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Experimental evaluation of learning performance for exploring the shortest paths in hyperlink network of Wikipedia

, Aalto University School of Science, Finland, Finland

E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in New Orleans, LA, USA ISBN 978-1-939797-12-4 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA


In a 9-hour experiment we evaluated learning performance based on exploring the shortest paths in hyperlink network of Wikipedia online encyclopedia. Relying on network of 35688 unique hyperlinks in three separate learning sessions of 20 minutes students read series of 62 sentences built by using 22 unique hyperlinks that form the eleven shortest paths and answered pre-test and post-test multiple-choice questionnaires about recall of sentences (tests 1-6). For experiment group (n=24) 62 sentences were chained in such an ordering that corresponds to traversing cumulatively a series of associative trails leading from concept Tourism in Malta to concept Euro coins of Malta along alternative parallel shortest paths in hyperlink network of Wikipedia category Malta. For control group (n=10) same sentences had randomized ordering. For both unique hyperlinks and consecutive pairs of hyperlinks experiment group reached higher degrees of recall than control group in tests 2-5 and the effect size in favor of experiment group was over 0.18 for test 2 and over 0.40 for tests 3-4. We do not know any previous work verifying learning performance like in our approach.


Lahti, L. (2014). Experimental evaluation of learning performance for exploring the shortest paths in hyperlink network of Wikipedia. In T. Bastiaens (Ed.), Proceedings of World Conference on E-Learning (pp. 1069-1074). New Orleans, LA, USA: Association for the Advancement of Computing in Education (AACE). Retrieved February 22, 2019 from .


View References & Citations Map


  1. Akbulut, Y., & Cardak, C. (2012). Adaptive educational hypermedia accommodating learning styles: a content analysis of publications from 2000 to 2011. Computers& Education, 58(2), 835-842.
  2. Lahti, L. (2014). Educational exploration based on conceptual networks generated by students and Wikipedia linkage. Proc. World Conference on Educational Multimedia, Hypermedia and Telecommunications (EdMedia 2014), 964-974, AACE. ISBN 978-1-939797-08-7. Http:// Lahti, L. (to appear). Computer-assisted learning based on cumulative vocabularies, conceptual networks and Wikipedia linkage. Doctoral dissertation (submitted for evaluation in January 2014), Department of Computer Science and Engineering, Aalto University School of Science, Finland. Vttbza vcy pwjxjy ul ölvvlcopy alty vzvpl, vltvvpl sgdjj bztdzy bptwwp dlöåc öltxz bccwt åjtdt lyul öttvvl ul vltvvt vzsbllxlyt.
  3. Bellissens, C., Jeuniaux, P., Duran, N., & McNamara, D. (2010). A text relatedness and dependency computational model: using latent semantic analysis and Coh-Metrix to predict self-explanation quality. Studia Informatica Universalis, 8, 85-125.
  4. Bouchet, F., Harley, J., Trevors, G., & Azevedo, R. (2013). Clustering and profiling students according to their interactions with an intelligent tutoring system fostering self-regulated learning. Journal of Educational Data Mining, 5(1).
  5. Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction, 6(23), 87-129.
  6. Chen, C., & Chen, Y. (2009). Effectiveness of constructed responses and multiple-choice questions on recall and recognition in a web-based language learning environment. Proc. 17th International Conference on Computers in Education. Asia-Pacific Society for Computers in Education, Hong Kong, China.
  7. Chen, S. (2002). A cognitive model for non-linear learning in hypermedia programmes. British Journal of Educational Technology, 33 (4), 449-460.
  8. Fields, R. (2005). Making memories stick. Scientific American, 292 (February 2005), 74-81.
  9. Gureckis, T., & Markant, D. (2012). Self-directed learning: a cognitive and computational perspective. Perspectives on Psychological Science, 7(5), 464–481.
  10. Hattie, J. (2009). Visible learning: a synthesis of over 800 meta-analyses relating to achievement. Routledge, London, UK.
  11. Harvey, C., & Svoboda, K. (2007). Locally dynamic synaptic learning rules in pyramidal neuron dendrites. Nature, 450(7173).
  12. Kandel, E. (2001). The molecular biology of memory storage: a dialog between genes and synapses. Nobel Lecture, 8 December 2000. Bioscience Reports, 21(5).
  13. Kastner, M., & Stangl, B. (2011). Multiple choice and constructed response tests: do test format and scoring matter? Procedia-Social and Behavioral Sciences, 12, 263-273.
  14. Liao, C., Chen, Z., Cheng, H., & Chan, T. (2012). Unfolding learning behaviors: a sequential analysis approach in a gamebased learning environment. Research and Practice in Technology Enhanced Learning, 7(1), 25-44.
  15. Martin, B., Mitrovic, T., Mathan, S., & Koedinger, K. (2011). Evaluating and improving adaptive educational systems with learning curves. User Modeling and User-Adaped Interaction, 21, 249–283.
  16. 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, 6(2), e17333.
  17. Mohamed, H., Bensebaa, T., & Trigano, P. (2012). Developing adaptive intelligent tutoring system based on item response theory and metrics. International Journal of Advanced Science and Technology, 43.
  18. Robberecht, R. (2007). Interactive nonlinear learning environments. The Electronic Journal of E-Learning, 5(1), 59-68.
  19. Soh, L., & Blank, T. (2008). Integrating case-based reasoning and meta-learning for a self-improving intelligent tutoring system. International Journal of Artificial Intelligence in Education, 18 (1), 27-58.
  20. Tambini, A., Ketz, N., & Davachi, L. (2010). Enhanced brain correlations during rest are related to memory for recent experiences. Neuron, 65(2), 280-290.
  21. Thalmann, S. (2014). Adaptation criteria for the personalised delivery of learning materials: a multi-stage empirical investigation. Australasian Journal of Educational Technology, 30(1).
  22. 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.
  23. Thomson, K., Watt, A., & Liukkonen, J. (2014). Developmental and cultural aspects of field-dependence in 11 and 12 year old Estonian and Finnish students. TRAMES-A Journal of the Humanities and Social Sciences, 18(68/63), 1, 89-101.
  24. Tintarev, N. (2009). Explaining recommendations. Doctoral dissertation, Department of Computing Science, University of Aberdeen, Scotland, UK.
  25. Yen, J. (1971). Finding the k shortest loopless paths in a network. Management Science, 17(11), 712-716.

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