Investigating Cognitive Network Models of Learners’ Knowledge Representations
Keywords:cognitive network science, knowledge representations, concept networks, learning, network growth, invited paper
This commentary discusses how research approaches from Cognitive Network Science can be of relevance to research in the field of Learning Analytics, with a focus on modelling the knowledge representations of learners and students as a network of interrelated concepts. After providing a brief overview of research in Cognitive Network Science, I suggest that a focus on the cognitive processes that occur in the knowledge network, as well as the mechanisms that give rise to changes in the structure of knowledge networks, can lead to potentially informative insights into how learners navigate their knowledge representations to retrieve information and how the knowledge representations of learners develop and grow over the course of their educational careers. Learning Analytics can leverage these insights to design adaptive learning or online learning platforms that optimize learning, and inform pedagogical practice and assessment design that support the development of effective and robust knowledge structures.
Abbott, J. T., Austerweil, J. L., & Griffiths, T. L. (2015). Random walks on semantic networks can resemble optimal foraging. Neural Information Processing Systems Conference, 122(3), 558. https://doi.org/10.1037/a0038693
Avella, J. T., Kebritchi, M., Nunn, S. G., & Kanai, T. (2016). Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Online Learning, 20(2), 13–29.
Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512. https://doi.org/10.1126/science.286.5439.509
Borodkin, K., Kenett, Y. N., Faust, M., & Mashal, N. (2016). When pumpkin is closer to onion than to squash: The structure of the second language lexicon. Cognition, 156, 60–70. https://doi.org/10.1016/j.cognition.2016.07.014
Buckingham Shum, S. (2012). Learning analytics policy brief. Moscow: UNESCO.
Chan, K. Y., & Vitevitch, M. S. (2009). The influence of the phonological neighborhood clustering coefficient on spoken word recognition. Journal of Experimental Psychology: Human Perception and Performance, 35(6), 1934–1949. https://doi.org/10.1037/a0016902
Cheng, J., Kleinberg, J., Leskovec, J., Liben-Nowell, D., State, B., Subbian, K., & Adamic, L. (2018). Do diffusion protocols govern cascade growth? Proceedings of the International AAAI Conference on Web and Social Media, 12(1), Article 1. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/15023
Christensen, A. P., & Kenett, Y. (2019). Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks. PsyArXiv. https://doi.org/10.31234/osf.io/eht8 7
Christianson, N. H., Sizemore Blevins, A., & Bassett, D. S. (2020). Architecture and evolution of semantic networks in mathematics texts. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 476(2239), 20190741. https://doi.org/10.1098/rspa.2019.0741
diSessa, A. A., & Sherin, B. L. (1998). What changes in conceptual change? International Journal of Science Education, 20(10), 1155–1191. https://doi.org/10.1080/0950069980201002
Dorogovtsev, S. N., & Mendes, J. F. F. (2000). Evolution of networks with aging of sites. Physical Review E, 62(2), 1842–1845. https://doi.org/10.1103/PhysRevE.62.1842
Fourtassi, A., Bian, Y., & Frank, M. C. (2020). The growth of children’s semantic and phonological networks: Insight from 10 languages. Cognitive Science, 44(7), e12847. https://doi.org/10.1111/cogs.12847
Goldstein, R., & Vitevitch, M. S. (2017). The influence of closeness centrality on lexical processing. Frontiers in Psychology, 8, 1683. https://doi.org/10.3389/fpsyg.2017.01683
Hens, C., Harush, U., Haber, S., Cohen, R., & Barzel, B. (2019). Spatiotemporal signal propagation in complex networks. Nature Physics, 15(4), 403–412. https://doi.org/10.1038/s41567-018-0409-0
Hills, T. T., Jones, M. N., & Todd, P. M. (2012). Optimal foraging in semantic memory. Psychological Review, 119(2), 431–440. https://doi.org/10.1037/a0027373
Hills, T. T., Maouene, M., Maouene, J., Sheya, A., & Smith, L. (2009). Longitudinal analysis of early semantic networks. Psychological Science, 20(6), 729–739. https://doi.org/10.1111/j.1467-9280.2009.02365.x
Hills, T. T., & Siew, C. S. Q. (2018). Filling gaps in early word learning. Nature Human Behaviour, 2(9), 622–623.
