Exploring Cluster Changes in Students’ Knowledge Structures Throughout General Chemistry

Ozcan Gulacar; Alexandra Milkey; Ingo Eilks

doi:10.29333/ejmste/7860

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Ozcan Gulacar ¹ ^* , Alexandra Milkey ¹, Ingo Eilks ²

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¹ University of California, Davis, USA
² University of Bremen, GERMANY
^* Corresponding Author

Abstract

Chemistry is traditionally perceived as difficult to comprehend. Its mastery requires that a variety of concepts be linked to form an organized knowledge system. The connections need to be made not only between the concepts associated with the macroscopic level of the chemistry triplet but also between the submicroscopic and symbolic levels. Many factors influence a learner’s success in bridging concepts between these levels. In this study, the aim was to identify and examine the changes in general chemistry students’ knowledge structures by utilizing Word Association Tests. Although many studies have examined knowledge structures and aspects of the chemistry triplet, almost none has considered both at the same time. This study highlights the interconnectedness between the chemistry triplet and changing knowledge structures in overall student populations and in high- and low-achieving students. It provides insights on why students fail to understand chemistry and suggests ideas for future research as limiting factors were noted.

Keywords

chemistry education research
learning theories
constructivist theory
first year undergraduate chemistry
knowledge structures

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

https://doi.org/10.29333/ejmste/7860

EURASIA J Math Sci Tech Ed, 2020 - Volume 16 Issue 6, Article No: em1850

Publication date: 28 Mar 2020

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References

Abraham, M. R., Williamson, V. M., & Westbrook, S. L. (1994). A cross-age study of the understanding of five chemistry concepts. Journal of Research in Science Teaching, 31(2), 147-165. https://doi.org/10.1002/tea.3660310206
Adadan, E., & Savasci, F. (2012). An analysis of 16-17-year-old students’ understanding of solution chemistry concepts using a two-tier diagnostic instrument. International Journal of Science Education, 34(4), 513-544. https://doi.org/10.1080/09500693.2011.636084
Adadan, E., Trundle, K. C., & Irving, K. E. (2010). Exploring Grade 11 students’ conceptual pathways of the particulate nature of matter in the context of multirepresentational instruction. Journal of Research in Science Teaching, 47(8), 1004-1035. https://doi.org/10.1002/tea.20366
Ausubel, D., Novak, J., & Hanesian, H. (1986). Educational psychology: A cognitive view (2 ed.). New York: Holt, Rinehart and Winston.
Bahar, M., & Hansell, M. H. (2000). The Relationship Between Some Psychological Factors and their Effect on the Performance of Grid Questions and Word Association Tests. Educational Psychology, 20(3), 349-364. https://doi.org/10.1080/713663739
Blake, A. J. D., & Nordland, F. H. (1978). Science instruction and cognitive growth in college students. Journal of Research in Science Teaching, 15(5), 413-419. https://doi.org/10.1002/tea.3660150515
Çalik, M. (2005). A cross-age study of different perspectives in solution chemistry from junior to senior high school. International Journal of Science and Mathematics Education, 3(4), 671-696. https://doi.org/10.1007/s10763-005-1591-y
Chemical Education: Towards Research-based Practice. (2003). (J. K. Gilbert, O. De Jong, R. Justi, D. F. Treagust, & J. H. van Driel Eds. 1 ed.). The Netherlands: Springer.
De Jong, O., Blonder, R., & Oversby, J. (2013). How to balance chemistry education between observing phenomena and thinking in models. In I. Eilks & A. Hofstein (Eds.), Teaching Chemistry - A Studybook (pp. 97-126). Rotterdam, The Netherlands: Sense.
Derman, A., & Eilks, I. (2016). Using a word association test for the assessment of high school students’ cognitive structures on dissolution. Chemistry Education Research and Practice, 17(4), 902-913. https://doi.org/10.1039/C6RP00084C
Dori, Y. J., & Hameiri, M. (1998). The “Mole environment” studyware: Applying multidimensional analysis to quantitative chemistry problems. International Journal of Science Education, 20, 317-333. https://doi.org/10.1080/0950069980200305
Dori, Y. J., & Hameiri, M. (2003). Multidimensional analysis system for quantitative chemistry problems -symbol, macro, micro and process aspects. Journal of Research in Science Teaching, 40, 278-302. https://doi.org/10.1002/tea.10077
Dori, Y. J., & Sasson, I. (2008). Chemical understanding and graphical skills in an honors case-based computerized chemistry laboratory environment: The value of bidirectional visual and textual representations. Journal of Research in Science Teaching, 45(2), 219-250. https://doi.org/10.1002/tea.20197
