You are here:

Generation of learning paths in educational texts based on vocabulary co-occurrence networks in Wikipedia and randomness PROCEEDINGS

, Aalto University School of Science, Finland, Finland

Global Learn, in Berlin, Germany Publisher: Association for the Advancement of Computing in Education (AACE)

Abstract

We propose a new computational method for generating learning paths in educational texts. The method relies on forming vocabulary co-occurrence networks among articles of Wikipedia online encyclopedia and exploiting random explorations to generate route weighting parameters to form pedagogic co-occurrence networks. Motivated by previous research about scale-free small-world networks we suggest our networks to offer efficient and intuitive properties for knowledge representation and learning. We provide experimental results about the properties of the linkage emerging in the vocabulary co-occurrence networks in a set of 175 Wikipedia articles and contrast it with the linkage emerging in the corresponding hyperlink network in Wikipedia. Furthermore we describe formation of the pedagogic co-occurrence network that can be exploited to recommend learning paths for the student.

Citation

Lahti, L. (2015). Generation of learning paths in educational texts based on vocabulary co-occurrence networks in Wikipedia and randomness. In Proceedings of Global Learn Berlin 2015: Global Conference on Learning and Technology (pp. 664-670). Berlin, Germany: Association for the Advancement of Computing in Education (AACE). Retrieved October 22, 2018 from .

View References & Citations Map

References

  1. Balota, D., Duchek, J., & Logan, J. (2007). Is expanded retrieval practice a superior form of spaced retrieval? A critical review of the extant literature. Psychology Press, 83-107.
  2. Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186-198.
  3. Capocci, A., Rao, F., & Caldarelli, G. (2008). Taxonomy and clustering in collaborative systems: the case of the on-line encyclopedia Wikipedia. Europhysics Letters, 81(2).
  4. Duyck, W., Vanderelst, D., Desmet, T., & Hartsuiker, R. (2008). The frequency effect in second-language visual word recognition. Psychonomic Bulletin& Review, 15(4), 850-855.
  5. Ellis, A., & Lambon R. (2000). Age of acquisition effects in adult lexical processing reflect loss of plasticity in maturing systems: insights from connectionist networks. Journal of Experimental Psychology: Learning, Memory and Cognition, 26, 1103-1123.
  6. Fields, R. (2005). Making memories stick. Scientific American, 292 (February 2005), 74-81.
  7. Gentner, D., & Boroditsky, L. (2009). Early acquisition of nouns and verbs: evidence from Navajo. In Gathercole, V. (ed.), Routes to Language: Studies in honor of Melissa Bowerman, 5-36. Taylor& Francis, New York, NY, USA. Http://wwwpsych.stanford.edu/~lera/papers/navajo.pdf
  8. Izura, C., & Ellis, A. (2002). Age of acquisition effects in word recognition and production in first and second languages. Psicológica, 23, 245-281. Http://www.uv.es/revispsi/articulos2.02/4.IZURA%26ELLIS.pdf Kandel, E. (2001). The molecular biology of memory storage: a dialog between genes and synapses. Nobel Lecture, 8 December 2000. Bioscience Reports, 21(5).
  9. Kinouchi, O., Martinez, A., Lima, G., Lourenco, G., Risau-Gusman, S. (2002). Deterministic walks in random networks: an application to thesaurus graphs. Physica A: Statistical Mechanics and its Applications, 315(3-4), 665-676.
  10. Lahti, L. (2014). Computational method for supporting learning with cumulative vocabularies, conceptual networks and Wikipedia linkage. International Journal for Cross-Disciplinary Subjects in Education (IJCDSE), 5(2), June 2014, 1632– 1644. Infonomics Society, UK. ISSN 2042-6364. Http://www.infonomics-society.org/IJCDSE/Computational%20 Method
  11. Laufer, B. (1989). What percentage of text-lexis is essential for comprehension? In C. Lauren and M. Nordman (eds.), Special Language: From Humans Thinking to Thinking Machines. Multilingual Matters, Clevedon, UK.
  12. Masucci, A., Kalampokis, A., Eguíluz, V., & Hernández-García, E. (2011). Wikipedia information flow analysis reveals the scale-free architecture of the semantic space. Public Library of Science ONE (PLoS ONE), 6(2), e17333.
  13. Morais, A., Olsson, H., & Schooler, L. (2013). Mapping the structure of semantic memory. Cognitive Science, 37, 125-145.
  14. Nation, P., & Waring, R. (1997). Vocabulary size, text coverage, and word lists. In Schmitt, N., & McCarthy, M. (eds.), Vocabulary: Description, Acquisition, Pedagogy. Cambridge University Press, New York, USA, 6-19.
  15. Thalheimer, W. (2006). Spacing learning over time: what the research says. Will Thalheimer, Work-Learning Research Inc., Somerville, Massachusetts, USA (published March 2006, reformatted 2010). Online available at http://willthalheimer.typepad.com/files/spacing_learning_over_time_2006.pdf Thompson, G., Kello, C., & Montez, P. (2013). Searching semantic memory as a scale-free network: evidence from category recall and a Wikipedia model of semantics. Proc. 35th Annual Meeting of the Cognitive Science Society. Cognitive Science Society, Austin, TX, USA, 3533-3538. ISBN 978-0-9768318-9-1.
  16. Voß, J. (2005). Measuring Wikipedia. Proc. 10th International Conference of the International Society for Scientometrics and Informetrics (ISSI 2005). Http://eprints.rclis.org/bitstream/10760/6207/1/MeasuringWikipedia2005.pdf
  17. Wang, J., Zuo, X., & He, Y. (2010). Graph-based network analysis of resting-state functional MRI. Frontiers in Systems Neuroscience, 4:16.

These references have been extracted automatically and may have some errors. If you see a mistake in the references above, please contact info@learntechlib.org.