BUKU ABSTRAK - Universiti Putra Malaysia

The Generalised Localisation Principle on the Critical Line
Dr. Anvarjon Ahmedov
Ravshan Ashurov
Faculty of Engineering, University Putra Malaysia,
43400 UPM Serdang, Selangor, Malaysia.
+603-8946 6350; anvar@eng.upm.edu.my
Ranking of Influencing Factors in Predicting Students’ Academic Performance
Dr. Lilly Suriani Affendey
Ikmal Hisyam Mohd. Paris, Norwati Mustapha, Md. Nasir Sulaiman and Zaiton Muda
Faculty of Computer Science and Information Technology, University Putra Malaysia,
43400 UPM Serdang, Selangor, Malaysia.
+603-8946 6549; suriani@fsktm.upm.edu.my
Keywords: Predicting academic performance, data mining, attributes ranking
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Science, Technology & Engineering
In this paper, we study the general localisation principle for Fourier-Laplace series on unit sphere. Weak
type (1, 1) property of maximal functions is used to establish the estimates of the maximal operators of Riesz
means at critical index (N?1)/2. The properties Jacobi polynomials are used in estimating the maximal operators
of spectral expansions in Hilbert spaces. For extending positive results on critical line ? =(N-1)(1/p-1/2), we apply
interpolation theorem for the family of the linear operators of weak types. The generalised localisation principle is
established by the analysis of spectral expansions in L2. We have proved the sufficient conditions for the almost
everywhere convergence of Fourier-Laplace series by Riesz means on the critical line.
Keywords: Fourier-laplace series, riesz means, spectral function, eigenfunction of the laplace-beltrami operator
Accurately predicting students’ performance is useful in identifying weak students who are likely to perform
poorly in their studies. Most higher learning institutions have systems to store students’ information and these
databases contain useful knowledge that can be extracted. This motivates us to mine patterns from archives of
the Students Information System, a real-world data set, which does not store sufficient background information
of the students’ previous academic achievements. Our objective is to rank influencing factors that contribute
to the prediction of students’ academic performance. In this study we used WEKA open source data mining
tool to analyse attributes for predicting a higher learning institution’s Bachelor of Computer Science students’
academic performance. The data set comprised of 2427 number of student records and 396 attributes of students
registered between year 2000 and 2006. Preprocessing includes attribute importance analysis. We applied the
data set to different classifiers (Bayes, trees or function) and obtained the accuracy of predicting the students’
performance into either first-second-upper class or second-lower-third class. A cross-validation with 10 folds was
used to evaluate the prediction accuracy. Our results showed the ranking of courses that has significant impact on
predicting the students’ overall academic results. In addition, we perform experiments comparing the performance
of different classifiers and the result showed that Naïve Bayes, AODE and RBFNetwork classifiers scored the
highest percentage of prediction accuracy of 95.29%.