Ebenezer, J. V. (2001). A hypermedia environment to explore and negotiate students’ conceptions: Animation of the solution process of table salt. Journal of Science Education and Technology, 10(1), 73-92. https://doi.org/10.1023/A:1016672627842
Ebenezer, J. V., & Erickson, G. L. (1996). Chemistry students’ conceptions of solubility: A phenomenography. Science Education, 80(2), 181-201. https://doi.org/10.1002/(SICI)1098-237X(199604)80:2<181::AID-SCE4>3.0.CO;2-C
Eilks, I. (2013). Teacher pathways through the particulate nature of matter in lower secondary school chemistry: Continuous switching between different models or a coherent conceptual structure? In G. Tsaparlis & H. Sevian (Eds.), Concepts of Matter in Science Education (pp. 213-230). Dordrecht, The Netherlands: Springer.
Garskof, B. E., & Houston, J. P. (1963). Measurement of verbal relatedness: An idiographic approach. Psychological Review, 70(3), 277-288. https://doi.org/10.1037/h0041879
Gilbert, J., de Jong, O., Justi, R., Treagust, D. F., & van Driel, J. H. (2003). Chemical Education: Towards Research-based Practice. Dordrecht, The Netherlands: Kluwer.
Gulacar, O., Sinan, O., Bowman, C. R., & Yildirim, Y. (2014). Exploring the Changes in Students’ Understanding of the Scientific Method Using Word Associations. Research in Science & Technological Education, 45, 717-726. https://doi.org/10.1007/s11165-014-9443-9
Haidar, A., H., & Abraham, M. R. (1991). A comparison of applied and theoretical knowledge of concepts based on the particulate nature of matter. Journal of Research in Science Teaching, 28(10), 919-938. https://doi.org/10.1002/tea.3660281004
Hewson, M. G., & Hewson, P. W. (1983). Effect of instruction using students’ prior knowledge and conceptual change strategies on science learning. Journal of Research in Science Teaching, 20(8), 731-743. https://doi.org/10.1002/tea.3660200804
Hovardas, T., & Korfiatis, K. J. (2006). Word associations as a tool for assessing conceptual change in science education. Learning and Instruction, 16(5), 416-432. https://doi.org/10.1016/j.learninstruc.2006.09.003
Isa, A. M., & Maskill, R. (1982). A comparison of science word meaning in the classrooms of two different countries: Scottish integrated science in Scotland and in Malaysia. British Journal of Educational Psychology, 52(2), 188-198. https://doi.org/10.1111/j.2044-8279.1982.tb00825.x
Johnson, P. E. (1967). Some psychological aspects of subject-matter nature. Journal of Educational Psychology, 58(2), 75-83. https://doi.org/10.1037/h0024465
Johnson, P. E. (1969). On the communication of concepts in science. Journal of Educational Psychology, 60(1), 32-40. https://doi.org/10.1037/h0026691
Johnstone, A. H. (1991). Why Is Science Difficult to Learn? Things Are Seldom What They Seem. Journal of Computer Assisted Learning, 7(2), 75-83. https://doi.org/10.1111/j.1365-2729.1991.tb00230.x
Johnstone, A. H. (2000). Teaching of chemistry - logical or psychological? Chemistry Education Research and Practice, 1(1), 9-15. https://doi.org/10.1039/A9RP90001B
Jonassen, D. H., & Marra, R. M. (1994). Concept mapping and other formalisms as mindtools for representing knowledge. Alt-J, 2(1), 50-56. https://doi.org/10.3402/rlt.v2i1.9573
JPathfinder. (2019). JPathfinder. Retrieved from https://interlinkinc.net/JPathfinder.htm
Lee, K.-W. (1986). Problem solving in electrochemistry: Variables, strategies, and teaching and learning. Monash University, Melbourne, Australia,
Lee, K.-W. (1988). Two non-traditional measures of chemistry learning: Word association and idea association. Research in Science Education, 18(1), 169-176. https://doi.org/10.1007/BF02356592
Levy Nahum, T., Mamlok‐Naaman, R., Hofstein, A., & Taber, K. S. (2010). Teaching and learning the concept of chemical bonding. Studies in Science Education, 46(2), 179-207. https://doi.org/10.1080/03057267.2010.504548
Liu, X., & Ebenezer, J. (2018). Descriptive Categories and Structural Characteristics of Students’ Conceptions: An exploration of the relationship. Research in Science & Technological Education, 20(1), 111-132. https://doi.org/10.1080/02635140220130966
Liu, X., & Lesniak, K. M. (2005). Students’ progression of understanding the matter concept from elementary to high school. Science Education, 89(3), 433-450. https://doi.org/10.1002/sce.20056
Magnusson, S., Krajcik, J., & Borko, H. (1999). Nature, sources, and development of pedagogical content knowledge for science teaching. In J. Gess-Newsome & N. G. Lederman (Eds.), Examining pedagogical content knowledge: The construct and its implication for science education (pp. 95-132). Dordrecht, The Netherlands: Kluwer.
Markic, S., Broggy, J., & Childs, P. (2013). How to deal with linguistic issues in chemistry classes. In I. Eilks & A. Hofstein (Eds.), Teaching Chemistry - A Studybook (pp. 127-152). Rotterdam, The Netherlands: Sense.
