The Science Learning Assessment (SLA) is an individually administered, instructionally sensitive science assessment for kindergarten students. The SLA is a 24-item objective test, broken down into two subtests. The Scientific Inquiry Processes subtest consists of 9 items designed to measure young children's functional understanding of the nature and processes of scientific inquiry. The Life Science Concepts subtest consists of 15 items designed to measure children's understanding of specific science concepts related to living things and the physical world. Our results on SLA items that assess life science concepts indicate that kindergarten children are able to develop a rich knowledge base about living things. The results of the SLA indicate that even young children can begin to develop an understanding of scientific inquiry with appropriate instructional support. Our findings are consistent with recent work by researchers such as Metz (2004), who argue for a richer conception of children's developmental capacities in the context of science instruction. As educators develop new science curricula and programs to address the lack of rich and challenging science instruction in the early grades, there is a need to document what children learn from such efforts. In order to develop research-based and pedagogically effective science curricula, we need assessments with clearly described theoretical and psychometric characteristics. The SLA is one example of such an assessment that can be used to aggregate and compare learning outcomes, as well as to provide empirical information on kindergarten children's capacities for science learning.
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References
1.
American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (1999). Standards for educational and psychological testing (2nd ed.). Washington, DC: American Educational Research Association.
BeauducelA.HerzbergP. Y. (2006). On the performance of maximum likelihood versus means and variance adjusted weighted least squares estimation in CFA. Structural Equation Modeling, 13, 186–203.
4.
BoydR.RichersonP. J. (2005). The origin and evolution of cultures. Oxford, UK: Oxford University Press.
5.
BrownA. L. (1997). Transforming schools into communities of thinking and learning about serious matters. American Psychologist, 32, 399–413.
6.
CareyS.SpelkeE. (1994). Domain specific knowledge and conceptual change. In HirschfeldL. A.GelmanS. A. (Eds.), Mapping the mind (pp. 169–200). Cambridge, UK: Cambridge University Press.
7.
Center for Science and Mathematics Education. (1996). National science education standards. Retrieved February 11, 2007, from http://books.nap.edu/html/nses/1.html
8.
ChinnC. A.SamarapungavanA. (2005, July). Toward a broader conceptualization of epistemology in science education. Paper presented at the meeting of the Eighth International History, Philosophy, Sociology, and Science Education Conference, University of Leeds, England.
9.
CohenJ. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
10.
DriverR.AsokoH.LeachJ.MortimerE.ScottP. (1994). Constructing scientific knowledge in the classroom. Educational Researcher, 23, 5–12.
11.
EbelR. T. (1954). Procedures for the analysis of classroom tests. Educational and Psychological Measurement, 14, 352–364.
12.
FinneyS. J.DiStefanoC. (2006). Non-normal and categorical data in structural equation modeling. In HancockG. R.MuellerR. O. (Eds.), Structural equation modeling: A second course (pp. 269–314). Greenwood, CT: Information Age.
13.
FloraD. B.CurranP. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9, 466–491.
14.
FrenchL. (2004). Science as the center of a coherent, integrated early childhood curriculum. Early Childhood Research Quarterly, 19, 138–149.
15.
GelmanR.BrennemanK. (2004). Science learning pathways for young children. Early Childhood Research Quarterly, 19, 150–158.
16.
GitomerG.DuschlR. A. (1998). Emerging issues and practices in science assessment. In FraserB. J.TobinK. G. (Eds.), International handbook of science education (pp. 791–810). Boston: Kluwer Academic.
17.
GreenoJ. G. (1998). The situativity of knowing, learning, and research. American Psychologist, 53, 5–26.
18.
HammerD.ElbyA. (2002). On the form of a personal epistemology. In HoferB. K.PintrichP. R. (Eds.), Personal epistemology: The psychology of beliefs about knowledge and knowing (pp. 169–190). Mahwah, NJ: Lawrence Erlbaum.
19.
HuL.BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.
KelleyT. L. (1939). The selection of upper and lower groups for the validation of test items. Journal of Educational Psychology, 30, 17–24.
