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Corroborating a sorting task measure of individual and of local collective knowledge structure

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Abstract

This experimental investigation seeks to corroborate a knowledge structure sorting task approach as a measure to more fully account for prior knowledge when reading. A latent semantic analysis (LSA) network derived from thousands of texts typically read by first year college students was used to create a prototypical referent network model of the global collective knowledge structure of the key terms in the text. Bilingual Chinese-English participants (n = 205) were randomly assigned to four treatments to sort terms in both languages, then to read an English expository text of an unfamiliar topic, then sort in both languages again, and lastly complete a comprehension posttest. All pre- and post- sorting tasks data were converted to Pathfinder networks as measures of knowledge structure. Multiword clusters in the LSA network were present in the initial pre-reading group-average sorting networks of both languages, but especially in Chinese (their L1), and these clusters tended to persist after reading. Reading had only a small influence on the post-reading group-average networks. Sorting in Chinese had a stronger influence downstream than did sorting in English (L1 > L2 influence). For researchers, these innovative approaches to establish local and global collective knowledge networks show promise as complementary measures to explain learning in terms of knowledge structure alignment and transitions, and pragmatically, sorting tasks are relatively easy to implement and interpret in real classrooms as formative diagnostic measures of conceptual understanding.

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References

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

    Article  Google Scholar 

  • Arnon, I. (2009). Starting big—The role of mulit-word phrases in language learning and use. [Doctoral Dissertation], Stanford University. https://tinyurl.com/Arnon-2009

  • Arnon, I., & Christiansen, M. H. (2017). The role of multiword building blocks in explaining L1–L2 differences. Topics in Cognitive Science, 9, 621–636. https://doi.org/10.1111/tops.12271

    Article  Google Scholar 

  • Asino, T., Clariana, R.B., Dong, Y., Groff, B., Ntshalintshali, G., Taricani, E., Techatassanasoontorn, C., & Yu, W. (2012). The effect of independent and interdependent group collaboration on knowledge extent, knowledge form, and knowledge convergence. In Proceedings of selected research and development papers presented at the national convention of the association for educational communications and technology (Vol. 35, pp. 20–29) (Louisville, KY, November 2012). https://members.aect.org/pdf/Proceedings/proceedings12/2012/12_02.pdf

  • Balloo, K., Pauli, R., & Worrell, M. (2016). Individual differences in psychology undergraduates’ development of research methods knowledge and skills. Procedia-Social and Behavioral Sciences, 217, 790–800.

    Article  Google Scholar 

  • Barron, B. (2003). When smart groups fail. Journal of the Learning Sciences, 12(3), 307–359.

    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 

  • Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school. National Academy Press.

    Google Scholar 

  • Britton, B. K., & Gülöz, 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, 329–345.

    Article  Google Scholar 

  • Brysbaert, M., Mandera, P., McCormick, S. F., & Keuleers, E. (2019). Word prevalence norms for 62,000 English lemmas. Behavior Research Methods, 51, 467–479. https://doi.org/10.3758/s13428-018-1077-9

    Article  Google Scholar 

  • Chan, S. S., Butters, N., & Salmon, D. P. (1997). The deterioration of semantic networks in patients with Alzheimer’s disease: A cross-sectional study. Neuropsychologia, 35(3), 241–248.

    Article  Google Scholar 

  • Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81.

    Article  Google Scholar 

  • Chen, B., Scardamalia, M., Resendes, M., Chuy, M., & Bereiter, C. (2012). Students’ intuitive Understanding of promising-ness and promising-ness judgments to facilitate knowledge advancement. In Proceedings of the 10th international conference of the learning sciences: The future of learning, ICLS 2012 - Sydney, NSW, Australia.

  • Chen, W. (2017). Knowledge convergence among pre-service mathematics teachers through online reciprocal peer feedback. Knowledge Management & E-Learning, 9(1), 1–18.

    Google Scholar 

  • Chen, X., Dong, Y., & Yu, X. (2018). On the predictive validity of various corpus-based frequency norms in L2 English lexical processing. Behavior Research Methods, 50, 1–25. https://doi.org/10.3758/s13428-017-1001-8

    Article  Google Scholar 

  • Chi, M. T., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.

    Article  Google Scholar 

  • Chi, M. T., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. Sternberg (Ed.), Advances in the psychology of human intelligence (pp. 7–75). Erlbaum.

