RESEARCH PAPER
Probing the Relation between Students’ Integrated Knowledge and Knowledge-in-Use about Energy using Network Analysis
 
More details
Hide details
1
IPN - Leibniz Institute for Science and Mathematics Education at Kiel University, GERMANY
 
2
Department of Science Teaching, Weizmann Institute of Science, Rehovot, ISRAEL
 
3
Michigan State University, East Lansing, Michigan, USA
 
 
Online publication date: 2019-04-09
 
 
Publication date: 2019-04-09
 
 
EURASIA J. Math., Sci Tech. Ed 2019;15(8):em1728
 
KEYWORDS
TOPICS
ABSTRACT
Modern science standards emphasize knowledge-in-use, i.e., connecting scientific practices with content. For knowledge to become usable in knowledge-in-use performances, students need well organized knowledge networks that allow them to activate and connect sets of relevant ideas across contexts, i.e. students need integrated knowledge. We conducted a longitudinal interview study with 30 students in a 7th grade energy unit and used network analysis to investigate students’ integrated knowledge, i.e., their knowledge networks. Linking these results with results from knowledge-in-use assessments, we found a strong connection between integrated knowledge and knowledge-in-use about energy. Further, we found evidence that well-connected ideas around the idea of energy transfer were particularly helpful for using energy ideas in the knowledge-in-use assessments. We present network analysis as a valuable extension of existing approaches to investigating students’ knowledge networks and the connection between them and knowledge-in-use.
REFERENCES (68)
1.
Anderson, J. R. (1983). Cognitive science series. The architecture of cognition. Hillsdale, NJ, US.
 
2.
Anderson, J. R., & Schunn, C. (2000). Implications of the ACT-R learning theory: No magic bullets. Advances in Instructional Psychology, Educational Design and Cognitive Science, 1–33.
 
3.
Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77. https://doi.org/10.1145/213380....
 
4.
Blei, D. M., & Lafferty, J. D. (2007). A correlated topic model of Science. The Annals of Applied Statistics, 1(1), 17–35. https://doi.org/10.1214/07-AOA....
 
5.
Bond, T. G., & Fox, C. M. (2015). Applying the Rasch model: fundamental measurement in the human sciences (Third edition). New York ; London: Routledge, Taylor and Francis Group.
 
6.
Bransford, J. (2000). How people learn: brain, mind, experience, and school. (National Research Council (U.S.), Ed.) (Expanded ed). Washington, D.C: National Academy Press.
 
7.
Brewe, E. (2011). Energy as a substancelike quantity that flows: Theoretical considerations and pedagogical consequences. Physical Review Special Topics - Physics Education Research, 7(2), 020106. https://doi.org/10.1103/PhysRe....
 
8.
Chabalengula, V. M., Sanders, M., & Mumba, F. (2012). Diagnosing Students’ Understanding Of Energy And Its Related Concpets In Biological Contexts. International Journal of Science and Mathematics Education, 10(2), 241–266. https://doi.org/10.1007/s10763....
 
9.
Chen, R. F., Eisenkraft, A., Fortus, D., Krajcik, J., Neumann, K., Nordine, J., & Scheff, A. (2014). Teaching and learning of energy in K-12 education. Cham: Springer. Retrieved from http://gso.gbv.de/DB=2.1/PPNSE....
 
10.
Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and Representation of Physics Problems by Experts and Novices*. Cognitive Science, 5(2), 121–152. https://doi.org/10.1207/s15516....
 
11.
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1-9. http://igraph.sf.net.
 
12.
Derry, S. J. (1996). Cognitive schema theory in the constructivist debate. Educational Psychologist, 31(3–4), 163–174. https://doi.org/10.1080/004615....
 
13.
diSessa, A. A. (1988). Knowledge in Pieces. In G. Forman & P. Pufall (Eds.), Constructivism in the Computer Age (pp.49-70). Hillsdale, NJ: Lawrence Erlbaum.
 
14.
diSessa, A. A. (2013). A bird’s-eye view of the “pieces” vs “coherence” controversy (from the “pieces” side of the fence). In Stella Vosniadou (Ed.), International handbook of research on conceptual change (pp. 31–48). New York, NY: Routledge.
 
15.
diSessa, A. A., & Sherin, B. L. (1998). What changes in conceptual change? International Journal of Science Education, 20(10), 1155–1191. https://doi.org/10.1080/095006....
 
16.
Driver, R., & Warrington, L. (1985). Students’ Use of the Principle of Energy Conservation in Problem Situations. Physics Education, 20(4), 171–176.
 
17.
Duit, R. (2014). Teaching and Learning the Physics Energy Concept. In Chen, R.F., Eisenkraft, A., Fortus, D., Krajcik, J., Neumann, K., Nordine, J., and Scheff, A. (Eds.), Teaching and Learning of Energy in K-12 Education (pp. 67–85). Cham: Springer. Retrieved from http://link.springer.com/chapt....
 
