Browsing by Author "Hambali, M.A."

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    Comparative Analysis of Decision Tree Algorithms for Predicting Undergraduate Students’ Performance in Computer Programming
    (Faculty of Science, Adeleke University, Ede, Nigeria - Journal of Advances in Scientific Research & Applications, A Multidisciplinary Journal Publication of the Faculty of Science, Adeleke University, Ede, Nigeria, 2015) Abdulsalam, S.O.; Babatunde, A.N.; Hambali, M.A.; Babatunde, R.S.
    Educational data are increasing tremendously with little exploration by the education managers. Hidden knowledge can be discovered from the huge data sets available in educational databases through the use of data mining techniques. Educational data mining (EDM) is an emerging discipline, concerned with developing methods for exploring the unique types of data that reside in educational databases. One of the significant areas of the application of EDM is the development of student models for predicting students’ performances in their educational institutions. The focus of this work is to identify the optimal decision tree algorithms for predicting students’ performance in a computer programming course taken in 200 level based on their ordinary level results in Mathematics and Physics and their 100 level results in Mathematics and Physics courses. One hundred and thirty one (131) students’ records from computer science programme at Kwara State University (KWASU) between 2009 and 2013 were used. The attributes used are students’ ordinary level scores in Mathematics and Physics, 100 level results in Mathematics and Physics courses and the score in a 200 level computer programming course (CSC 203). C4.5 (known as J48 in WEKA) Classification and Regression Tree (CART), and Best-First Tree (BF Tree) decision tree algorithms were used in Waikato Environment for Knowledge Analysis data mining software to generate three classification models employed in predicting students’ performance in CSC 203. The results of these algorithms were compared using 10-fold cross validation method in terms of prediction accuracy and computational time. Our results showed that J48 tree has the highest prediction accuracy of 70.37% and least execution time of 0.02 seconds while CART and BFTree has prediction accuracy of 60.44% and 60.30% respectively and both having execution time of 0.22 seconds based on the data set used in this study. This study also revealed that previous knowledge of Mathematics and Physics both at Ordinary level and 100 level are essential determinants of students’ performance in a computer programming course. Keywords: Data Mining, Educational Data Mining, Decision Tree algorithm, Students’ Performance
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    Knowledge Discovery from Educational Database Using Apriori Algorithm
    (Georgian Technical University an Niko Muskhelishvili Institute of Computational Mathematics, Georgia - Georgian Electronic Scientific Journals (GESJ): Computer Science and Telecommunications, 1(51): 41 – 51, 2017) Abdulsalam, S.O.; Hambali, M.A.; Salau-Ibrahim, T.T.; Saheed, Y.K.; Babatunde, A.N.
    Ability to predict student’s performance has become very crucial in educational environments and plays important role in producing the best quality graduates. There are several statistical tools for analyzing students' performance for knowledge discovery from available data. This study presents data mining in educational sector that identifies students’ failure pattern using Apriori algorithm. The results of 20 students in 25 courses taken in their 100 and 200 level of an educational institute in North Central Nigeria were considered as a case study. The patterns discovered were used to provide recommendations to academic planners so as to improve their level of decision making, restructuring of curriculum, and modifying the prerequisites of various courses. This study revealed some interesting patterns in failed courses as some failed courses have a relationship with other failed courses. A data mining software for mining student failed courses was developed, used to mine students result, and the analysis were described. Keywords: Association Rule Mining, Apriori Algorithm, Academic performance, Educational data mining, Curriculum, Educational database, Students' result repository
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    Student’s Performance Analysis Using Decision Tree Algorithms
    (Faculty of Computers and Applied Computer Science, Tibiscus University of Trinisoara, Romania - Annals Computer Science Series Journal, Faculty of Computers and Applied Computer Science, Tibiscus University of Trinisoara, Romania, 15, 55– 62, 2017) Abdulsalam, S.O.; Saheed, Y.K.; Hambali, M.A.; Salau-Ibrahim, T.T.; Babatunde, A.N.
    Educational Data Mining (EDM) is concerns with developing and modeling methods that discover knowledge from data originating from educational environments. This paper presents the use of data mining approach to study students’ performance in CSC207 (Internet Technology and Programming I) a 200 level course in the department of Computer, Library and Information Science. Data mining provides many approaches that could be used to study the students’ performance, classification task is used in this work to evaluate the student’s performance and as there are numbers of approaches that can be used for data classification, including decision tree method. In this work, decision trees were used which include BFTree, J48 and CART. Students’ attribute such as Attendance, Class test, Lab work, Assignment, Previous Semester Marks and End Semester Marks were collected from the students’ management system, to predict the performance at the end of semester examination. This paper also investigates the accuracy of different Decision tree algorithms used. The experimental results show that BFtree is the best algorithm for classification with correctly classified instance of 67.07% and incorrectly classified instance of 32.93%. KEYWORDS: Classification, Decision tree, Students’ Performance, Educational Data Mining