    Google Scholar 

  • Chollet, S., Valentin, D., & Abdi, H. (2014). Free sorting task. In P. Varela & G. Ares (Eds.), Novel techniques in sensory characterization and consumer profiling. CRC Press, Taylor and Francis. https://doi.org/10.1201/b16853

  • Christiansen, M. H., & Arnon, I. (2017). More than words: The role of multiword sequences in language learning and use. Topics in Cognitive Science, 9, 542–551. https://doi.org/10.1111/tops.12274

    Article  Google Scholar 

  • Clariana, R. B. (2010a). Deriving group knowledge structure from semantic maps and from essays. In D. Ifenthaler, P. Pirnay-Dummer, & N. M. Seel (Eds.), Computer-based diagnostics and systematic analysis of knowledge (pp. 117–130). Springer.

    Chapter  Google Scholar 

  • Clariana, R. B. (2010b). 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 (Chapter 4) (pp. 41–59). Springer.

    Chapter  Google Scholar 

  • Clariana, R. B., Follmer, D. J., & Li, P. (2019). Sentence versus paragraph processing: Linear and relational knowledge structure measures. Presented at the 7th International Workshop on Advanced Learning Sciences (IWALS 2019), June 17–19, 2019, University of Jyväskylä, Finland. https://www.slideshare.net/rbc4/sentence-versus-paragraph-processing-linear-and-relational-knowledge-structure-measures

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

    Google Scholar 

  • Clariana, R. B., Wallace, P. E., & Godshalk, V. M. (2009). Deriving and measuring group knowledge structure from essays: The effects of anaphoric reference. Educational Technology Research and Development, 57, 725–737.

    Article  Google Scholar 

  • Crowley, K., & Jacobs, M. (2002). Building islands of expertise in everyday family activity. In G. Leinhardt, K. Crowley, & K. Knutson (Eds.), Learning conversations in museums (pp. 333–256). Lawrence Erlbaum Associates.

    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.

    Article  Google Scholar 

  • Draper, D. C. (2010). The instructional effects of knowledge-based community of practice learning environments on student achievement and knowledge convergence. [Doctoral Dissertation], The Pennsylvania State University. https://etda.libraries.psu.edu/files/final_submissions/133

  • Elman, J. L. (2004). An alternative view of the mental lexicon. Trends in Cognitive Science, 8(7), 301–306.

    Article  Google Scholar 

  • Elman, J. L. (2009). On the meaning of words and dinosaur bones: Lexical knowledge without a lexicon. Cognitive Science, 33, 547–582.

    Article  Google Scholar 

  • Feng, S., & Law, N. (2021). Mapping artificial intelligence in education research: A network-based keyword analysis. International Journal of Artificial Intelligence in Education, 31, 277–303. https://doi.org/10.1007/s40593-021-00244-4

    Article  Google Scholar 

  • Fischer, F., & Mandl, H. (2005). Knowledge convergence in computer-supported collaborative learning—The role of external representation tools. Journal of the Learning Sciences, 14, 405–441.

    Article  Google Scholar 

  • Furtner, M. R., Rauthmann, J. F., & Sachse, P. (2009). Nomen est omen: Investigating the dominance of nouns in word comprehension with eye movement analyses. Advances in Cognitive Psychology, 5, 91–104.

    Article  Google Scholar 

  • Gentner, D. (1983). Structure-Mapping: A theoretical framework for analogy. Cognitive Science, 7, 155–170.

    Article  Google Scholar 

  • Gentner, D., & Hoyos, C. (2017). Analogy and abstraction. Topics in Cognitive Science, 9, 672–693. https://doi.org/10.1111/tops.12278

    Article  Google Scholar 

  • Gentner, D., & Markman, A. B. (1997). Structure mapping in analogy and similarity. American Psychologist, 52, 45–56.

    Article  Google Scholar 

  • Georgakopoulos, T., & Polis, S. (2018). The semantic map model: State of the art and future avenues for linguistic research. Language and Linguistics Compass, 12, 1–33. https://doi.org/10.1111/lnc3.12270

    Article  Google Scholar 

  • Glaser, B. G., & Strauss, A. L. (1967). Discovery of grounded theory: Strategies for qualitative research. Aldine.