22.
KlahrD. (2000). Exploring science: The cognition and development of discovery processes. Cambridge, MA: MIT Press.
23.
KleinE. R.HammrichP. L.BloomS.RaginsA. (2000). Language development and science inquiry: The Head Start on Science and Communication program. Early Childhood Research and Practice, 2, 1–22.
24.
KuhnD.AmselE.O'LoughlinM. (1988). The development of scientific reasoning skills. Orlando, FL: Academic Press.
25.
KuhnD.DeanD. (2005). Is developing scientific thinking all about learning to control variables?Psychological Science, 16, 866–870.
26.
MantzicopoulosP.PatrickH.SamarapungavanA. (2005). The scientific literacy project. Grant proposal to the Institute of Education. Unpublished manuscript.
27.
McGrewK. S.WoodcockR. W. (2001). Technical manual, Woodcock-Johnson III. Itasca, IL: Riverside.
28.
MeiselsS. J.LiauwF.DorfmanA.NelsonR. F. (1995). The work sampling system: Reliability and validity of a performance assessment for young children. Early Childhood Research Quarterly, 10, 277–296.
29.
MessickS. (1994). The interplay of evidence and consequences in the validation of performance assessments. Educational Researcher, 23, 12–23.
30.
MetzK. E. (2004). Children's understanding of scientific inquiry: Their conceptualization of uncertainty in investigations of their own design. Cognition and Instruction, 22, 219–290.
31.
MuthénL. K.MuthénB. O. (2004). Mplus user's guide (3rd ed.). Los Angeles: Muthén & Muthén.
32.
National Association for the Education of Young Children. (2003). Executive summary. Early learning standards: Creating the conditions for success. Young Children, 58, 69–70.
33.
National Research Council. (1996). National science education standards. Washington, DC: National Academy Press.
34.
RogoffB. (1990). Apprenticeship in thinking: Cognitive development in social context. New York: Oxford University Press.
35.
Ruiz-PrimoM. A.ShavelsonR. J.HamiltonL.KleinS. (2002). On the evaluation of systemic science education reform: Searching for instructional sensitivity. Journal of Research in Science Teaching, 39, 369–393.
36.
SamarapungavanA.MantzicopoulosP.PatrickH. (2008). Learning science through inquiry in kindergarten. Science Education, 92, 868–908.
37.
SchaubleL. (1996). The development of scientific reasoning in knowledge-rich contexts. Developmental Psychology, 32, 102–119.
38.
SchrankF. A.McGrewK. S.WoodcockR. W. (2001). Technical abstract (Woodcock-Johnson III Assessment Service Bulletin No. 2). Itasca, IL: Riverside.
39.
SheppardL. (2000). The role of assessment in a learning culture. Educational Researcher, 29, 4–14.
40.
ShepardL.KaganS. L.WurtsE. (1998) Recommendations and principles for early childhood assessments. Washington, DC: National Educational Goals Panel Report. (ERIC Document Reproduction Service No. ED416033)
41.
ThompsonB.DanielL. G. (1996). Factor analytic evidence for the construct validity of scores: A historical overview and some guidelines. Educational and Psychological Measurement, 56, 197–208.
42.
TsaiM.SamarapungavanA., (2007, October). Understanding the characteristics of gifted kindergarten students in science learning. Paper presented at the annual conference of the National Association for Gifted Children, Minneapolis, MN.
43.
TsaiM.SamarapungavanA. (2009, April). Identifying the characteristics of gifted kindergarten students in science learning. Paper presented at the annual conference of the American Educational Research Association, San Diego, CA.
44.
WoodcockR. W.McGrewK. S.MatherN. (2001). Woodcock-Johnson III Tests of Achievement. Itasca, IL: Riverside.
45.
YuC. Y.MuthénB. (2002). Evaluation of model fit indices for latent variable models with categorical and continuous outcomes (Tech. Rep.). Los Angeles: University of California at Los Angeles, Graduate School of Education & Information Studies.
