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Using Cortical Learning Algorithm to Arrange Sporadic Online Conversation Groups According to Personality Traits

  • Roberto Agustín García-Vélez
  • Martín López-NoresEmail author
  • Yolanda Blanco-Fernández
  • José Juan Pazos-Arias
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9753)

Abstract

Online conversation spaces are becoming an increasingly popular tool for language learning. Various portals on the Internet offer technological platform where groups of students can meet native teachers and arrange conversation sessions in convenient dates and times. The students’ satisfaction is very important in this context because potential new users pay much attention to the comments and ratings provided by others in the past. Therefore, the portals implement simple mechanisms for the students to rate their experiences, and use the feedback so gathered to promote the teachers who get the best evaluations. Notwithstanding, the current online conversation portals do not implement any means to proactively supervise the formation of the sporadic groups of students, aiming to ensure that the conversations will be balanced, engaging and pleasant to everyone. In this paper we look at the question of whether social data mining and machine learning technologies can be used to maximise the chances that the people put into the same group will get on well together. Specifically, we present one approach based on mining personality traits and using Cortical Learning Algorithm to aid in the planning of the sessions.

Keywords

Online language learning Sporadic social networks Personality traits Cortical Learning Algorithm 

Notes

Acknowledgment

This work is being supported by the European Regional Development Fund (ERDF) and the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC), and by the Ministerio de Educación y Ciencia (Gobierno de España) research project TIN2013-42774-R (partly financed with FEDER funds).

References

  1. 1.
    Ben Nejma, G., Roose, P., Dalmau, M., Gensel, J.: Service discovery for spontaneous communities in pervasive environments. In: Wang, J., et al. (eds.) WISE 2015. LNCS, vol. 9419, pp. 337–347. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-26187-4_32 CrossRefGoogle Scholar
  2. 2.
    Boutet, A., Frenot, S., Laforest, F., Launay, P., Le Sommer, N., Maheo, Y., Reimert, D.: C3PO: a network and application framework for spontaneous and ephemeral social networks. In: Wang, J., et al. (eds.) WISE 2015. LNCS, vol. 9419, pp. 348–358. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-26187-4_33 CrossRefGoogle Scholar
  3. 3.
    Bravo-Torres, J.F., López-Nores, M., Blanco-Fernández, Y., Pazos-Arias, J.J., Ordióñez-Morales, E.F.: Leveraging ad-hoc networking and mobile cloud computing to exploit short-lived relationships among users on the move. In: al Saidi, A., Fleischer, R., Maamar, Z., Rana, O.F. (eds.) ICC 2014. LNCS, vol. 8993, pp. 84–102. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  4. 4.
    Dascalu, M., Bodea, C.N., Lytras, M., Ordez de Pablos, P., Burlacu, A.: Improving e-learning communities through optimal composition of multidisciplinary learning groups. Comput. Hum. Behav. 30, 362–371 (2014)CrossRefGoogle Scholar
  5. 5.
    Gamst, G., Meyers, L., Guarino, A.: Analysis of Variance Designs: A Conceptual and Computational Approach with SPSS and SAS. Cambridge University Press, New York (2008)CrossRefzbMATHGoogle Scholar
  6. 6.
    McCrae, R., Costa, P.: The five-factor theory of personality. In: John, O.P., Robins, R.W., Pervin, L.A. (eds.) Handbook of Personality: Theory and Research, 3rd edn, pp. 159–181. Guilford Press, New York (2008)Google Scholar
  7. 7.
    Mudaliar, D., Modi, N.: Contemplating crossover operators of genetic algorithm for student group formation problem. Int. J. Emerg. Technol. Adv. Eng. 2, 192–197 (2012)Google Scholar
  8. 8.
    Neubaum, G., Wichmann, A., Eimler, S., Kramer, N.: Investigating incentives for students to provide peer feedback in a semi-open online course: an experimental study. In: Proceedings of 10th International Symposium on Open Collaboration (OpenSym), Berlin, Germany, August 2014Google Scholar
  9. 9.
    Numenta, Inc.: HTM Cortical Learning Algorithms. White paper (2011). http://numenta.org/resources/HTM_CorticalLearningAlgorithms.pdf
  10. 10.
    Sinha, T.: Together we stand, together we fall, together we win: dynamic team formation in massive open online courses. In: Proceedings of 5th International Conference on Applications of Digital Information and Web Technologies (ICADIWT), Bangalore, India, February 2014Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Roberto Agustín García-Vélez
    • 1
  • Martín López-Nores
    • 2
    Email author
  • Yolanda Blanco-Fernández
    • 2
  • José Juan Pazos-Arias
    • 2
  1. 1.Área de Ciencias ExactasUniversidad Politécnica SalesianaCuencaEcuador
  2. 2.Departamento de Ingeniería TelemáticaAtlantTIC Research Center for Information and Communication Technologies, EE Telecomunicación, Universidade de VigoVigoSpain

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