Humphries, M. D., & Gurney, K. (2008). Network ‘small-world-ness’: A quantitative method for determining canonical network equivalence. PloS One, 3(4). https://doi.org/10.1371/journal.pone.0002051
Kenett, Y. N., Anaki, D., & Faust, M. (2014). Investigating the structure of semantic networks in low and high creative persons. Frontiers in Human Neuroscience, 8, 407. https://doi.org/10.3389/fnhum.2014.00407
Kenett, Y. N., Beaty, R. E., Silvia, P. J., Anaki, D., & Faust, M. (2016). Structure and flexibility: Investigating the relation between the structure of the mental lexicon, fluid intelligence, and creative achievement. Psychology of Aesthetics, Creativity, and the Arts, 10(4), 377–388. https://doi.org/10.1037/aca0000056
Kinchin, I. M., Hay, D. B., & Adams, A. (2000). How a qualitative approach to concept map analysis can be used to aid learning by illustrating patterns of conceptual development. Educational Research, 42(1), 43–57. https://doi.org/10.1080/001318800363908
Kleinberg, J. M. (2000). Navigation in a small world. Nature, 406(6798), 845. https://doi.org/10.1038/35022643
Koponen, I. T., & Nousiainen, M. (2014). Concept networks in learning: Finding key concepts in learners’ representations of the interlinked structure of scientific knowledge. Journal of Complex Networks, 2(2), 187–202. https://doi.org/10.1093/comnet/cnu003
Koponen, I. T., & Nousiainen, M. (2019). Pre-service teachers’ knowledge of relational structure of physics concepts: Finding key concepts of electricity and magnetism. Education Sciences, 9(1), 18. https://doi.org/10.3390/educsci9010018
Linn, M. C. (2006). The knowledge integration perspective on learning and instruction. Cambridge, UK: Cambridge University Press.
Luce, P. A., & Pisoni, D. B. (1998). Recognizing spoken words: The neighborhood activation model. Ear and Hearing, 19(1), 1–36.
Mak, M. H. C., Hsiao, Y., & Nation, K. (2021). Lexical connectivity effects in immediate serial recall of words. Journal of Experimental Psychology: Learning, Memory, and Cognition, 47(12), 1971–1997. https://doi.org/10.1037/xlm0001089
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(5), 1059–1069. https://doi.org/10.3758/s13423-020-01773-0
Mallavarapu, A., Lyons, L., & Uzzo, S. (2022). Exploring the utility of social-network-derived collaborative opportunity temperature readings for informing design and research of large-group immersive learning environments. Journal of Learning Analytics, 9(1), 53–76. https://doi.org/10.18608/jla.2022.7419
Malmberg, J., Saqr, M., Järvenoja, H., & Järvelä, S. (2022). How the monitoring events of individual students are associated with phases of regulation: A network analysis approach. Journal of Learning Analytics, 9(1), 77–92. https://doi.org/10.18608/jla.2022.7429
Mokryn, O., Wagner, A., Blattner, M., Ruppin, E., & Shavitt, Y. (2016). The role of temporal trends in growing networks. PLOS ONE, 11(8), e0156505. https://doi.org/10.1371/journal.pone.0156505
Newman, M. (2008). The physics of networks. Physics Today, 61(11), 33–38. https://doi.org/10.1063/1.3027989
Nkhoma, C., Dang-Pham, D., Hoang, A.-P., Nkhoma, M., Le-Hoai, T., & Thomas, S. (2020). Learning analytics techniques and visualisation with textual data for determining causes of academic failure. Behaviour & Information Technology, 39(7), 808–823. https://doi.org/10.1080/0144929X.2019.1617349
Poquet, O., Saqr, M., & Chen, B. (2021). Recommendations for network research in learning analytics: To open a conversation. In O. Poquet, B. Chen, M. Saqr, & T. Hecking (Eds.), Proceedings of the NetSciLA 2021 Workshop: Using Network Science in Learning Analytics: Building Bridges towards a Common Agenda (NetSciLA 2021), 12 April 2021, Newport Beach, CA, USA (virtual). Retrieved from http://ceur-ws.org/Vol-2868/
Saqr, M., & López-Pernas, S. (2022). The curious case of centrality measures: A large-scale empirical investigation. Journal of Learning Analytics, 9(1), 13–31. https://doi.org/10.1007/s42113-018-0003-7
Siew, C. S. Q. (2018). Using network science to analyze concept maps of psychology undergraduates. Applied Cognitive Psychology, 33(4), 662–668. https://doi.org/10.1002/acp.3484
Siew, C. S. Q. (2019). spreadr: An R package to simulate spreading activation in a network. Behavior Research Methods, 51, 910–929. https://doi.org/10.3758/s13428-018-1186-5
Siew, C. S. Q. (2020). Applications of network science to education research: Quantifying knowledge and the development of expertise through network analysis. Education Sciences, 10(4), 101. https://doi.org/10.3390/educsci10040101
Siew, C. S. Q., & Vitevitch, M. S. (2020a). An investigation of network growth principles in the phonological language network. Journal of Experimental Psychology: General, 149(12), 2376–2394. https://doi.org/10.1037/xge0000876
Siew, C. S. Q., & Vitevitch, M. S. (2020b). Investigating the influence of inverse preferential attachment on network development. Entropy, 22(9), 1029. https://doi.org/10.3390/e22091029
Siew, C. S. Q., Wulff, D. U., Beckage, N. M., & Kenett, Y. N. (2019). Cognitive network science: A review of research on cognition through the lens of network representations, processes, and dynamics. Complexity, 2019, e2108423. https://doi.org/10.1155/2019/2108423
Siew, C. S. Q., & Guru, A. (under review). Investigating the network structure of domain-specific knowledge using the semantic fluency task.
