A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Students Performance

Chug, Anuradha; Velmurugan, T.

doi:10.17485/ijst/2015/v8i15/74555

Cited by 65 publications

(33 citation statements)

References 8 publications

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“…Each algorithm has its own learning technique to detect data that are related to other data in the data set, which helps it build an accurate model that could predict values that are unknown, the popular classifiers are support vector machines, decision tree, neural networks, naive Bayes, and rule-based classifiers. 7 For each of these classifiers, there are a number of algorithms and versions that have been modified and enhanced over time. Researchers often apply multiple algorithms and compare results and accuracy because different algorithms work better in specific scenarios.…”

Section: Classificationmentioning

confidence: 99%

Study of general education diploma students’ performance and prediction in Sultanate of Oman, based on data mining approaches

Mazidi

Abusham

2018

International Journal of Engineering Business Management

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The Sultanate of Oman is one of the first countries in the Middle East to utilize technology in the management of the education process. Over time, education data have accumulated, and at the present, large volumes of data with numerous types of statistics have been collected as operational data. This research article takes the advantage of these data and applied predictive data mining approaches to study the performance of general education diploma students (year 12 of school). The decision tree as a classification method has been applied and a prediction model is constructed with ability to show relatively high accuracy of students' performance prior to year 12 of school, using 30% test data of nearly 6000 student's records. The significant variables that influence students' performance are identified which would help stakeholders and decision makers plan ahead and have more time to prepare for decisions.

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Section: Classificationmentioning

confidence: 99%

Study of general education diploma students’ performance and prediction in Sultanate of Oman, based on data mining approaches

Mazidi

Abusham

2018

International Journal of Engineering Business Management

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“…It is used to evaluate the classifiers amongst themselves. According to Landis and Koch, the kappa value 0.4323 comes under the category of moderate and 0.4055 comes under the category of fair [23]. There are also other studies that get Naive Bayes accuracy much better than the decision tree's accuracy.…”

Section: A Naive Bayes Classification Using Rmentioning

confidence: 99%

Comparing the Performance of Naive Bayes And Decision Tree Classification Using R

Yadav¹,

Thareja²

2019

IJISA

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The use of technology is at its peak. Many companies try to reduce the work and get an efficient result in a specific amount of time. But a large amount of data is being processed each day that is being stored and turned into large datasets. To get useful information, the dataset needs to be analyzed so that one can extract knowledge by training the machine. Thus, it is important to analyze and extract knowledge from a large dataset. In this paper, we have used two popular classification techniques-Decision tree and Naive Bayes to compare the performance of the classification of our data set. We have taken student performance dataset that has 480 observations. We have classified these students into different groups and then calculated the accuracy of our classification by using the R language. Decision tree uses a divide and conquer method including some rules that makes it easy for humans to understand. The Naive Bayes theorem includes an assumption that the pair of features being classified are independent. It is based on the Bayes theorem.

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“…This will improvise the understanding of subjects taught by the staffs. C.Anuradha and T.Velmurugan [16] focused the data mining algorithms for the classification of the students based on the attributes selected reveals that the prediction rates are not uniform among the algorithms. The range of prediction varies from 61-75 %.…”

Section: Review Of Literaturementioning

confidence: 99%

Analyzing student performance using evolutionary artificial neural network algorithm

Arunachalam¹,

Velmurugan²

2018

IJET

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Educational Data Mining (EDM) and Learning Systematic (LS) research have appeared as motivating areas of research, which are clarifying beneficial understanding from educational databases for many purposes such as predicting student's success factor. The ability to predict a student's performance can be beneficial in modern educational systems. This research work aims at developing an evolutionary approach based on genetic algorithm and the artificial neural network. The traditional artificial neural network lacks predicting student performance due to the poor modeling structure and the capability of assigning proper weights to each node under the hidden layer. This problem is overwhelmed with the aid of genetic algorithm optimization approach which produces appropriate fitness function evaluation in each iteration of the learning process. The performances gradually increase the accuracy of the prediction and classification more precisely.

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A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Students Performance

Cited by 65 publications

References 8 publications

Study of general education diploma students’ performance and prediction in Sultanate of Oman, based on data mining approaches

Study of general education diploma students’ performance and prediction in Sultanate of Oman, based on data mining approaches

Comparing the Performance of Naive Bayes And Decision Tree Classification Using R

Analyzing student performance using evolutionary artificial neural network algorithm

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