No. RR-11-04). http://www.gmac.com/~/media/Files/gmac/Research/research-report- series/guessagaintheeffectofcorrect.pdf
  • Thissen, D., & Steinberg, L. (1997). A response model for multiple-choice items. In W. van der Linden & R. Hambleton (Eds.), Handbook of modern item response theory (pp. 52- 65). New York: Springer-Verlag.
  • van der Linen, W. J., & Hambleton, R. K. (Eds.). (1997). Handbook of modern item response theory. New York: Springer.
  • Wainer, H., & Thissen, D. (1993). Combining multiple-choice and constructed-response test scores: Toward a Marxist theory of test construction. Applied Measurement in Education, 6(2), 103-118.
  • Wilson, M., & Wang, W.C. (1995). Complex composites: Issues that arise in combining different modes of assessment. Applied Psychological Measurement, 19(1), 51-71.
  • Wright, B.D., Linacre, J.M., Gustafsson, J.E. & Martin-Loff, P. (1994). Reasonable mean- square fit values. Rasch Meas Trans 1994; 8: 370.
  • Wu, M.L., Adams, R.J. & Wilson, M.R. (1998). ACER Conquest: Generalised item response modelling software. Melbourne: ACER Press.
  • Wu, M.L., Adams, R.J., Wilson, M. R. & Haldane, S. A. (2007). ACER ConQuest Version 2.0: generalised item response modeling software. Camberwell, Australia: Australia Council for Educational Research.
  • Yao, L., & Boughton, K.A. (2009). Multidimensional linking for tests with mixed item types. Journal of Educational Measurement, 46 (2), 177–197.
There are 46 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Articles
Authors

Jing Chen This is me

Publication Date July 1, 2016
Submission Date January 1, 2016
Published in Issue Year 2016 Volume: 3 Issue: 2

Cite

APA Chen, J. (2016). The Influence of Item Formats when Locating a Student on a Learning Progression in Science. International Journal of Assessment Tools in Education, 3(2), 101-122. https://doi.org/10.21449/ijate.245196

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