Skip to main content

Leveraging a Sorting Task as a Measure of Knowledge Structure in Bilingual Settings

Abstract

This descriptive exploratory study considers whether a simple sorting task can elicit readers’ knowledge structure in learners’ first and second language. In this investigation, knowledge structure is considered from a symbolic connectionist viewpoint as the fundamental pre-meaningful aspect of knowledge, where structure is the precursor of knowledge acquisition and the underpinning of meaningful activity. Chinese–English (C–E) bilingual participants (n = 23) were assigned to one of four counter-balanced conditions to complete two presorting tasks, read an English expository text passage, and then complete two post-sorting tasks including: CE-read-EC, CE-read-EC, EC-read-EC, and EC-read-CE. Data analysis focused on the knowledge structure measures elicited by the four sorting tasks. Results show that both the reading and the sorting elicitation task itself differentially influenced knowledge structure; the Chinese post-sorting task immediately after reading the English text led to a relatively more relational structure than did the English post-sorting task. Individual’s knowledge structure elicited by the sorting tasks was not much like those of other students, but each individual’s self-to-self structure in Chinese and English were somewhat alike before reading (r = .35, 13 % overlap) and moderately alike after reading (r = .62, 39 % overlap). These findings add to the evidence base that sorting tasks can elicit knowledge structures in dual language settings.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  • Anderson, R. C. (1984). Some reflections on the acquisition of knowledge. Educational Researcher, 13(10), 5–10.

    Article  Google Scholar 

  • Aronoff, J. M., Gonnermanb, L. M., Almorc, A., Arunachalam, S., Kempler, D., & Andersen, E. S. (2005). Information content versus relational knowledge: Semantic deficits in patients with Alzheimer’s disease. Neuropsychologia, 44, 21–35.

    Article  Google Scholar 

  • Bonilla, J. L., & Johnson, M. K. (1995). Semantic space in Alzheimer’s disease patients. Neuropsychology, 9(3), 345–353.

    Article  Google Scholar 

  • Britton, B. K., & Gülgöz, S. (1991). Using Kintsch’s computational model to improve instructional text: Effects of repairing inference calls on recall and cognitive structures. Journal of Educational Psychology, 83(3), 329–345.

    Article  Google Scholar 

  • Champagne, A. B., Klopfer, L. E., Desena, A. T., & Squires, D. A. (1981). Structural representations of student’s knowledge before and after science instruction. Journal of Research in Science Teaching, 18(2), 97–111.

    Article  Google Scholar 

  • Clariana, R. B. (2010). Multi-decision approaches for eliciting knowledge structure. In D. Ifenthaler, P. Pirnay-Dummer, & N. M. Seel (Eds.), Computer-based diagnostics and systematic analysis of knowledge (pp. 41–59). Berlin: Springer.

    Chapter  Google Scholar 

  • Clariana, R. B., Rysavy, M. D., & Taricani, E. M. (2015). Text signals influence team artifacts. Educational Technology Research and Development, 63, 35–52.

    Article  Google Scholar 

  • Clariana, R. B., & Wallace, P. (2007). A computer-based approach for deriving and measuring individual and team knowledge structure from essay questions. Journal of Educational Computing Research, 37(3), 211–227.

    Article  Google Scholar 

  • Clariana, R. B., & Wallace, P. E. (2009). A comparison of pair-wise, list-wise, and clustering approaches for eliciting structural knowledge. International Journal of Instructional Media, 36(3), 287–302.

    Google Scholar 

  • Clark, D. B., D’Angelo, C. M., & Schleigh, S. P. (2011). Comparison of student’s knowledge Structure coherence and understanding of force in the Philippines, Turkey, China, Mexico, and the United States. Journal of the Learning Sciences, 20, 207–261. doi:10.1080/10508406.2010.508028.

    Article  Google Scholar 

  • Collins, A. M., & Quillian, M. R. (1969). Retrieval lime from long-term memory. Journal of Verbal Learning and Verbal Behavior, 8, 240–247.

    Article  Google Scholar 

  • Davis, M. H., & Guthrie, J. T. (2015). Measuring reading comprehension of content area texts using an assessment of knowledge organization. The Journal of Educational Research, 108(2), 148–164. doi:10.1080/00220671.2013.863749.

    Article  Google Scholar 

  • Fitzpatrick, T., & Izura, C. (2011). Word associations in L1 and L2: An exploratory study of response types, response times, and interlingual mediation. Studies in Second Language Acquisition, 33, 373–398.

    Article  Google Scholar 

  • Fuessel, D., & Isermann, R. (2000). Hierarchical motor diagnosis utilizing structural knowledge and a self-learning neuro-fuzzy scheme. IEEE Transactions on Industrial Electronics, 47(5), 1070–1077.

    Article  Google Scholar 

  • Galton, F. R. S. (1879). Psychometric experiments. Brain, 2, 149–162.

    Article  Google Scholar 

  • Gonzalvo, P., Canas, J. J., & Bajo, M. (1994). Structural representations in knowledge acquisition. Journal of Educational Psychology, 86(4), 601–616.

    Article  Google Scholar 

  • Günther, J., Bergner, A., Hendlich, M., & Klebe, G. (2003). Utilizing structural knowledge in drug design strategies: Applications using Relibase. Journal of Molecular Biology, 326(2), 621–636.

    Article  Google Scholar 

  • Guthrie, J. T., Wigfield, A., Barbosa, P., Perencevich, K. C., Taboada, A., Davis, M. H., et al. (2004). Increasing reading comprehension and engagement through concept-oriented reading Instruction. Journal of Educational Psychology, 96, 403–423. doi:10.1037/0022-0663.96.3.403.