    Google Scholar 

  • Grabowski, B. (2003). Generative learning contributions to the design of instruction and learning. In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology (2nd ed., pp. 719–743). Lawrence Erlbaum Associates.

    Google Scholar 

  • Graesser, A., Karnavat, A., Pomeroy, V. & Wiemer-Hastings, K. (2000). Latent Semantic Analysis captures casual, goal-oriented, and taxonomic structures. In Proceedings of the annual meeting of the cognitive science society, 22. https://escholarship.org/uc/item/2mw8430f

  • Günther, F., Dudschig, C., & Kaup, B. (2016). Latent semantic analysis cosines as a cognitive similarity measure: Evidence from priming studies. The Quarterly Journal of Experimental Psychology, 69(4), 626–653. https://doi.org/10.1080/17470218.2015.1038280

    Article  Google Scholar 

  • Hecker, A. (2012). Knowledge beyond the individual? Making sense of a notion of collective knowledge in organization theory. Organization Studies, 33(3), 423–445.

    Article  Google Scholar 

  • Hesse, M. B. (1966). Models and analogies in science. University of Notre Dame Press.

    Google Scholar 

  • Hesse, M. B. (2008). Models and analogies. In W. H. Newton-Smith (Ed.), A companion to the Philosophy of Science (pp. 299–307). Blackwell Publishers Ltd.

    Google Scholar 

  • Hulin, W. S., & Katz, D. (1935). The Frois-Wittmann pictures of facial expression. Journal of Experimental Psychology, 18(4), 482–498. https://doi.org/10.1037/h0056770

    Article  Google Scholar 

  • Ifenthaler, D. (2010). Relational, structural, and semantic analysis of graphical representations and concept maps. Educational Technology Research and Development, 58, 81–97.

    Article  Google Scholar 

  • Ifenthaler, D. (2011). Identifying cross-domain distinguishing features of cognitive structure. Educational Technology Research and Development, 59, 817–840.

    Article  Google Scholar 

  • Jeong, H., & Chi, M. (2007). Knowledge convergence and collaborative learning. Instructional Science, 35, 287–316.

    Article  Google Scholar 

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

    Google Scholar 

  • Kauhanen, I. (2006). Norms and sociolinguistic description. In A man of measure: festschrift in honour of Fred Karlsson on his 60th Birthday (pp. 34–46). (SKY Journal of Linguistics; Vol. 19, No. Special supplement). The Finnish Linguistics Association. http://www.linguistics.fi/julkaisut/SKY2006_1/1FK60.1.4.KAUHANEN.pdf

  • Kemp, C., & Tenenbaum, J. B. (2008). The discovery of structural form. Proceedings of the National Academy of Sciences, 105(31), 10687–10692. https://doi.org/10.1073/pnas.0802631105

    Article  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 

  • Kim, K., & Clariana, R. B. (2018). Text signals influence second language expository text comprehension: Knowledge structure analysis. Educational Technology Research and Development, 65, 909–830. https://doi.org/10.1007/s11423-016-9494-x

    Article  Google Scholar 

  • Kim, K., & Clariana, R. B. (2019). Applications of Pathfinder Network scaling for identifying the optimal use of a first language to support second language text comprehension. Educational Technology Research and Development, 67, 85–103. https://doi.org/10.1007/s11423-018-9607-9

    Article  Google Scholar 

  • Kim, K., Clariana, R. B., & Kim, Y. (2019). Automatic representation of knowledge structure: Enhancing learning through knowledge structure reflection in an online course. Educational Technology Research and Development, 67, 105–122. https://doi.org/10.1007/s11423-018-9626-6

    Article  Google Scholar 

  • Kintsch, W., & Mangalath, P. (2011). The construction of meaning. Topics in Cognitive Science, 3, 346–370. https://doi.org/10.1111/j.1756-8765.2010.01107.x

    Article  Google Scholar 

  • Koda, K. (2007). Reading and language learning: Cross linguistic constraints on second language reading development. Language Learning, 57(1), 1–44.

