SECURITY CHALLENGES IN MOBILE ASSISTED LANGUAGE LEARNING IN THE MILLENNIUM FOR EDUCATION

Authors

  • Hassan Soleimani Department of Applied Linguistics, Payame Noor University, Tehran, Iran, Iran, Islamic Republic of
  • Ali Asghar Rostami Abu Saeedi full professor in English Literature, Department of Applied Linguistics, Payame Noor University, Iran, Iran, Islamic Republic of
  • Mahboubeh Rahmanian Ph.D candidate, Payame Noor University, Tehran, Iran, Iran, Islamic Republic of

DOI:

https://doi.org/10.20535/2410-8286.72703

Keywords:

security, technology, mobile assisted language learning, testing.

Abstract

Distance learning technologies enrich learning opportunities due to many advantages like ubiquity and flexibility. Although the usefulness of such technologies in teaching and learning is clear, their testing part is remained to be discussed due to the security issue. Administrators and teachers need to use more authentic and secure distant testing software in which the scores are guaranteed and the testees keep away from cheating. Static and online authentication systems like “username” and “password” and face detection have empowered educational parties to have more reliable testing outcomes. Mobile devices as the necessity of the new millennium need to use authentication software in their testing. Mobile devices with their multimedia course materials provide learners with many optimistic learning opportunities through collaboration, cooperation, interaction and testing. The unique chances of ubiquity, individualization, informality, and spontaneity make the mobile learning of particular importance not only for digital natives but also for teachers, administrators, developers, instructors, and policy makers. Yielding an economical learning opportunity along with providing authentic contexts for collaborative learning is beneficial for the economy of the country in general and for the meaningful and deep learning of the learners. This paper will discuss how authentication techniques have applied to electronic devices like mobile phones.

Downloads

Download data is not yet available.

