Multi-models of Educational Data Mining for Predicting Student Performance in Mathematics: A Case Study on High Schools in Cambodia

Sokkhey, Phauk; Navy, Sin; Tong, Ly; Okazaki, Takeo

doi:10.5573/ieiespc.2020.9.3.217

Cited by 17 publications

(16 citation statements)

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“…The study of this work utilized the four prediction models as follows: 1) K-nearest neighbor (KNN): KNN KNN is known as an popular non-parametric EDM models utilized in many classicaiton problems. The KNN is confirmed to be a succesful classifier in our classifcation problem as proposed in the previous work [24]. Similarly to other classifiers, the KNN is noise-sensitive classifier.…”

Section: E Classification Algorithmssupporting

confidence: 67%

“…The comparative study of prediction models on predicting student performance was conducted in [23]. The improvement version of the comparative study was conducted in [24]. The experimental results of both works indicated that k-nearest neighbor (KNN), two tree-based models: C5.0 and random forest (RF) are the optimal models.…”

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. Four baseline models, naïve Bayes (NB), support vector machine (SVM), C5.0, and random forrest (RF) were utilized.…”

Section: E Classification Algorithmsmentioning

confidence: 99%

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

Sokkhey¹,

Okazaki²

2020

IJACSA

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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.

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Section: E Classification Algorithmssupporting

confidence: 67%

Section: E Classification Algorithmsmentioning

confidence: 99%

Section: E Classification Algorithmsmentioning

confidence: 99%

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

Sokkhey¹,

Okazaki²

2020

IJACSA

Self Cite

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“…The random forest was found to be the best prediction model. The improvement of the previously proposed prediction models and additional models for predicting student performance was further carried out [25]. K-nearest neighbor (KNN), ensemble decision tree (Boosted C5.0 and Bagged CART), and random forest (RF) outperformed the rest prediction models.…”

Section: E Classification Algorithmsmentioning

confidence: 99%

“…KNN is one of the effective non-parametric EDM models used for classification tasks. The KNN is an effective classifier and produces higher classification results [25]. Like many other classifiers, the k-NN classifier is noise-sensitive.…”

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

IJACSA

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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.

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A novel color labeled student modeling approach using e-learning activities for data mining

Buyrukoğlu

2022

Univ Access Inf Soc

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Student modeling approaches are important to identify students’ needs, learning styles, and to monitor their improvements for individual modules. Lecturers may incorrectly identify the students’ needs and learning styles based on solely an exam grade or performance in the class. In doing so, students need to be classified using more parameters such as e-learning activities, attendance to virtual live class (for theory and practice) and submission time of the assignment, etc. This study proposes a novel color-labeled student modeling/classification approach using e-learning activities to identify students’ learning styles and to monitor students’ weekly improvements for individual modules. A novel Student Classification Rate (SCR) formula was created by combining three stages including pre-study stage, virtual_class stage, and virtual_LAB_class stage. In the evaluation part of the SCR, Artificial Neural Network and Random Forest algorithms were employed based on two different feature sets for an Object-Oriented Programming Module. Feature set 1 consisted of a combination of e-learning and regular data while the feature set 2 was referred as the combination of the SCR and the regular data. Random Forest yielded the lowest MAE (0.7) by using feature set 2. Also, the majority of the students’ (81%) learning styles referred to attending the live virtual class. Students’ weekly learning progress was also monitored successfully since the Pearson correlation was measured as 0.78 with the 95% confidence interval between the mean of SCR and lab grades. Additionally, SCR used for two more different modules yielded convincing results in the determination of students’ learning styles. The obtained results reveal that the proposed SCR approach has significant potential to correctly classify students, identify students’ learning styles, and help the lecturer to monitor the students' weekly progress. Finally, it seems that SCR would have a significant impact on improvement of students learning.

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Multi-models of Educational Data Mining for Predicting Student Performance in Mathematics: A Case Study on High Schools in Cambodia

Cited by 17 publications

References 0 publications

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

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

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

A novel color labeled student modeling approach using e-learning activities for data mining

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