Sizemore, A. E., Karuza, E. A., Giusti, C., & Bassett, D. S. (2018). Knowledge gaps in the early growth of semantic feature networks. Nature Human Behaviour, 2(9), 682–692. https://doi.org/10.1038/s41562-018-0422-4
Stasewitsch, E., Barthauer, L., & Kauffeld, S. (2022). Knowledge transfer in a two-mode network between higher education teachers and their innovative teaching projects. Journal of Learning Analytics, 9(1), 93–110. https://doi.org/10.18608/jla.2022.7427
Stella, M., Beckage, N. M., & Brede, M. (2017). Multiplex lexical networks reveal patterns in early word acquisition in children. Scientific Reports, 7. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5402256/
Steyvers, M., & Tenenbaum, J. B. (2005). The large-scale structure of semantic networks: Statistical analyses and a model of semantic growth. Cognitive Science, 29(1), 41–78. https://doi.org/10.1207/s15516709cog2901_3
Storkel, H. L. (2001). Learning new words: Phonotactic probability in language development. Journal of Speech, Language, and Hearing Research, 44(6), 1321–1337. https://doi.org/10.1044/1092-4388(2001/103)
Storkel, H. L., Armbrüster, J., & Hogan, T. P. (2006). Differentiating phonotactic probability and neighborhood density in adult word learning. Journal of Speech, Language, and Hearing Research, 49(6), 1175–1192. https://doi.org/10.1044/1092-4388(2006/085)
Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268–276. https://doi.org/10.1038/35065725
Troyer, A. K., Moscovitch, M., Winocur, G., Alexander, M. P., & Stuss, D. O. N. (1998). Clustering and switching on verbal fluency: The effects of focal frontal- and temporal-lobe lesions. Neuropsychologia, 36(6), 499–504. https://doi.org/10.1016/s0028-3932(97)00152-8
Vitevitch, M. S. (2008). What can graph theory tell us about word learning and lexical retrieval? Journal of Speech, Language, and Hearing Research, 51(2), 408–422. https://doi.org/10.1044/1092-4388(2008/030)
Vitevitch, M. S., Chan, K. Y., & Roodenrys, S. (2012). Complex network structure influences processing in long-term and short-term memory. Journal of Memory and Language, 67(1), 30–44. https://doi.org/10.1016/j.jml.2012.02.008
Vitevitch, M. S., Goldstein, R., Siew, C. S. Q., & Castro, N. (2014). Using complex networks to understand the mental lexicon. Yearbook of the Poznań Linguistic Meeting, 1(1), Article 1. Retrieved from https://pressto.amu.edu.pl/index.php/yplm/article/view/21611
Vitevitch, M. S., Niehorster-Cook, L., & Niehorster-Cook, S. (2021). Exploring how phonotactic knowledge can be represented in cognitive networks. Big Data and Cognitive Computing, 5(4), 47. https://doi.org/10.3390/bdcc5040047
Watts, D. J. (2004). The “new” science of networks. Annual Review of Sociology, 30(1), 243–270. https://doi.org/10.1146/annurev.soc.30.020404.104342
Wulff, D. U., De Deyne, S., Jones, M. N., Mata, R., & Consortium, A. L. (2019). New perspectives on the aging lexicon. Trends in Cognitive Sciences, 23(8), 686–698. https://doi.org/10.1016/j.tics.2019.05.003
Yon, G. G. V., & Valente, T. W. (2017). netdiffuseR: Analysis of diffusion and contagion processes on networks. Zenodo. https://doi.org/10.5281/zenodo.1039317
Zhang, J., Huang, Y., & Gao, M. (2022). Video features, engagement, and patterns of collective attention allocation: An open flow network perspective. Journal of Learning Analytics, 9(1), 32–52. https://doi.org/10.18608/jla.2022.7421
Zemla, J. C., & Austerweil, J. L. (2018). Estimating semantic networks of groups and individuals from fluency data. Computational Brain & Behavior, 1(1), 36–58. https://doi.org/10.1007/s42113-018-0003-7
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