References

  1. Coates, H., James, R., & Baldwin, G. (2005). A critical examination of the effects of learning management systems on university teaching and learning. Tertiary education and management, 11, 19-36. https://doi.org/10.1007/s11233-004-3567-9
    |
  2. Daugman, J.G. (2004). How iris recognition works, IEEE Trans. Circuits Syst: Video Technol, 14 (1), 21-30. https://doi.org/10.1109/tcsvt.2003.818350
    |
  3. De Marsico, M., Nappi, M., Riccio, D., & Wechsler, H. (2015). Mobile Iris Challenge Evaluation (MICHE)-I, biometric iris dataset and protocols. Pattern Recognition Letters, 57, 17-23. https://doi.org/10.1016/j.patrec.2015.02.009
  4. Demouy, V., Jones, A., Kan, Q., Kukulska-Hulme, A., & Eardley, A. (2016). Why and how do distance learners use mobile devices for language learning? The EuroCALL Review, 24(1), 10-24. https://doi.org/10.4995/eurocall.2016.5663
  5. Dias, J. (2002). Cell phones in the classroom: Boon or bane? Calling Japan, 10 (2), 16-21. Retrieved from http://jaltcall.org/cjo/10_2.pdf/
  6. Farshchi, S. M. R., & Toosizadeh, S. (2014). A safe authentication system for distance education. Computer Applications in Engineering Education, 22(4), 593-603. https://doi.org/10.1002/cae.20583
  7. Gagne, R. M. (2005). Principles of instructional design (5th ed.). Belmont, CA: Thomson/Wadsworth.
  8. Garcıa-Hernández, J., & Paredes, R. (2005). Biometric identification using palmprint local features. Biometrics on the Internet, 11.
  9. Guillén-Gámez, F. D., García-Magariño, I., & Romero, S. J. (2015). Analysis of the Perception of Students about Biometric Identification. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 10(3), 1-18. https://doi.org/10.4018/ijwltt.2015070101
    |
  10. Hannay, M., & Newvine, T. (2006). Perceptions of distance learning: A comparison of online and traditional learning. Journal of Online Learning and Teaching, 2(1), 1-11. Retrieved from: http://jolt.merlot.org/05011.htm
  11. Jain, A., Flynn, P., & Ross, A. A. (Eds.). (2008). Handbook of biometrics. Springer. https://doi.org/10.1007/978-0-387-71041-9
  12. Johnson, S. D., Aragon, S. R., Shaik, N., & Palma-Rivas, N. (2000). Comparative analysis of learner satisfaction and learning outcomes in online and face-to-face learning environments. Journal of interactive learning research, 11(1), 29.
  13. Kukulska-Hulme, A., & Shield, L. (2008). An overview of mobile assisted language learning: From content delivery to supported collaboration and interaction. ReCALL, 20(03), 271-289. https://doi.org/10.1017/s0958344008000335
    |
  14. Leasure, A. R., Davis, L., & Thievon, S. L. (2000). Comparison of student outcomes and preferences in a traditional vs. world wide web-based baccalaureate nursing research course. Journal of Nursing Education, 39(4), 149-154.
    | |
  15. Levy, M., & Kennedy, C. (2005). Learning Italian via mobile SMS. Mobile learning: A handbook for educators and trainers, 76-83.
  16. Liu, P. L., & Chen, C. J. (2015). Learning English through actions: a study of mobile-assisted language learning. Interactive Learning Environments, 23(2), 158-171. https://doi.org/10.1080/10494820.2014.959976
  17. Lorenzetti, J. P. (2006). Proctoring Assessments: Benefits & Challenges. Distance Education Report, 10(8), 5-6.
  18. Martin, F., & Ertzberger, J. (2013). Here and now mobile learning: An experimental study on the use of mobile technology. Computers & Education, 68, 76-85. https://doi.org/10.1016/j.compedu.2013.04.021
  19. Pal, M., & Saha, G. (2015). On robustness of speech-based biometric systems against voice conversion attack. Applied Soft Computing, 30, 214-228. https://doi.org/10.1016/j.asoc.2015.01.036
  20. Petersen, S., & Divitini, M. (2005). Language Learning: From Individual Learners to Communities. In: Proceedings of the IEEE International Workshop on Wireless and Mobile Technologies in Education (WMTE), Washington, DC, USA: IEEE Computer Society, 169-173. https://doi.org/10.1109/wmte.2005.41
    |
  21. Phillips, P. J., Martin, A., Wilson, C. L., & Przybocki, M. (2000). An introduction evaluating biometric systems. Computer, 33(2), 56-63. https://doi.org/10.1109/2.820040
    |
  22. Ribaric, S., Fratric, I., & Kis, K. (2005, September). A biometric verification system based on the fusion of palmprint and face features. In ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005 (pp. 12-17). https://doi.org/10.1109/ispa.2005.195376
    |
  23. Robles, M., & Braathen, S. (2002). Online assessment techniques. Delta Pi Epsilon Journal, 44(1), 5-15.
  24. Rodríguez, C. D., & Cumming, T. M. (2016). Employing Mobile Technology to Improve Language Skills of Young Students with Language-Based Disabilities. Assistive Technology, (just-accepted). https://doi.org/10.1080/10400435.2016.1171810
    |
  25. Rose, R. R., Suruliandi, A., & Meena, K. (2015). Local texture description framework-based modified local directional number pattern: a new descriptor for face recognition. International Journal of Biometrics, 7(2), 147-169. https://doi.org/10.1504/ijbm.2015.070928
  26. Samuels, J. (2003). Wireless and handheld devices for language learning. Proceedings of the 19th Annual Conference on Distance Teaching and Learning, Madison, WI. Retrieved from http://www.uwex.edu/disted/conference/Resource_library/proceedings/03_50.pdf
  27. Shan, C., Gong, S., & McOwan, P.W. (2009). Facial expression recognition based on local binary patterns: A comprehensive study. Image and Vision Computing, 27 (6), 803–816. https://doi.org/10.1016/j.imavis.2008.08.005
  28. Shen, R., Wang, M., Gao, W., Novak, D., & Tang, L. (2009). Mobile learning in a large blended computer science classroom: System function, pedagogies, and their impact on learning. IEEE Transactions on Education, 52(4), 538-546. https://doi.org/10.1109/te.2008.930794
    | |
  29. Sjoer, E., & Dopper, S. M. (2003, May). Are the promises of online assessment being proved in practice. In A case study into what conditions should be met in order to use online assessment successfully, Sefi Proceedings.
  30. Sung, H. Y., Hwang, G. J., & Chang, Y. C. (2013). Development of a mobile learning system based on a collaborative problem-posing strategy. Interactive Learning Environments, 24(3), 1-16. https://doi.org/10.1080/10494820.2013.867889
    |
  31. Tian, Y.L. (2004). Evaluation of face resolution for expression analysis. In Computer Vision and Pattern Recognition Workshop: CVPRW, 4, 82. https://doi.org/10.1109/cvpr.2004.334
    |
  32. Wang, Y. H. (2016). Could a mobile‐assisted learning system support flipped classrooms for classical Chinese learning? Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.12141
    |
  33. Wang, Y. H., & Shih, S. K. H. (2015). Mobile-assisted language learning: Effects on EFL vocabulary learning. International Journal of Mobile Communications, 13(4), 358-375. https://doi.org/10.1504/ijmc.2015.070060
  34. Yang, T-Y., & Chen, H-J. (2012). Investigating the effects of a mobile game on EFL learners’ vocabulary learning. In J. Colpaert, A. Aerts, W-C. V. Wu, & Y-C. J. Chao (Eds.), The medium matters: Proceedings 15th International CALL Conference (pp. 697–700).
  35. Zhao, W., Chellappa, R., Phillips, P.J., & Rosenfeld, A. (2003). Face recognition: A literature survey. ACM Computing Surveys, 35 (4), 399–458. https://doi.org/10.1145/954339.954342

Downloads

Published

2016-12-28

How to Cite

Soleimani, H., Rostami Abu Saeedi, A. A., & Rahmanian, M. (2016). SECURITY CHALLENGES IN MOBILE ASSISTED LANGUAGE LEARNING IN THE MILLENNIUM FOR EDUCATION. Advanced Education, (6), 4–10. https://doi.org/10.20535/2410-8286.72703

Issue

Section

Education