The improvements in educational data mining (EDM) and machine learning motivated the academic staff to implement educational models to predict the performance of students and find the factors that increase their success. EDM faced many approaches for classifying, analyzing and predicting a student’s academic performance. This paper presents a model of prediction based on an artificial neural network (ANN) by implementing feature selection (FS). A questionnaire is built to collect students’ answers using LimeSurvey and google forms. The questionnaire holds a combination of 61 questions that cover many fields such as sports, health, residence, academic activities, social and managerial information. 161 students participated in the survey from two departments (Computer Science Department and Computer Information Systems Department), college of Computer Science and Information Technology, University of Basra. The data set is combined from two sources applications and is pre-processed by removing the uncompleted answers to produce 151 answers used in the model. Apart from the model, the FS approach is implemented to find the top correlated questions that affect the final class (Grade). The aim of FS is to eliminate the unimportant questions and find those which are important, besides improving the accuracy of the model. A combination of Four FS methods (Info Gain, Correlation, SVM and PCA) are tested and the average rank of these algorithms is obtained to find the top 30 questions out of 61 questions of the questionnaire. Artificial Neural Network is implemented to predict the grade (Pass (P) or Failed (F)). The model performance is compared with three previous models to prove its optimality.
Academic institutions always try to use a solid platform for supporting their short-to-long term decisions related to academic performance. These platforms utilize historical data and turn them into strategic decisions. The hidden patterns in the data need tools and approaches to be discovered. This paper aims to present a short roadmap for implementing educational data mart based on a data set from Alexandria Private Elementary School, located in the Basrah province of Iraq in the 2017-2018 academic year. The educational data mart is implemented, then the cube is constructed to perform OLAP operations and present OLAP reports. Next, OLAP mining is performed on the educational cube using nine algorithms, namely: decision tree with score method (entropy) and split method (complete)), decision tree with score method (entropy) and split method (complete)), decision tree with score method (entropy) and split method (both)), Logistic, Naïve Bayes, Neural Network, clustering with expectation maximization, clustering with K-means clustering, and association rules mining. According to a comparison of all algorithms, clustering with expectation-maximization proved the highest accuracy with 96.76% for predicting the students' performance and 96.12% for predicting students' grades amongst all other algorithms.
Prediction in data mining is a sophisticated task that is conducted in various disciplines. Given that the overall success of educational institutions can be measured by their students' success, many studies are dedicated to predicting it. This paper provides a model of student's success prediction based on Bayes algorithms and suggests the best algorithm based on performance details. Two built Bayes Algorithms (naïve Bayes and Bayes network) were used in this model with students' questionnaire answers. The questionnaire consists of 62 questions that cover the fields affecting students' performance the most. The questions refer to health, social activity, relationships and academic performance. The questionnaire is constructed based on a Google form and open-source applications (LimeSurvey); the total number of student answers is 161. To build this model, the tool Weka 3.8 is used. The overall model design process can be divided into two stages. The first stage is finding the most correlated questions to the final class, and the second is applying algorithms and finding the optimal algorithm. A comparison is made between these two Bayes algorithms based on performance details. Finally, the naïve Bayes algorithm is selected as an optimal choice for students' success prediction.
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