Abstract
This paper describes the methodology for creating the intelligent, user adapted testing system that has been developed using LMS Moodle. The integration of the intelligent processes into the existing training systems will prevent the drawbacks of the existing knowledge assessment systems and will make it possible to assess the learners’ ability automatically disable problematics or incorrect questions from database question set.
The methodology to provide fast online fault detection in Moodle question database using scan statistics method is described. Scan statistics have long been used to detect statistically significant bursts of events. This research of student faults in time enables to detect the most problematics topics of educational process, check the efficiency of the decisions taken to select the education strategy.
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References
Abraham, G., Balasubramanian, V., Saravanaguru, RA.K.: Adaptive e-Learning environment using learning style recognition. Int. J. Eval. Res. Educ. 2, 23–31 (2013)
Berman, M., Eagleson, G.K.: A useful upper bound for the tail probabilities of the scan statistics. J. Am. Stat. Assoc. 80(392), 886–889 (1985). doi:10.2307/2288548
Delgado, M., Gibaja, E., Pegalajar, M.C., Pérez, O.: Predicting students’ marks from moodle logs using neural network models. In: Current Developments in Technology-Assisted Education, Badajoz, pp. 586–590 (2006)
El Bachari, E., Abelwahed, E., El Adnani, M.: An adaptive learning model using learner’s preference. In: International Conference on Models of Information and Communication Systems, ENSIAS, Rabat, Morocco (2010)
Glaz, J., Naus, J., Wallenstein, S.: Scan Statistics. Springer Series in Statistics. Springer, New York (2001). pp. 367–360
Niemiec, C., Ryan, R.M.: Autonomy, competence, and relatedness in the classroom. Applying self-determination theory to educational practice. Theory Res. Educ. 7(2), 133–144 (2009)
Surjono, H.D.: The design of adaptive e-Learning system based on student’s learning styles. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 2(5), 2350–2353 (2011)
Ward, M.P., Carpenter, T.E.: Methods for determining temporal clusters in surveillance and survey programs. In: Salman, M.D. (ed.) Animal Disease Surveillance and Survey Systems. Methods and Applications, pp. 87–99. Iowa State Press, Ames Iowa (2003)
Wallenstein, S., Naus, J.: Statistics for temporal surveillance of bioterrorism. In: Syndrome Surveillance: Reports from a National Conference, Morbidity and Mortality Weekly Report 2004, vol. 53, pp. 74–78 (2003)
Гeкк, M.B., Иcтoмин, T.E., Фaйзyлxaкoв, Я.P., Чeчeндaeв, A.B.: Aдaптивный aлгopитм быcтpoй дocтaвки cooбщeний пo выдeлeнным нaпpaвлeниям, Becтник мoлoдыx yчeныx “Лoмoнocoв”. Bыпycк III. C. 55–60 (2006)
Moodle question database structure. https://docs.moodle.org/dev/Question_database_structure. Accessed 6 Mar 2017
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Jurenoks, A., Jurenoka, S., Novickis, L. (2017). Online Fault Detection Methodology of Question Moodle Database Using Scan Statistics Method. In: Damaševičius, R., Mikašytė, V. (eds) Information and Software Technologies. ICIST 2017. Communications in Computer and Information Science, vol 756. Springer, Cham. https://doi.org/10.1007/978-3-319-67642-5_40
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DOI: https://doi.org/10.1007/978-3-319-67642-5_40
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