18.
Duncan, R. G., & Hmelo-Silver, C. E. (2009). Learning progressions: Aligning curriculum, instruction, and assessment. Journal of Research in Science Teaching, 46(6), 606–609. https://doi.org/10.1002/tea.20....
 
19.
Duncan, R. G., & Rivet, A. E. (2013). Science Learning Progressions. Science, 339(6118), 396–397. https://doi.org/10.1126/scienc....
 
20.
Ellse, M. (1988). Transferring Not Transforming Energy. School Science Review, 69(248), 427–437.
 
21.
Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.
 
22.
Gilbert, J. K., Watts, D. M., & Osborne, R. J. (1982). Students’ conceptions of ideas in mechanics. Physics Education, 17(2), 62–66. https://doi.org/10.1088/0031-9....
 
23.
Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. (2008). Bayesian models of cognition.
 
24.
Harris, C. J., Krajcik, J. S., Pellegrino, J. W., & McElhaney, K. W. (2016). Constructing assessment tasks that blend disciplinary core Ideas, crosscutting concepts, and science practices for classroom formative applications. Menlo Park, CA.
 
25.
Hmelo-Silver, C., & Pfeffer, M. G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science, 28(1), 127–138. https://doi.org/10.1016/S0364-....
 
26.
Kauertz, A., & Fischer, H. E. (2006). Assessing students’ level of knowledge and analysing the reasons for learning difficulties in physics by Rasch analysis. Applications of Rasch Measurement in Science Education, 212–246.
 
27.
Koponen, I. T., & Huttunen, L. (2013). Concept Development in Learning Physics: The Case of Electric Current and Voltage Revisited. Science and Education, 22(9), 2227–2254. https://doi.org/10.1007/s11191....
 
28.
Landauer, T. K. (2014). Handbook of latent semantic analysis. New York: Routledge.
 
29.
Landis, J. R., & Koch, G. G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics, 33(1), 159. https://doi.org/10.2307/252931....
 
30.
Lee, H.-S., & Liu, O. L. (2010). Assessing learning progression of energy concepts across middle school grades. Science Education, 94(4), 665–688. https://doi.org/10.1002/sce.20....
 
31.
Lee, H.-S., Liu, O. L., & Linn, M. C. (2011). Validating Measurement of Knowledge Integration in Science Using Multiple-Choice and Explanation Items. Applied Measurement in Education, 24(2), 115–136. https://doi.org/10.1080/089573....
 
32.
Linn, M. C. (2006). The Knowledge Integration Perspective on Learning and Instruction. In The Cambridge handbook of: The learning sciences. New York, NY: Cambridge University Press.
 
33.
Liu, O. L., Lee, H.-S., Hofstetter, C., & Linn, M. (2008). Assessing Knowledge Integration in Science: Construct, Measures, and Evidence. Educational Assessment, 13(1), 33–55. https://doi.org/10.1080/106271....
 
34.
Liu, O. L., Ryoo, K., Linn, M. C., Sato, E., & Svihla, V. (2015). Measuring Knowledge Integration Learning of Energy Topics: A two-year longitudinal study. International Journal of Science Education, 37(7), 1044–1066. https://doi.org/10.1080/095006....
 
35.
Liu, X., & McKeough, A. (2005). Developmental growth in students’ concept of energy. Journal of Research in Science Teaching, 42(5), 493–517. https://doi.org/10.1002/tea.20....
 
36.
Loh, A. S. L., & Subramaniam, R. (2018). Mapping the knowledge structure exhibited by a cohort of students based on their understanding of how a galvanic cell produces energy. Journal of Research in Science Teaching. https://doi.org/10.1002/tea.21....
 
37.
Mayring, P. (2014). Qualitative Content Analysis. Beltz.
 
38.
McClelland, J. L., & Cleeremans, A. (2009). Connectionist Models. In T. Byrne, Axel Cleeremans, and P. Wilken (Eds.), Oxford Companion to Consciousness. New York: Oxford University Press.
 
39.
National Academies of Sciences, Engineering, and Medicine. (2018). How People Learn II: Learners, Contexts, and Cultures. Washington, D.C.: National Academies Press. https://doi.org/10.17226/24783.
 
40.
National Research Council. (2012). A framework for K-12 science education. Washington, D.C.: The National Academies Press. Retrieved from http://www.worldcat.org/oclc/7....
 
41.
Neumann, K., Kubsch, M., Nordine, J., Fortus, D., & Krajcik, J. (2018). Assessing students’ progression in developing a deeper understanding of energy. Paper presented at NARST 2018 national conference. Atlanta.
 
42.
Neumann, K., Viering, T., Boone, W. J., & Fischer, H. E. (2013). Towards a learning progression of energy. Journal of Research in Science Teaching, 50(2), 162–188. https://doi.org/10.1002/tea.21....
 