    Article  Google Scholar 

  • Krabbe, H. (2014). Digital concept mapping for formative assessment. In D. Ifenthaler & R. Hanewald (Eds.), Digital knowledge maps in education: Technology-enhance support for teachers and learners (pp. 275–297). Springer. https://doi.org/10.1007/978-1-4614-3178-7_15

  • Krethlow, G., Fargier, R., & Laganaro, M. (2020). Age-specific effects of lexical–semantic networks on word production. Cognitive Science, 44(11), e12915. https://doi.org/10.1111/cogs.12915

    Article  Google Scholar 

  • Kurtz, K. J., & Honke, G. (2020). Sorting out the problem of inert knowledge: Category construction to promote spontaneous transfer. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(5), 803–821. https://doi.org/10.1037/xlm0000750

    Article  Google Scholar 

  • Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104(2), 211–240. https://doi.org/10.1037/0033-295X.104.2.211

    Article  Google Scholar 

  • Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.

    Book  Google Scholar 

  • Leshchenko, Y., Dotsenko, T., & Ostapenko, T. (2015). Cross-linguistic interactions in bilingual mental lexicon and professional linguistic competence formation: An experimental research with native speakers of the Komi-Permyak and Russian languages. Social and Behavioral Sciences, 214, 1039–1047.

    Google Scholar 

  • Levy, P. (1999). Collective intelligence (R. Bononno, trans.). Perseus Books.

  • Li, P., & Clariana, R. B. (2019). Reading comprehension in L1 and L2: An integrative approach. Journal of Neurolinguistics, 50, 94–105.

    Article  Google Scholar 

  • Louwerse, M. M. (2011). Symbol interdependency in symbolic and embodied cognition. Topics in Cognitive Science, 3, 273–302.

    Article  Google Scholar 

  • Lu, L., Yuan, Y. C., & McLeod, P. L. (2012). Twenty-five years of hidden profiles in group decision making: A meta-analysis. Personality and Social Psychology Review, 16, 54–75.

    Article  Google Scholar 

  • Mak, M. H. C., & Twitchell, H. (2020). Evidence for preferential attachment: Words that are more well connected in semantic networks are better at acquiring new links in paired-associate learning. Psychonomic Bulletin & Review, 27, 1059–1069. https://doi.org/10.3758/s13423-020-01773-0

    Article  Google Scholar 

  • McCauley, S. M., & Christiansen, M. H. (2017). Computational investigations of multiword chunks in language learning. Topics in Cognitive Science, 9, 637–652. https://doi.org/10.1111/tops.12258

    Article  Google Scholar 

  • McComb, S. A. (2007). Mental model convergence: The shift from being an individual to being a team member. In F. Dansereau & F. J. Yammarino (Eds.) Multi-level issues in organizations and time (Research in multi-level issues) (Vol. 6, pp. 95–147). Emerald Group Publishing Limited. https://doi.org/10.1016/S1475-9144(07)06005-5

  • McNamara, D. S., & Magliano, J. (2009). Toward a comprehensive model of comprehension. In B. H. Ross (Ed.), Psychology of learning and motivation (Vol. 51, pp. 297–384). Academic Press.

    Chapter  Google Scholar 

  • Mun, Y. (2015). The effect of sorting and writing tasks on knowledge structure measure in bilinguals’ reading comprehension (Master’s thesis). https://scholarsphere.psu.edu/concern/generic_works/x059c7329.

  • NCES. (2019). Indicator 8: English language learners in public schools. Institute of Education Science: National Center for Education Statistics. Retrieved April, 2022, from https://nces.ed.gov/programs/raceindicators/indicator_rbc.asp

  • Ntshalintshali, G. N., & Clariana, R. B. (2020). Paraphrasing refutation text improved higher knowledge forms and hindered lower knowledge forms: Examples from repairing relational database design misconceptions. Educational Technology Research and Development, 68, 2165–2183. https://doi.org/10.1007/s11423-020-09758-5

    Article  Google Scholar 

  • Oden, D. L., Thompson, R. K. R., & Premack, D. (1990). Infant chimpanzees spontaneously perceive both concrete and abstract same/different relations. Child Development, 61, 621–631.

    Article  Google Scholar 

  • Raudszus, H., Segers, E., & Verhoeven, L. (2017). Quality of situation model building predicts first and second language reading comprehension. Presented at the 27th Annual Meeting of the Society for Text and Discourse, Philadelphia, PA. http://www.societyfortextanddiscourse.org/wp-content/uploads/2017/09/STD.Program.2017.pdf

  • Raudszus, H., Segers, E., & Verhoeven, L. (2019). Situation model building ability uniquely predicts first and second language reading comprehension. Journal of Neurolinguistics, 50, 106–119.

