Hybrid Machine Learning Algorithms for Predicting Academic Performance

Self Cite

All educational institutions always try to investigate the learning behaviors of students and give early prediction toward student's outcomes for interventing and improving their learning performance. Educational data mining (EDM) offers various effective prediction models to predict student performance. Simultaneously, feature selection (FS) is a method of EDM that is utilized to determine the dominant factors that are needed and sufficient for the target concept. FS method extracts high-quality data that reduce the complexity of the prediction task that can increase the robustness of decision rule. In this paper, we provide a comparative study of feature selection methods for determining dominant factors that highly affect classification performance and improve the performance of prediction models. A new feature selection CHIMI based on ranked vector score is proposed. Analysis of feature sets of each FS method to get the dominant set is executed. The experimental results show that by using the dominant set of the proposed CHIMI method, the classification performance of the proposed models is significantly improved.

Section: E Classification Algorithmsmentioning

confidence: 99%

“…2) Hybrid C5.0 and Hybrid RF: Hybrid C5.0 and Hybrid RF are the developed models that were studied in our earlier work [25]. The study gave the development and improvement www.ijacsa.thesai.org version of [23] [24] for prediction academic performance.…”

Section: E Classification Algorithmsmentioning

confidence: 99%

Study on Dominant Factor for Academic Performance Prediction using Feature Selection Methods

Sokkhey¹,

Okazaki²

2020

Self Cite

“…2) Hybrid C5.0 and Hybrid RF: In the earlier work [26], we have proposed hybrid machine learning models which are the combination of baseline classifiers (support vector machine (SVM), naïve Bayes (NB), C5.0, and random forest (RF)) with principal component analysis (PCA) and validated by 10-fold cross-validation. The Hybrid C5.0 (C5.0+PCA+10-CV) and Hybrid RF (RF+PCA+10-CV) were found to be the best classifiers in our classification problem.…”

Section: ) K-nearest Neighbor (Knn)mentioning

confidence: 99%

Developing Web-based Support Systems for Predicting Poor-performing Students using Educational Data Mining Techniques

Sokkhey¹,

Okazaki²

2020

Self Cite

The primary goal of educational systems is to enrich the quality of education by maximizing the best results and minimizing the failure rate of poor-performing students. Early predicting student performance has become a challenging task for the improvement and development of academic performance. Educational data mining is an effective discipline of data mining concerned with information integrated into the education domain. The study is of this work is to propose techniques in educational data mining and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models was conducted. Subsequently, high performing models were developed to get higher performance. The hybrid random forest named Hybrid RF produces the most successful classification. For the context of intervention and improving the learning outcomes, a novel feature selection method named MICHI, which is the combination of mutual information and chi-square algorithms based on the ranked feature scores is introduced to select a dominant set and improve performance of prediction models. By using the proposed techniques of educational data mining, and academic performance prediction system is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental results and evaluation surveys report the effectiveness and usefulness of the developed academic prediction system. The system is used to help educational stakeholders for intervening and improving student performance.

“…Phase 3 involved the building, training, and testing of all the hybrid ensemble-based models by hybridising ensemble algorithms with classification algorithms as base learners [14] [16]. The models were BAG+NB, BAG+MLP, BAG +KNN and BAG+DT.…”

Section: Phase 3 -Train Models With Hybridisationmentioning

confidence: 99%

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

Sakri¹,

Alluhaidan²

2020

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.