Mining Educational Data In Predicting Th Pdf Computer Programming In this meta analysis, we find that most used data mining techniques for student’s academic performance prediction are decision tree algorithm, naive bayes algorithm, random forest algorithm, classification and regression trees algorithm (cart), j48, logistic regression, ladtree and reptree. Abstract —this paper highlights important issues of higher education system such as predicting student's academic performance. this is trivial to study predominantly from the point of view of the institutional administration, management, different stakeholder, faculty, students as well as parents.
Pdf Data Mining And Predicting Student Performance A decade of research work conducted between 2010 and november 2020 was surveyed to present a fundamental understanding of the intelligent techniques used for the prediction of student. The problem of student final grade prediction in a particular course has recently been addressed using data mining techniques. in this paper, we present two different approaches solving this task. The primary goal of this article is to provide a thorough overview of data mining approaches to predict students’ academic performance, as well as how various prediction techniques aid in determining the most significant students’ attributes which contribute to students’ performance. This paper provides a systematic review of the spp study from the perspective of machine learning and data mining.
Pdf Predicting Student Performance With Data Mining The primary goal of this article is to provide a thorough overview of data mining approaches to predict students’ academic performance, as well as how various prediction techniques aid in determining the most significant students’ attributes which contribute to students’ performance. This paper provides a systematic review of the spp study from the perspective of machine learning and data mining. This section adopted traditional machine learning algorithms to predict student performance on two data sets, i.e., a private data set from our institution and a public data set. Educational data mining, random forest, decision tree, naive bayes, bayes network. this paper highlights important issues of higher education system such as predicting student’s academic performance. In this study, we have collected students' data from two undergraduate courses. three different data mining classification algorithms (naï ve bayes, neural network, and decision tree) were. In this study, we investigated several data mining tools and algorithms for predicting student performance. there is significant promise for predicting student performance and facilitating more tailored and successful educational experiences by using data mining techniques.
Comments are closed.