A Bayesian Classification Approach for Handling Uncertainty in Adaptive E-Assessment
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New technologies have provided the educational field with innovations that allow significant improvement in the teaching learning process. The development of information technology has increased the popularity of Web based education, ; particularly E-assessment This paper provides an overview of Bayesian Network and its application to handle uncertainty in adaptive E-assessment. An adaptive assessment system was realized in PHP and MYSQL. The empirical data was collected and the relationship between the response time and the assessment grading was established. The classifier accuracy obtained using Bayesian classifier was compared with C4.5 Decision tree algorithm and Naïve Bayes classifier. The superiority of Bayesian algorithm over the well known algorithms in handling uncertainty of adaptive assessment data is established in this paper.
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Catherine C Marinagi & Vassilis G.Kaburlasos, Work in Progress: Practical Computerized Adaptive Assessment based on Bayesian Decision Theory,, 36th ASEE/IEEE Frontiers in Education Conference, pp 23- 24, 2006.
Wen-Chih Chang, & Hsuan-Che Yang , Applying IRT to Estimate Learning Ability and K-means Clustering in Web based Learning , Journal of Software , VOL. 4, No. 2, pp 167- 174, 2009.
Catherine C Marinagi, Vassilis G.Kaburlasos Vassilis & Th. Soukalas, An Architecture for an Adaptive Assessment Tool, 37th ASEE/IEEE Frontiers in Education Conference , pp T3D-11 - T3D-16, 2007.
Mansoor Al-A’ali) , Implementation of an Improved Adaptive Testing Theory, Educational Technology and Society, pp 80-94, 2007.
Mariana Lilley (2007), The Development and Application of Computer Adaptive Testing in a Higher Education Environment, PhD Thesis, University of Hertfordshire, UK . 2007.
Nguyen Thai Nghe, Paul Janecek & Peter Haddawy, A Comparative Analysis of Techniques for Predicting Academic Performance, 37th ASEE/IEEE Frontiers in Education Conference, Oct10 – 13 , T2G7 – T2G12, 2007.
Minaei-Bidgoli, B., Kashy, D. A., Kortemeyer, G., and Punch.W. F, Predicting Student Performance: An Application of Data Mining Methods with an Educational Web-based System, Proceedings of 33rd Annual Conference on Frontiers in Education( FIE 2003), Volume 1, pp 13–18, 2003.
Cristina Conati, Abigail Gertner & Kurt Vanlehn, Using Bayesian Networks to Manage Uncertainty in Student Modeling , User Modeling & User Adaptive Interaction, Vol .12 , Issue 4,pp. 371-417, 2002.
Bekele, R.& Menzel, W., A Bayesian Approach to Predict Performance of a Student (BAPPS): A Case with Ethiopian Students, Proceedings of the International Conference on Artificial Intelligence and Applications (AIA-2005), Austria, 2005.
Adel Alorai,ni, Different Machine Learning Algorithms for Breast Cancer Diagnosis, International Journal of Artificial Intelligence and Applications(IJAIA) , Vol 3,No.6 , pp 21-30, 2012.
Gouda I. Salama1, M.B.Abdelhalim2 & Magdy Abd-elghany Zeid, Breast Cancer Diagnosis on Three Different Datasets using Multi-Classifiers , International Journal of Computer and Information Technology,Vol.1, No.1, pp 36-43, 2012.
Baskar Pant, Kumud Pant & K.R.Pardasani, Naïve bayes Classifier for Classification of Plant and Animal miRNA, International journal of Computer Theory & Engineering , Vol.2.No.3.pp.420-424, 2010.
Khlifia Jayech & Mohamed Ali Mahjoub, Clustering and Bayesian network for Image of Faces Classification, International Journal of Advanced Computer Science & Applications, Special Issue on Image processing and Analysis, 2011
Neri Merhav & Yariv Ephraim, A Bayesian Classification Approach with Application to Speech Recognition, IEEE Transactions on Signal processing, Vol 39.No.10, pp.2157- 212166, 1991
M. Mehdi, S. Zair, A. Anou and M. Bensebti, A Bayesian Networks in Intrusion Detection Systems, Journal of Computer Science Vol.3 No.5 pp. 259-265, 2007
H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques,( Morgan Kaufmann, 2011).
Cohen J, A Coefficient of Agreement for Nominal Scale, Educational and Psychological Measurement, pp 37-46, 1960.
Breast Cancer Diagnosis on Three Different Datasets Using Multi-Classifiers
Cohen J. Weighted Kappa: Nominal Scale Agreement with Provision for Scale and Disagreement or Partial Credit. Psychological Bulletin , Vol. 70, pp 213-220, 1968.
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