Maskill, R., & Cachapuz, A. F. C. (1989). Learning about the chemistry topic of equilibrium: the use of word association tests to detect developing conceptualizations. International Journal of Science Education, 11(1), 57-69. https://doi.org/10.1080/0950069890110106
McGowen, M.A. (1998). Cognitive units, concept images, and colligative collages: An examination of the processes of knowledge construction (Doctoral dissertation). Available from ProQuest Dissertations & Theses Global database. (UMI No. 62186328).
Nakhleh, M. B. (1992). Why some students don’t learn chemistry. Journal of Chemical Education, 69(3), 191-196. https://doi.org/10.1021/ed069p191
Nakiboglu, C. (2008). Using word associations for assessing non major science students’ knowledge structure before and after general chemistry instruction: the case of atomic structure. Chemistry Education Research and Practice, 9(4), 309-322. https://doi.org/10.1039/b818466f
Othman, J., Treagust, D. F., & Chandrasegaran, A. L. (2008). An Investigation into the Relationship between Students’ Conceptions of the Particulate Nature of Matter and their Understanding of Chemical Bonding. International Journal of Science Education, 30(11), 1531-1550. https://doi.org/10.1080/09500690701459897
Petrey, S. (1977). Word associations and the development of lexical memory. Cognition, 5(1), 57-71. https://doi.org/10.1016/0010-0277(77)90017-8
Schizas, D., Katrana, E., & Stamou, G. (2013). Introducing network analysis into science education: Methodological research examining secondary school students’ understanding of ‘decomposition’. International Journal of Environmental and Science Education, 8(1), 175-198.
Schvaneveldt, R. W. (1990). Proximities, networks, and schemata. In R. W. Schvaneveldt (Ed.), Pathfinder associative networks: Studies in knowledge organization (pp. 135-148). Norwood, NJ: Ablex.
Şendur, G., Özbayrak, Ö., & Uyulgan, M. A. (2011). A study of determination of pre-service chemistry teachers’ understanding about acids and bases. Procedia Computer Science, 3, 52-56. https://doi.org/10.1016/j.procs.2010.12.010
Shavelson, R. J. (1972). Some aspects of the correspondence between content structure and cognitive structure in physics instruction. Journal of Educational Psychology, 63(3), 225-234. https://doi.org/10.1037/h0032652
Shavelson, R. J. (1974). Methods for examining representations of a subject-matter structure in a student’s memory. Journal of Research in Science Teaching, 11(3), 231-249. https://doi.org/10.1002/tea.3660110307
Spekkink, W. (2015). MDS Layout.
Spekkink, W. (2019). A simple example of Multidimensional Scaling with R and Gephi. Retrieved from https://www.wouterspekkink.org/r/mds/gephi/2015/12/15/a-simple-example-of-mds-using-r-and-gephi.html
Taber, K. S. (2008). Conceptual Resources for Learning Science: Issues of transience and grain‐size in cognition and cognitive structure. International Journal of Science Education, 30(8), 1027-1053. https://doi.org/10.1080/09500690701485082
Thomson, D. M., & Tulving, E. (1970). Associative encoding and retrieval: Weak and strong cues. Journal of Experimental Psychology, 86(2), 255-262. https://doi.org/10.1037/h0029997
Tsai, C.-C. (2001). Probing students’ cognitive structures in science: the use of a flow map method coupled with a meta-listening technique. Studies in Educational Evaluation, 27(3), 257-268. https://doi.org/10.1016/S0191-491X(01)00029-3
Tyson, L. M., Venville, G. J., Harrison, A. G., & Treagust, D. F. (1997). A multidimensional framework for interpreting conceptual change events in the classroom. Science Education, 81(4), 387-404. https://doi.org/10.1002/(SICI)1098-237X(199707)81:4<387::AID-SCE2>3.0.CO;2-8
Uzuntiryaki, E., & Geban, Ö. (2005). Effect of conceptual change approach accompanied with concept mapping on understanding of solution concepts. Instructional Science, 33(4), 311-339. https://doi.org/10.1007/s11251-005-2812-z
von der Heidt, T. (March 01, 2015). Concept maps for assessing change in learning: a study of undergraduate business students in first-year marketing in China. Assessment & Evaluation in Higher Education, 40, 2, 286-308.
West, L., Fensham, P., & Garrand, J. (1985). Describing the cognitive structures of learners following instruction in chemistry. In L. West & L. Pines (Eds.), Cognitive structure and conceptual change (pp. 29-49). Orlando, Florida: Academic Press.
White, R. T. (1985). Interview protocols and dimensions of cognitive structure. In L. West & L. Pines (Eds.), Cognitive structure and conceptual change (pp. 51-59). Orlando, Florida: Academic Press.
Yildirir, H. E., & Demirkol, H. (2018). Revealing students’ cognitive structure about physical and chemical change: Use of a word association test. European Journal of Education Studies, 4(1), 134-154. https://doi.org/10.5281/zenodo.1156414

