Analyze Students Performance of a National Exam Using Feature Selection Methods

Hashemi, Hanieh; Parvasideh, Parvane; Larijani, Zahra Hasan; Moradi, Fatemeh

doi:10.1109/iccke.2018.8566671

Cited by 4 publications

(13 citation statements)

References 7 publications

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“…As concerned by some of the researchers, data preprocessing is essential for improving data quality and impacting its reliability for data mining algorithms [38] whereby failing to do so will allow the erroneous conclusions to be made by the prediction model since the raw data contains a lot of unwanted features and noise [27]. Researchers in [25] emphasized that the data mining quality is mainly affected by the acquired data and features.…”

Section: Feature Selection Techniquesmentioning

confidence: 99%

“…Specifically, educational data mining focuses on developing the algorithms that can uncover the hidden patterns in educational data since the study involves with numerous features of students' information that need to be analyzed [23]- [26]. However, most of the acquired data are comprehensive which also contain the unwanted features whereby without data preprocessing, some misinterpretations might be made by the model which indicate inaccuracy in predicting students' performance [27], [28]. Attributes in the dataset with minimal variance, where the values exhibit negligible differences, are excluded as they contribute insignificantly to the mining process [29].…”

Section: Introductionmentioning

confidence: 99%

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Feature selection techniques and classification algorithms for student performance classification: a review

Alias,

Hambali,

Abdul Aziz

et al. 2024

IJECE

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The process of categorizing students’ performance based on input data, encompassing demographic information and final exam results, is recognized as student performance classification. Educational data mining has gained traction in assessing students’ performance. However, this study entails the need to analyze the diverse attributes of students’ information within an educational institution by using data mining techniques. This study thoroughly examines both previous and current methodologies presented by researchers, addressing two main aspects: data preprocessing and classification algorithms applied in student performance classification. Data preprocessing specifically delves into the exploration of feature selection techniques, encompassing three types of feature selection and search methods. These techniques aim to identify the most significant features, eliminate unnecessary ones, and reduce data dimensionality. In addition, classification algorithms play a crucial role in categorizing or predicting student performance. Models such as k-nearest neighbors (KNN), decision tree (DT), artificial neural networks (ANN), and linear models (LR) were scrutinized based on their performance in prior research. Ultimately, this study highlights the potential for further exploration of feature selection techniques like information gain, Chi-square, and sequential selection, particularly when applied to new datasets such as students’ online learning activities, utilizing a variety of classification algorithms.

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Section: Feature Selection Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature selection techniques and classification algorithms for student performance classification: a review

Alias,

Hambali,

Abdul Aziz

et al. 2024

IJECE

View full text Add to dashboard Cite

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“…The main objective of this work consisted in analyzing student performance by collecting questioners from various colleges. The approach described in [Hashemi et al 2018] also used filtering and wrapper methods for FS. The main focus of the work was to predict university student acceptance based on results from a national exam in Iran.…”

Section: Related Workmentioning

confidence: 99%

“…Other works include FS as part of a classification model, and are commonly referred to as embedded approaches [Gitinabard et al 2018;Niu et al 2018;Hassan et al 2019;Teodoro and Kappel 2020]. Furthermore, comparisons between different FS techniques have been examined in order to identify the most suitable to specific scenarios [Punlumjeak and Rachburee 2015;Zaffar et al 2018;Hashemi et al 2018;Ajibade et al 2019;Ahmed et al 2019;Govindasamy and Velmurugan 2019;Ahmed et al 2020;Chaves et al 2021;Jalota and Agrawal 2021].…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm with Flexible Fitness Function for Feature Selection in Educational Data: Comparative Evaluation

Albuquerque

Tarrataca

Brandão

et al. 2022

jidm

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Educational Data Mining is an interdisciplinary field that helps understand educational phenomena through computational techniques. The databases of educational institutions are usually extensive, possessing many descriptive attributes that make the prediction process complex. In addition, the data can be sparse, redundant, irrelevant, and noisy, which can degrade the predictive quality of the models and affect computational performance. One way to simplify the problem is to identify the least important attributes and omit them from the modeling process. This can be performed by employing attribute selection techniques. This work evaluates different feature selection techniques applied to open educational data and paired alongside a genetic algorithm with a flexible fitness function. The methods and results described herein extend a previously published paper by: (i) describing a larger set of computational experiments; (ii) performing a hypothesis test over different classifiers; and (iii) presenting a more in-depth literature revision. The results obtained indicate an improvement in the classification process.

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“…Another study applying Naïve Bayes with information gain on student performance in a national exam showed that the Naïve Bayes algorithm with information gain obtained an accuracy of 82.1%. [29]. Another study applying Naïve Bayes with forward selection on student academic performance showed that by applying some features, the predictive model of students' academic grades could perform better, so the accuracy of 94.43% was obtained with three selected features rather than only using Naïve Bayes (85.56%) [30].…”

Section: Introductionmentioning

confidence: 99%

Classification of Student Graduation using NaÃ¯ve Bayes by Comparing between Random Oversampling and Feature Selections of Information Gain and Forward Selection

Fahrudy

Uyun

2022

JOIV : Int. J. Inform. Visualization

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Class-imbalanced data with high attribute dimensions in datasets frequently contribute to issues in a classification process as this can affect algorithms’ performance in the computing process because there are imbalanced numbers of data in each class and irrelevant attributes that must be processed; therefore, this needs for some techniques to overcome the class-imbalanced data and feature selection to reduce data complexity and irrelevant features. Therefore, this study applied random oversampling (ROs) method to overcome the class-imbalanced data and two feature selections (information gain and forward selection) compared to determine which feature selection is superior, more effective and more appropriate to apply. The results of feature selection then were used to classify the student graduation by creating a classification model of Naïve Bayes algorithm. This study indicated an increase in the average accuracy of the Naïve Bayes method without the ROs preprocessing and the feature selection (81.83%), with the ROs (83.84%), with information gain with 3 selected features (86.03%) and forward selection with 2 selected features (86.42%); consequently, these led to increasing accuracy of 4.2% from no pre-processing to information gain and 4.59% from no pre-processing to forward selection. Therefore, the best feature selection was the forward selection with 2 selected features (GPA of the 8th semester and the overall GPA), and the ROs and both feature selections were proven to improve the performance of the Naïve Bayes method.

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Analyze Students Performance of a National Exam Using Feature Selection Methods

Cited by 4 publications

References 7 publications

Feature selection techniques and classification algorithms for student performance classification: a review

Feature selection techniques and classification algorithms for student performance classification: a review

A Genetic Algorithm with Flexible Fitness Function for Feature Selection in Educational Data: Comparative Evaluation

Classification of Student Graduation using NaÃ¯ve Bayes by Comparing between Random Oversampling and Feature Selections of Information Gain and Forward Selection

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