A Hybrid Scheme for Feature Selection of High Dimensional Educational Data

Ali, Usman; Arif, Khawaja Sarmad; Qamar, Usman

doi:10.1109/comtech.2019.8737829

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…According to Usman Ali et al [7] in 2019, huge numbers of records in educational data sets can make it difficult to produce data of high quality. Many scholars in the field of education are now analysing data utilizing the DM technique.…”

Section: )mentioning

confidence: 99%

Machine Learning Based Result Analysis Prediction of Student’s Academic Performance

Bodkhe¹

2023

IJRASET

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Failure at any phase of education happens frequently. The rise in drop - out rates is a result of numerous reasons. Poor grades are one of the biggest causes of school abandonment. This has an influence on performance because so many students find it difficult to adjust to the institution's learning environment once they get there. Other factors include student participation in extracurricular tasks and politics. Learners' performances frequently tend to be unsatisfactory for these different predictable and unpredictable reasons, which have an impact on development. As a result, it's important to examine undergraduate results to identify the real reasons for students' varied level of performance. The primary goal of our research work is to identify the numerous variables that affect achievement at the under-graduation level. Therefore, the main motivation behind this effort is to help students identify the factors that lead to their performance so that they can take action to change their results. The learners, course teachers, and others will have the opportunity to improve the environment once the major elements have been recognized and assessed. This paper highlights the importance of using student data to drive improvement in education planning. It then presents techniques of how to obtain knowledge from databases such as large arrays of student data from academic Institution databases. To early predict the student’s academic performance, we have proposed deep learning model of Recurrent Neural (RNN) classifier. This proposed methodology is compared with various traditional machines learning classification models and deep learning classifier.

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Section: )mentioning

confidence: 99%

Machine Learning Based Result Analysis Prediction of Student’s Academic Performance

Bodkhe¹

2023

IJRASET

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“…Then data mining technique is used for prediction. High dimensional educational data for feature selection of hybrid scheme is described in [11]. The feature selection methods like wrapper and filter method is given to input student's data.…”

Section: Introductionmentioning

confidence: 99%

Educational Data Classification using Data Mining and Kernel Ensemble Classifier

2019

IJITEE

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The success of students gives the good name for institution and it become popular. Due to the large number of student’s database it is difficult to identify the performance and activities of each student. The educational data mining is used to identify the performance and status of the students individually. In this study, the Educational Data Classification (EDC) using data mining technique and kernel ensemble classification using Support Vector Machine (SVM) based kernels like linear, polynomial, quadratic and Radial Basis Function (RBF) is discussed. Initially the data preprocessing is made to remove the raw data into understandable format. The SVM kernels like linear, polynomial, quadratic and radial basis function based ensemble classifier is used for classification of student’s data. The data mining is used for making final decision of student’s performance in class like activities and interaction with electronic learning system. The performance of the system is evaluated by kalboard 360 database. The performance of the system is made by classification accuracy of 72.52% using SVM kernel ensemble classification

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