Advertisement

Language Proficiency Classification During Computer-Based Test with EEG Pattern Recognition Methods

  • Federico Cirett-GalánEmail author
  • Raquel Torres-Peralta
  • Carole R. Beal
Conference paper
  • 889 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10267)

Abstract

The answering of any test represents a challenge for students; however, foreign students whose first language is not English have to deal with the difficulty of the understanding of a series of questions written on a different language in addition of the effort required to solve the problem. In this study, we recorded the behavior of the brain signals of 16 students, 10 whom first language was English and 6 who were English learners, and used two supervised classification algorithms in order to identify the students’ language proficiency. The results shown that in both approaches, harder problems which required longer time to be responded had a higher accuracy rate; however, more tests are needed in order to understand the physical processing of written math text problem and the difference among both groups.

Keywords

Electroencephalography Machine learning Data mining Pattern recognition Intelligent Tutoring Systems Physiology Behavior 

Notes

Acknowledgments

The research was supported by National Science Foundation HRD 0903441. We would like to thank the staff at Advanced Brain Monitoring for their support, as well as the students who participated in the research.

References

  1. 1.
    Arroyo, I., Cooper, D.G., Burleson, W., Woolf, B.P., Muldner, K., Christopherson, R.: Emotion sensors go to school. In: Proceeding of the 2009 Conference on Artificial Intelligence in Education, pp. 17–24. IOS Press (2009)Google Scholar
  2. 2.
    Arroyo, I., Mehranian, H., Woolf, B.P.: Effort-based tutoring: an empirical approach to intelligent tutoring. In: Baker, R.S.J.d., Merceron, A., Pavlik, Jr., P.I. (eds.) Proceedings of the 3rd International Conference on Educational Data Mining (2010)Google Scholar
  3. 3.
    Baker, R.S.J.d., Walonoski, J., Heffernan, N., Roll, I., Corbett, A., Koedinger, K.: Why students engage in “gaming the system” behavior in interactive learning environments. J. Interact. Learn. Res. 19, 185–224 (2008)Google Scholar
  4. 4.
    Beal, C., Mitra, S., Cohen, P.R.: Modeling learning patterns of students with a tutoring system using hidden markov models. In: Proceeding of the 2007 Conference on Artificial Intelligence in Education, pp. 238–245. IOS Press (2007)Google Scholar
  5. 5.
    Berka, C., Levendowski, D.J., Lumicao, M.N., Yau, A., Davis, G., Zivkovic, V.T., Olmstead, R.E., Tremoulet, P.D., Craven, P.L.: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Space Environ. Med. 78(5 Suppl), B231–B244 (2007)Google Scholar
  6. 6.
    Berka, C., Levendowski, D.J., Ramsey, C.K., Davis, G., Lumicao, M.N., Stanney, K., Reeves, L., Regli, S.H., Tremoulet, P.D., Stibler, K.: Evaluation of an EEG workload model in an aegis simulation environment. In: Caldwell, J.A., Wesensten, N.J. (eds.) Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 5797, pp. 90–99 (2005)Google Scholar
  7. 7.
    Cirett, F., Beal, C.R.: Problem solving by English learners and English primary students in an algebra readiness ITS. In: Guesgen, H., Murray, R.C. (eds.) Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference, pp. 492–497. Flairs (Florida Artificial Intelligence Research Society), AIII Publications (2010). http://www.aaai.org/ocs/index.php/FLAIRS/2010/paper/view/1250
  8. 8.
    Cirett Galán, F., Beal, Carole R.: EEG estimates of engagement and cognitive workload predict math problem solving outcomes. In: Masthoff, J., Mobasher, B., Desmarais, Michel C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 51–62. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-31454-4_5 CrossRefGoogle Scholar
  9. 9.
    Collins-Thompson, K., Callan, J.: Predicting reading difficulty with statistical language models. J. Am. Soc. Inf. Sci. Technol. 56(13), 1448–1462 (2005). doi: 10.1002/asi.20243, ISSN 1532-2882
  10. 10.
    Gándara, P., Maxwell-Jolly, J., Driscoll, A.: Listening to teachers of English language learners: a survey of california teachers’ challenges, experiences, and professional development needs. Policy Analysis for California Education, PACE (NJ1) (2005)Google Scholar
  11. 11.
    Gandara, P., Rumberger, R., Maxwell-Jolly, J., Callahan, R.: English learners in California Schools: unequal resources, unequal outcomes. Educ. Policy Anal. Arch. 11(36), 1–54 (2003)Google Scholar
  12. 12.
    Johns, J., Woolf, B.P.: A dynamic mixture model to predict student motivation and proficiency. In: Proceedings of the AAAI. IOS Press, Boston (2006)Google Scholar
  13. 13.
    Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowl. Inf. Syst. 3(3), 263–286 (2001)CrossRefzbMATHGoogle Scholar
  14. 14.
    Niedermeyer, E.F.H., et al.: Electroencephalography Basic Principles, Clinical Applications, and Related Fields, 5th edn. Lippincott Williams and Wilkins, Philadelphia (2005)Google Scholar
  15. 15.
    Peregoy, S.F., Boyle, O.F.: English learners reading English: what we know, what we need to know. Theory Pract. 39(4), 237–247 (2000)CrossRefGoogle Scholar
  16. 16.
    Poythress, M., et al.: Correlation between expected workload and EEG indices of cognitive workload and task engagement. In: Augmented Cognition: Past, Present and Future, pp. 32–44 (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Universidad de SonoraHermosilloMexico
  2. 2.University of FloridaGainesvilleUSA

Personalised recommendations