    Article  Google Scholar 

  • Reed, S. K. (2012). Learning by mapping across situations. Journal of the Learning Sciences, 21(3), 353–398. https://doi.org/10.1080/10508406.2011.607007

    Article  Google Scholar 

  • Rosch, E. H. (1973). Natural categories. Cognitive Psychology, 4, 328–350.

    Article  Google Scholar 

  • Roschelle, J. (1992). Learning by collaborating: Convergent conceptual change. Journal of the Learning Sciences, 2(3), 235–276.

    Article  Google Scholar 

  • Schuelke, M. (2012). jRateDrag version 2.0 [Computer software]. https://drive.google.com/drive/folders/0B62ahmj_ECTCUjBDMGswRXg3d0U?resourcekey=0-pzzOXhwN1aKWN7pd1KnR7Q

  • Schvaneveldt, R. W. (2020). JPathfinder software. https://research-collective.com/PFWeb/

  • Schvaneveldt, R. W., Durso, F. T., & Dearholt, D. W. (1989). Network structures in proximity data. In G. Bower (Ed.), The psychology of learning and motivation: Advances in research & theory (pp. 249–284). Academic Press.

    Google Scholar 

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

    Article  Google Scholar 

  • Surowiecki, J. (2004). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations. Doubleday.

    Google Scholar 

  • Tang, H., & Clariana, R. (2017). Leveraging a sorting task as a measure of knowledge structure in bilingual settings. Technology, Knowledge and Learning, 22(1), 23–35.

    Article  Google Scholar 

  • 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 

  • Tenser, A. (2016). Semantic map borrowing—Case representation in northeastern Romani dialects. Journal of Language Contact, 9(2), 211–245. https://doi.org/10.1163/19552629-00902001

    Article  Google Scholar 

  • Teplovs, C., & Scardamalia, M. (2007). Visualizations for knowledge building assessment. [Conference presentation]. AgileViz workshop, CSCL 2007 Convention, New Brunswick, NJ, United States. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.590.1779

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

    Article  Google Scholar 

  • Trumpower, D. L., & Sarwar, G. S. (2010). Effectiveness of structural feedback provided by Pathfinder networks. Journal of Educational Computing Research, 43, 7–24.

    Article  Google Scholar 

  • Tseng, Y.-H., Chang, C.-Y., Rundgren, S.-N.C., & Rundgren, C.-J. (2010). Mining concept maps from news stories for measuring civic scientific literacy in media. Computers and Education, 55, 165–177.

    Article  Google Scholar 

  • Ursachi, G., Horodnic, I. A., & Zait, A. (2015). How reliable are measurement scales? External factors with indirect influence on reliability estimators. Procedia Economics and Finance, 20, 679–686.

    Article  Google Scholar 

  • Vincent-Lamarre, P., Massé, A. B., Lopes, M., Lord, M., Marcotte, O., & Harnada, S. (2016). The latent structure of dictionaries. Topics in Cognitive Science, 8, 625–659.

    Article  Google Scholar 

  • Winkielman, P., Halberstadt, J., Fazendeiro, T., & Catty, S. (2006). Prototypes are attractive because they are easy on the mind. Psychological Science, 17(9), 799–806.

    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(1), 41–67. https://doi.org/10.1177/0267658311423452

    Article  Google Scholar 

  • Zemčík, T. (2020). Failure of chatbot Tay was evil, ugliness and uselessness in its nature or do we judge it through cognitive shortcuts and biases? AI & Society, 36, 361–367. https://doi.org/10.1007/s00146-020-01053-4

    Article  Google Scholar 

  • Zhang, J., Scardamalia, M., Lamon, M., Messina, R., & Reeve, R. (2007). Socio-cognitive dynamics of knowledge building in 9- and 10-year-olds. Educational Technology Research and Development, 55, 117–145.

    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.

    Article  Google Scholar 

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Clariana, R.B., Tang, H. & Chen, X. Corroborating a sorting task measure of individual and of local collective knowledge structure. Education Tech Research Dev 70, 1195–1219 (2022). https://doi.org/10.1007/s11423-022-10123-x

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