Abstract:Student performance is a critical factor in determining a university's reputation because it has a negative effect on student retention. Students who do not perform well in a course are more likely to drop out from their programmes before graduating. Many students who enrol in Computing Science programmes struggle to find success because it is considered a difficult discipline. In this study, a sample of 918 observations were selected containing demographic and academic information about students enrolled in a… Show more
“…The indices generated by the random index generator cross-over operation take place in terms of several filters [ 1 , 2 ] and the filter size [ 2 , 3 ]. The new child generation of chromosomes is processed based on indices and cross-over operation from parent 1 and parent 2.…”
Section: Proposed Workmentioning
confidence: 99%
“…Root parents are mutated to produce the new child chromosome using the random index generator. The example considers the number of filters as [ 1 , 4 ] and the size of filters as [ 2 , 3 ]. FIGURE 3 Mutation Operation …”
Section: Proposed Workmentioning
confidence: 99%
“…That is, COVID-19 infects most people without any indications of symptoms. These, in turn, lead to the drastic spread of the disease to other people, Sam et al [1] Data analysis and classification to predict the student's performance in the undergraduate computing branch and suggest an approach for reducing student dropouts. In Italy, it is observed that Vo Euganeo, which is 50km west of Venice, has been completely shut down.…”
“…The indices generated by the random index generator cross-over operation take place in terms of several filters [ 1 , 2 ] and the filter size [ 2 , 3 ]. The new child generation of chromosomes is processed based on indices and cross-over operation from parent 1 and parent 2.…”
Section: Proposed Workmentioning
confidence: 99%
“…Root parents are mutated to produce the new child chromosome using the random index generator. The example considers the number of filters as [ 1 , 4 ] and the size of filters as [ 2 , 3 ]. FIGURE 3 Mutation Operation …”
Section: Proposed Workmentioning
confidence: 99%
“…That is, COVID-19 infects most people without any indications of symptoms. These, in turn, lead to the drastic spread of the disease to other people, Sam et al [1] Data analysis and classification to predict the student's performance in the undergraduate computing branch and suggest an approach for reducing student dropouts. In Italy, it is observed that Vo Euganeo, which is 50km west of Venice, has been completely shut down.…”
“…Goundar et al [22] applied Decision Tree, Random Forest, Naï ve Bayes, and Support Vector Machine algorithms to build predictive models to determine whether a student will pass or fail the course. The results concluded that the Random Forest algorithm had superior predictive performance capability.…”
In the current age, students' academic performance deterioration is a very crucial problem in engineering education. Prediction of low-performing students at an early stage is important so that their faculties and administration could provide timely support. The present study attempts to perform this prediction task at the entry-time with the help of four single supervised educational data mining algorithms, namely: Decision tree, Naïve Bayes, k-Nearest Neighbor, and Support Vector Machine along with an ensemble method called “Random Forest”. These classifiers have been applied to a students‟ dataset of an Indian Engineering College, having four categories of parameters viz., student‟s background, academic, social, and psychological parameters. Different libraries of Python programming language such as Pandas, Seaborn, Scikit-learn, and Scipy were used for analysis, visualization, classification, and statistics computation, respectively. The present study shows that among all of the five algorithms, Naïve Bayes gives the highest accuracy with 89%, and finally to improve the results, a model is proposed in which three Naïve Bayes classifiers were integrated with the help of 'Bagging'. The achieved accuracy with the proposed model was 91%, with the highest recall and highest precision for identifying low performers.
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