43.
NGSS Lead States. (2013). Next generation science standards. Washington DC: National Acad. Press.
 
44.
Nordine, J., Fortus, D., Krajcik, J., Neumann, K., & Lehavi, Y. (2018). Modelling Eergy Transfers between Systems to Support Energy Knowledge in Use. Manuscript submitted for publication.
 
45.
Nordine, J., Krajcik, J., & Fortus, D. (2011). Transforming energy instruction in middle school to support integrated understanding and future learning. Science Education, 95(4), 670–699. https://doi.org/10.1002/sce.20....
 
46.
Novak, J. D. (1990). Concept mapping: A useful tool for science education. Journal of Research in Science Teaching, 27(10), 937–949. https://doi.org/10.1002/tea.36....
 
47.
Opsahl, T. (2009). Structure and evolution of weighted networks (PhD Thesis). Queen Mary, University of London.
 
48.
Osborne, R. J., & Gilbert, J. K. (1980). A technique for exploring students’ views of the world. Physics Education, 15(6), 376.
 
49.
Papadouris, N., & Constantinou, C. P. (2016). Investigating middle school students’ ability to develop energy as a framework for analyzing simple physical phenomena. Journal of Research in Science Teaching, 53(1), 119–145. https://doi.org/10.1002/tea.21....
 
50.
Park, H.-J., & Friston, K. (2013). Structural and Functional Brain Networks: From Connections to Cognition. Science, 342(6158), 1238411–1238411. https://doi.org/10.1126/scienc....
 
51.
Pellegrino, J. W., Chudowsky, N., & Glaser, R. (2004). Knowing what Students Know (3. print). Washington, DC: National Acad. Press. Retrieved from http://gso.gbv.de/DB=2.1/PPNSE....
 
52.
Pellegrino, J. W., Wilson, M. R., Koenig, J. A., Beatty, A. S., National Research Council (U.S.) (Eds.). (2014). Developing assessments for the Next Generation Science Standards. Washington, D.C: The National Academies Press.
 
53.
Quinn, H. R. (2014). A Physicist’s Musings on Teaching About Energy, In Chen, R.F., Eisenkraft, A., Fortus, D., Krajcik, J., Neumann, K., Nordine, J., and Scheff, A. (Eds.), Teaching and Learning of Energy in K-12 Education. Cham: Springer. https://doi.org/10.1007/978-3-....
 
54.
R Development Core Team. (2008). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org.
 
55.
Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience. Scientometrics, 82(2), 263–287. https://doi.org/10.1007/s11192....
 
56.
Ruiz-Primo, M. A. (2004). Examining concept maps as an assessment tool. Proceedings of the First International Conference on Concept Mapping. Pamplona, Spain.
 
57.
Rumelhart, D. E., & McClelland, J. L. (1986). Parallel distributed processing: explorations in the microstructure of cognition. Cambridge, Mass: MIT Press.
 
58.
Schneider, M., & Stern, E. (2009). The Inverse Relation of Addition and Subtraction: A Knowledge Integration Perspective. Mathematical Thinking and Learning, 11(1–2), 92–101. https://doi.org/10.1080/109860....
 
59.
Schwartz, D. L., & Arena, D. (2013). Measuring What Matters Most, 192.
 
60.
Sekretariat der ständigen Konferenz der Kultusminister der Länder in der Bundesrepublik Deutschland. (2004). Bildungsstandards Physik-Mittlerer Schulabschluss.
 
61.
Smith III, J. P., diSessa, A., & Roschelle, J. (1994). Misconceptions Reconceived: A Constructivist Analysis of Knowledge in Transition. Journal of the Learning Sciences, 3(2), 115–163. https://doi.org/10.1207/s15327....
 
62.
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. https://doi.org/10.1002/tea.20....
 
63.
Swackhamer, G. (2005). Cognitive Resources for Understanding Energy.
 
64.
Swackhamer, G., & Hestenes, D. (2005). An energy concept inventory. Arizona State University.
 
65.
Thagard, P. (2000). Coherence in thought and action. Cambridge, Mass.: MIT Press.
 
66.
Watts, D. M. (1983). Some alternative views of energy. Physics Education, 18(5), 213. https://doi.org/10.1088/0031-9....
 
67.
Won, M., Krabbe, H., Ley, S. L., Treagust, D. F., & Fischer, H. E. (2017). Science Teachers’ Use of a Concept Map Marking Guide as a Formative Assessment Tool for the Concept of Energy. Educational Assessment, 22(2), 95–110. https://doi.org/10.1080/106271....
 
68.
Zehner, F., Sälzer, C., & Goldhammer, F. (2016). Automatic Coding of Short Text Responses via Clustering in Educational Assessment. Educational and Psychological Measurement, 76(2), 280–303. https://doi.org/10.1177/001316....
 
eISSN:1305-8223
ISSN:1305-8215
Journals System - logo
Scroll to top