    Article  Google Scholar 

  • Hakan, K. U. R. T., Ekici, G., Aktas, M., & Aksu, O. (2013). Determining biology student teachers’ cognitive structure on the concept of “diffusion” through the free word-association test and the drawing-writing technique. International Education Studies, 6(9), p187.

    Google Scholar 

  • Hwang, G. J., Wu, P. H., & Ke, H. R. (2011). An interactive concept map approach to supporting mobile learning activities for natural science courses. Computers and Education, 57(4), 2272–2280.

    Article  Google Scholar 

  • Jonassen, D. H., Beissner, K., & Yacci, M. (1993). Structural knowledge: Techniques for representing, conveying, and acquiring structural knowledge. Hillsdale: Lawrence Erlbaum Associates.

    Google Scholar 

  • Kaufman, A. B., Green, S. R., Seitz, A. R., & Burgess, C. (2012). Using a self-organizing map (SOM) and the hyperspace analog to language (HAL) model to identify patterns of syntax and structure in the songs of humpback whales. International Journal of Comparative Psychology, 25, 237–275.

    Google Scholar 

  • Kim, K., & Clariana, R. B. (2015). Knowledge structure measures of reader’s situation models across languages: Translation engenders richer structure. Technology Knowledge and Learning, 20, 249–268.

    Article  Google Scholar 

  • Kroll, J. F., & de Groot, A. M. B. (2014). Lexical and conceptual memory in the bilingual: Mapping form to meaning in two languages. In J. F. Kroll & A. M. B. de Groot (Eds.), Tutorials in bilingualism (pp. 169–200). New York: Psychology Press.

    Google Scholar 

  • Martinez, M. M. (2010). Learning and cognition: The design of the mind. Upper Saddle River, NJ: Merrill.

    Google Scholar 

  • National Research Council. (2001). Knowing what students know: The science and design of educational assessment. Washington: National Academies Press.

    Google Scholar 

  • Novak, J. D. (1990). Concept mapping: A useful tool for science education. Journal of Research in Science Teaching, 27(10), 937–949.

    Article  Google Scholar 

  • Ober, B. A., & Shenaut, G. K. (1999). Well-organized conceptual domains in Alzheimer’s disease. Journal of the International Neuropsychological Society, 5(7), 676–684.

    Google Scholar 

  • Ozgungor, S., & Guthrie, J. T. (2004). Interactions among elaborative interrogation, knowledge, and interest in the process of constructing knowledge from text. Journal of Educational Psychology, 96, 437–443. doi:10.1037/0022-0663.96.3.437.

    Article  Google Scholar 

  • Rumlehart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. E. (1986). Schemata and sequential thought processes in PDP models. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 2, pp. 7–57)., Psychological and biological models Cambridge: MIT Press.

    Google Scholar 

  • Schvaneveldt, R. (1990). Pathfinder associative networks: Studies in knowledge organization (p. 1990). Norwood: Ablex.

    Google Scholar 

  • Schvaneveldt, R. (2016). Curated bibliography of 272 published investigation using Pathfinder network methods. http://interlinkinc.net/References.html.

  • Shavelson, R. J., & Stanton, G. C. (1975). Construct validation: Methodology and application to three measures of cognitive structure. Journal of Educational Measurement, 12(2), 67–85.

    Article  Google Scholar 

  • Spinozzi, G. (1993). Development of spontaneous classificatory behavior in chimpanzees (Pan troglodytes). Journal of Comparative Psychology, 107, 193–200. doi:10.1037/0735-7036.107.2.193.

    Article  Google Scholar 

  • Suen, H. K., & Murphy, L. C. R. (1999). Validating measures of structural knowledge through the multitrait-multimethod matrix. Presented at AERA.

  • Taricani, E. M., & Clariana, R. B. (2006). A technique for automatically scoring open-ended concept maps. Educational Technology Research and Development, 54(1), 65–82.

    Article  Google Scholar 

  • Trumpower, D. L., & Goldsmith, T. E. (2004). Structural enhancement of learning. Contemporary Educational Psychology, 29, 426–446.

    Article  Google Scholar 

  • Willson, V. L., & Rupley, W. H. (1997). A structural equation model for reading comprehension based on background, phonemic, and strategy knowledge. Scientific Studies of Reading, 1(1), 45–63.

    Article  Google Scholar 

  • Zareva, A., & Wolter, B. (2012). The ‘promise’ of three methods of word association analysis to L2 lexical research. Second Language Research, 28, 41–67.

    Article  Google Scholar 

  • Zhao, X., & Li, P. (2013). Simulating cross-language priming with a dynamic computational model of the lexicon. Bilingualism Language and Cognition, 16, 288–303. doi:10.1017/S1366728912000624.

    Article  Google Scholar 

Download references

Acknowledgments

This is a research project for Dr. Roy Clariana’s course, LDT 594 Research Topics. Special thanks go to Yaozu Dong, Yuanyuan Shang, Yu Yan, and Shulong Yan for the support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hengtao Tang.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tang, H., Clariana, R. Leveraging a Sorting Task as a Measure of Knowledge Structure in Bilingual Settings. Tech Know Learn 22, 23–35 (2017). https://doi.org/10.1007/s10758-016-9290-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10758-016-9290-z

Keywords

  • Knowledge structure
  • Sorting task
  • Second language reading
  • Measurement