How to cite this article

Vancouver

Gulacar O, Milkey A, Eilks I. Exploring Cluster Changes in Students’ Knowledge Structures Throughout General Chemistry. EURASIA J Math Sci Tech Ed. 2020;16(6):em1850. https://doi.org/10.29333/ejmste/7860

APA

Gulacar, O., Milkey, A., & Eilks, I. (2020). Exploring Cluster Changes in Students’ Knowledge Structures Throughout General Chemistry. Eurasia Journal of Mathematics, Science and Technology Education, 16(6), em1850. https://doi.org/10.29333/ejmste/7860

AMA

Gulacar O, Milkey A, Eilks I. Exploring Cluster Changes in Students’ Knowledge Structures Throughout General Chemistry. EURASIA J Math Sci Tech Ed. 2020;16(6), em1850. https://doi.org/10.29333/ejmste/7860

Chicago

Gulacar, Ozcan, Alexandra Milkey, and Ingo Eilks. "Exploring Cluster Changes in Students’ Knowledge Structures Throughout General Chemistry". Eurasia Journal of Mathematics, Science and Technology Education 2020 16 no. 6 (2020): em1850. https://doi.org/10.29333/ejmste/7860

Harvard

Gulacar, O., Milkey, A., and Eilks, I. (2020). Exploring Cluster Changes in Students’ Knowledge Structures Throughout General Chemistry. Eurasia Journal of Mathematics, Science and Technology Education, 16(6), em1850. https://doi.org/10.29333/ejmste/7860

MLA

Gulacar, Ozcan et al. "Exploring Cluster Changes in Students’ Knowledge Structures Throughout General Chemistry". Eurasia Journal of Mathematics, Science and Technology Education, vol. 16, no. 6, 2020, em1850. https://doi.org/10.29333/ejmste/7860