Grade Prediction of Student Academic Performance with Multiple Classification Models

Zhang, Xu; Xue, Ruojuan; Liu, Bin; Lü, Wenpeng; Zhang, Yiqun

doi:10.1109/fskd.2018.8687286

Cited by 14 publications

(12 citation statements)

References 3 publications

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“…Modern Education and Computer Science, 2021, 1, 1-21 implemented. In cases [2,5,24], Random Forest, J48, and NB tend to predict the results more accurately. For SVM, the results achieved are remarkable, as it has maximum F1-measure value after DT [4].…”

Section: Related Workmentioning

confidence: 93%

“…DM and ML techniques are widely applied in field of analytics and predictions. The research work in [2][3][4][5][6][7][8] make use of such algorithms which are Logistic Regression, J48, Decision Tree, Support Vector Machine (SVM), NB, Random Tree, ANN, K-Nearest Neighbour (KNN), MLP, and Random Forest. In few cases, other algorithms are also used such as association rules [9], and clustering [10].…”

Section: Introductionmentioning

confidence: 99%

“… Academic data such as subject marks, previous examination marks are included in [2,14],  Previous semester marks, participation in activities are included in [3,12],  GPA, subject marks and assignment marks are included in [11,15]  Exam scores, absences and attendance is used in [2,7].  Consideration of behavioural data is done in dataset of Kalboard 360 available on Kaggle used in [5,7].  Comment based data is used in [16] for every lesson taught and various attributes are retrieved using text classification on those comments and [17] comprehends questionnaire based input variables.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, many machine learning algorithms tested for the problem. In most of cases in related work, the input parameters that are related target are not identified, such as in [2][3]5]. Also, it is found out that the algorithms those are applied in previous work [7-8, 28, 40] are processed without fine tuning which doesn't pushes limits to know how far an algorithm can accurately predict results.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Study of Supervised Algorithms for Prediction of Students’ Performance

Sathe¹,

Adamuthe²

2021

IJMECS

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Predicting academic performance of the student is crucial task as it depends on various factors. To perform such predictions the machine learning and data mining algorithms are useful. This paper presents investigation of application of C5.0, J48, CART, Naï ve Bayes (NB), K-Nearest Neighbour (KNN), Random Forest and Support Vector Machine for prediction of students' performance. Three datasets from school level, college level and e-learning platform with varying input parameters are considered for comparison between C5.0, NB, J48, Multilayer Perceptron (MLP), PART, Random Forest, BayesNet, and Artificial Neural Network (ANN). Paper presents comparative results of C5.0, J48, CART, NB, KNN, Random forest and SVM on changing tuning parameters. The performance of these techniques is tested on three different datasets. Results show that the performances of Random forest and C5.0 are better than J48, CART, NB, KNN, and SVM.

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Section: Related Workmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Study of Supervised Algorithms for Prediction of Students’ Performance

Sathe¹,

Adamuthe²

2021

IJMECS

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show abstract

“…A dataset was collected from VNU University of Science, Vietnam, and three educational datasets were taken from KDD Cup. Another attempt using the Fuzzy Probabilistic Neural Network was mentioned in [12]. The Probabilistic Neural Network is a 4layer, feed-forward, which includes an algorithm used for classification and mapping.…”

Section: Related Workmentioning

confidence: 99%

RHEM: A Robust Hybrid Ensemble Model for Students’ Performance Assessment on Cloud Computing Course

Sakri¹,

Alluhaidan²

2020

IJACSA

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Creating tools, such as a prediction model to assist students in a traditional or virtual setting, is an essential activity in today's educational climate. The early stage towards incorporating these predictive models using techniques of machine learning focused on predicting the achievement of students in terms of the grades obtained. The research aim is to propose a robust hybrid ensemble model (RHEM) that can warn at-risks students (on Cloud Computing course) of their likely outcomes at the early semester assessment. We hybridised four renowned single algorithms-Naïve Bayes, Multilayer Perceptron, k-Nearest Neighbours, and Decision Tablewith four well-established ensemble algorithms-Bagging, RandomSubSpace, MultiClassClassifier, and Rotation Forestwhich produced 16 new hybrid ensemble classifier models. Hence, we have thoroughly and rigorously built, trained, and tested 24 models all together. The experiment concluded that the Rotation Forest + MultiLayer Perceptron model was the best performing model based on the model evaluation in terms of Accuracy (91.70%), Precision (86.1%), F-Score rate (87.3%), and Receiver Operating Characteristics Area detection (98.6%). Our research will help students identify their likely final grades in terms of whether they are excellent, very good, good, pass, or fail, and, thus, transform their academic conduct to achieve higher grades in the final exam accordingly.

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Factors for Academic Performance Prediction

Zhang

2020

Advances in Intelligent Systems and Computing

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Grade Prediction of Student Academic Performance with Multiple Classification Models

Cited by 14 publications

References 3 publications

Comparative Study of Supervised Algorithms for Prediction of Students’ Performance

Comparative Study of Supervised Algorithms for Prediction of Students’ Performance

RHEM: A Robust Hybrid Ensemble Model for Students’ Performance Assessment on Cloud Computing Course

Factors for Academic Performance Prediction

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