Analyzing students’ performance using multi-criteria classification

Al-Obeidat, Feras; Tubaishat, Abdallah; Tamim, Rana; Shah, Babar

doi:10.1007/s10586-017-0967-4

Cited by 25 publications

(16 citation statements)

References 6 publications

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“…The different techniques used in the student performance predictions are compared with RFBTRF-GWO method. The classifiers such as Support Vector Machine, Decision Tree (DT), Random Forest, and DTFMC [15] are compared with the proposed method in Table 5. The RFBTRF-GWO method has the highest performance compared to the other classifier.…”

Section: Resultsmentioning

confidence: 99%

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Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest

Deepika*¹,

Sathyanarayana²

2019

IJRTE

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The student academic prediction model helps to predict the student performance that helps the university to provide necessary care to the particular students. Efficient prediction model helps to encourage the student for better performance in the academic. In this research, the Relief-F Budget Tree Random Forest with Gray Wolf Optimization (RFBTRF-GWO) method is proposed for the feature selection. The Gray Wolf Optimization (GWO) helps to scale the relevant feature with ranking order from the features selected by the Relief-F Budget Tree Random Forest (RFBTRF). The selected features are given as input to the classifier for the effective prediction. The k-Nearest Neighbor (kNN) and Artificial Neural Network (ANN) are used for the classification. The proposed RFBTRF-GWO method is evaluated on the three datasets such as two UCI datasets and one collected dataset. The RFBTRF-GWO has a higher performance accuracy of 96.2 % while the existing method RFBTRF has an accuracy of 70.88 %.

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Section: Resultsmentioning

confidence: 99%

“…Feras Al-Obeidat, et al [15] developed hybrid technique of decision tree and fuzzy multi-criteria classification in the student academic prediction. There are several factors used to analyze the performance of the system.…”

Section: Related Workmentioning

confidence: 99%

Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest

Deepika*¹,

Sathyanarayana²

2019

IJRTE

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“…Beberapa teknik FMADM yang digunakan antara lain SAW, Weighted Product (WP), Electre, TOPSIS, dan Analytic Hierarchy Process (AHP). Beberapa penelitian menunjukkan bahwa teknik FMADM memberikan alternatif solusi paling optimal pada permasalahan-permasalahan akademik, seperti memprediksi kinerja siswa [10], pengambilan keputusan penerimaan beasiswa [11], penjurusan siswa terkendala [12], dan menetapkan prosedur seleksi mahasiswa baru berdasarkan serangkaian kriteria [13]. Kinerja FMADM dapat dioptimasi dengan mengintegrasikan beberapa teknik yang ada, seperti teknik SAW, TOPSIS, dan Borda, sehingga menghasilkan keputusan yang paling menguntungkan dalam memilih tools untuk meminimalkan sumber daya produksi [14].…”

Section: Pendahuluan Dalam Menyongsong Era Bonus Demografi Dan Menunclassified

“…Koefisien korelasi rank Spearman menunjukkan kekuatan hubungan tiga teknik FMADM yang dibandingkan. Dengan menggunakan (10), koefisien korelasi rank Spearman ketiga teknik FMADM dapat diekspresikan dalam Tabel III.…”

Section: Evaluasi Dataunclassified

Fuzzy Multi-Attribute Decision Making untuk Klasifikasi Potensi Kewirausahaan Berdasarkan Theory of Planned Behavior (Fuzzy Multi-Attribute Decision Making for Classifying Entrepreneurial Potential based on Theory of Planned Behavior)

Rijati

Purwitasari

Sumpeno

et al. 2020

JNTETI

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Indonesia government has launched a program to encourage youth entrepreneurship as a strategy to improve national economy. This paper proposes a method to find an entrepreneurial potential based on academic behavior features that are extracted from the Higher Education Database PDDikti. The proposed approach applies the Fuzzy Multi-Attribute Decision Making (FMADM) technique. Rules for extracting features of student academic behavior were following Theory of Planned Behavior (TPB) and resulting in 14 features. The FMADM model combines Fuzzy Simple Additive Weighting and Fuzzy Technique for Order Preference by Similarity to Ideal Solution, which is called FSAW-TOPSIS. Friedman Test demonstrated that FSAW-TOPSIS gives more optimal solution with the highest Mean Rank of the potential entrepreneurial value of 2.96. Besides, through Hamming Distance Test, FSAW-TOPSIS results the best order with a 98% percentage and ranking of the smallest Squared Error of 0.3%, which makes the proposed model offered a better solution. It can be concluded that using TPB variables in PDDikti environment with FSAW-TOPSIS technique provides an optimal recommendation on student entrepreneurship potential, which can be used as a part of a decision-making system for higher education management.

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“…For both of the datasets (mathematics and Portuguese), you will hardly get any better than 50% accuracy. Depending on the data preprocessing the analyst can obtain better results like in [3] or [4] but still the last grade (G3) depends on G1 and G2.…”

Section: Related Workmentioning

confidence: 99%

Student Testing Activity Dataset from Data Structures Course

Popescu

Mihaescu²,

Teodorescu

et al. 2020

RoCHI - International Conference on Human-Computer Interaction

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E-learning platforms became more and more popular not only for distance learning but also for learning in full-time education. As this popularity grows, we can use the data extracted from them to complement the professor's work and make predictions regarding students' performance. In this paper, we present a dataset extracted from our e-Learning platform, which is based on the logs collected from testing activity. The focus of this paper is to present the dataset; the experiments presented in the paper are meant to explore the dataset along with its capabilities. The dataset consists of attributes relevant to the testing activity and provides labels which consist of average test grade and final exam grade. Our focus when building the dataset was to keep only the attributes relevant for the learning activity and to provide means to analyse and predict the student's final grade or failure. The paper presents the structure of the dataset, the methodology of collecting the data and experiments using several popular algorithms. The experimental results reveal that the actions performed by the users correlate with the results of the tests and the exam failure can be predicted with a pretty good accuracy using the default set of tuning parameters for our algorithms. As feature work, we can extend the set of experiments with other algorithms, and we can also use parameter tuning for each algorithm for a slight increase in performance.

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Analyzing students’ performance using multi-criteria classification

Cited by 25 publications

References 6 publications

Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest

Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest

Fuzzy Multi-Attribute Decision Making untuk Klasifikasi Potensi Kewirausahaan Berdasarkan Theory of Planned Behavior (Fuzzy Multi-Attribute Decision Making for Classifying Entrepreneurial Potential based on Theory of Planned Behavior)

Student Testing Activity Dataset from Data Structures Course

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