A Data Mining Approach to Predict Academic Performance of Students Using Ensemble Techniques

Ajibade, Samuel-Soma M.; Ahmad, Nor Bahiah; Shamsuddin, Siti Mariyam

doi:10.1007/978-3-030-16657-1_70

Cited by 21 publications

(9 citation statements)

References 6 publications

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“…Naive Bayes achieved 78.77% accuracy as compared to 88.64% in case of standard ANFIS algorithm. From recent literature, the study performed in [24] achieved second best accurate results with 94.10%. Overall, from comparisons, it can be easily affirmed that the proposed ANFIS training method using HGSO not only outperformed recently introduced methods from literature, but also various other machine learning algorithms implemented on SPP in this study.…”

Section: Resultsmentioning

confidence: 83%

“…Overall, from comparisons, it can be easily affirmed that the proposed ANFIS training method using HGSO not only outperformed recently introduced methods from literature, but also various other machine learning algorithms implemented on SPP in this study. Random Forest [28] 85.00% ANN [27] 81.70% Hybrid Stacking ensemble technique [26] 84.30% DNN [25] 86.01% ADDE [25] 94.10% Tech [24] 84…”

Section: Resultsmentioning

confidence: 99%

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A Novel Metaheuristic Approach to Optimization of Neuro-Fuzzy System for Students' Performance Prediction

Hussain¹,

Talpur²,

Aftab³

et al. 2020

JSCDM

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Today, technology is aggressively adopted by educational institutes to maximize output of existing resources, to benefit both students and the teachers. The use of technology-based learning makes it possible to collect enormous amount of data pertaining to student and teacher performance. This data is then used for extracting meaningful information that deems useful for administration in making efficient decisions. For this purpose, literature shows significant advantages of data mining and machine learning techniques with regards to educational data mining [1,2,3]. Utilizing behavioral and performance data of students and teachers, researchers have applied these methods to discover knowledge in hidden patterns, to assess the effectiveness of courses, teaching methodologies, curriculum, and overall education system [4]. More specifically, the educational data mining is now being used to predict students' performances in order to identify the vulnerable ones at risk of failing the upcoming examinations so that more focus could be drawn on them to solve any difficulties [5,6].

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Section: Resultsmentioning

confidence: 83%

Section: Resultsmentioning

confidence: 99%

A Novel Metaheuristic Approach to Optimization of Neuro-Fuzzy System for Students' Performance Prediction

Hussain¹,

Talpur²,

Aftab³

et al. 2020

JSCDM

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show abstract

“…Ajibade et al [17] proposed one more new performance prediction model for students which be contingenton data mining methods which integrate new features known as behavioral features of students.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble Swarm based Feature Selection (ESFS) and Ensemble Three Classifiers (ETCS) to Predict Student’s Academic Performance

2019

ijeat

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Recently Educational Data Mining (EDM) has attracted many researchers in recent years. Many techniques of data mining are formulated to generate the techniques of the knowledge that is hidden within the educational data. The knowledge which is extracted aid the educational institutions to enhance the teaching process and learning methods. These improvements enhance the student performance and the performance of overall outputs. In EDM, Feature Selection (FS) plays a significant role in the improvement of quality of the models used for the purpose of prediction of educational datasets. Single feature selection algorithms do not render enhanced results of prediction. In this proposed work, Ensemble Swarm based Feature Selection (ESFS) and Ensemble Three Classifiers (ETCs) is formulated to classify the performance of students based on the selected features. This work concentrates on ESFS techniques are formulated to select the important and intrinsic features before the process of classification, ETCs are proposed. The samples are selected from the knowledge repository, which is initially pre-processed by means of Min Max Normalization (MMN) and Z Score Normalization (ZCN) method. Then the selected attributes from the technique called Ensemble Swarm based Feature Selection (ESFS) are combined to the learner’s communication together with e-learning management system. ESFS algorithm fuses the Fuzzy Membership Genetic Algorithm (FMGA) and Improved Clonal Selection Algorithms (ICSAs). Also, Ensemble Three Classifiers (ETCs) is identified for the prediction of students’ performance by combining the qualifiers like Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) classifier and Decision Tree (DT). A widespread ensemble approach namely Bagging is utilized to combine all the results of three classifiers. The results that are obtained are found to have strong relationship among the learner’s behaviors and their academic achievement.

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“…Analysis of educational data using data-mining techniques helps extract unique information of students from educational database and use that hidden information to solve various academic problems of students by understanding learners, improve teaching-learning methods and process [6,7]. Moreover, these data mining techniques help educational stakeholders to make quality decisions to enhance students' outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…Various methods like Decision tree and Naïve Bayesian were used by many researchers for predicting learners' academic performance and make decisions to help those who need help immediately [7]. Other researchers used ensemble methods such as Random Forest (RF), AdaBoosting, and Bagging as classification methods [7,8]. Different data mining methods can solve different educational problems such as classification and clustering.…”

Section: Introductionmentioning

confidence: 99%

Predicting Students' Academic Performance in Educational Data Mining Based on Deep Learning Using TensorFlow

Abubakari¹,

Arifin²,

Hungilo³

2020

IJEME

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A Data Mining Approach to Predict Academic Performance of Students Using Ensemble Techniques

Cited by 21 publications

References 6 publications

A Novel Metaheuristic Approach to Optimization of Neuro-Fuzzy System for Students' Performance Prediction

A Novel Metaheuristic Approach to Optimization of Neuro-Fuzzy System for Students' Performance Prediction

Ensemble Swarm based Feature Selection (ESFS) and Ensemble Three Classifiers (ETCS) to Predict Student’s Academic Performance

Predicting Students' Academic Performance in Educational Data Mining Based on Deep Learning Using TensorFlow

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