Improving Student Academic Performance Prediction Models using Feature Selection

Nuankaew, Wongpanya; Thongkam, Jaree

doi:10.1109/ecti-con49241.2020.9158286

Cited by 14 publications

(14 citation statements)

References 12 publications

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“…[26] used the [5, 6, 16, 17, 21, 25, 27, 28, 30, 31, 33, 35, 37, 40, 41, 43, 44, 46, 52-57, 59, 60] 26 (60%) Family information Father's qualification, mother's qualification, father's occupation, and mother's occupation. [16,23,28,30,41,46,52,53,55] 9 (21%)…”

Section: Further Discussionmentioning

confidence: 99%

“…Recently, several studies [15][16][17][52][53][54][55][56][57][58][59] have focused on predicting the student's performance at the end of the academic year. A study published in Ref.…”

Section: Predicting Student's Performance At the End Of The Academic ...mentioning

confidence: 99%

“…Similarly, authors in Ref. [55] conducted a study on a dataset which contains 9,458 student records and 14 attributes, including demographics and environment features, to classify the CGPA into low, medium, or good classes. Two datasets were used in this study; the first dataset represents students' achievement in the first year, while the second one represents the students' achievement in the second year.…”

Section: Predicting Student's Performance At the End Of The Academic ...mentioning

confidence: 99%

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Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Alwarthan

Aslam

Khan

2022

Applied Computational Intelligence and Soft Computing

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Recently, educational institutions faced many challenges. One of these challenges is the huge amount of educational data that can be used to discover new insights that have a significant contribution to students, teachers, and administrators. Nowadays, researchers from numerous domains are very interested in increasing the quality of learning in educational institutions in order to improve student success and learning outcomes. Several studies have been made to predict student achievement at various levels. Most of the previous studies were focused on predicting student performance at graduation time or at the level of a specific course. The main objective of this paper is to highlight the recently published studies for predicting student academic performance in higher education. Moreover, this study aims to identify the most commonly used techniques for predicting the student's academic level. In addition, this study summarized the highest influential features used for predicting the student academic performance where identifying the most influential factors on student’s performance level will help the student as well as the policymakers and will give detailed insights into the problem. Finally, the results showed that the RF and ensemble model were the most accurate models as they outperformed other models in many previous studies. In addition, researchers in previous studies did not agree on whether the admission requirements have a strong relationship with students' achievement or not, indicating the need to address this issue. Moreover, it has been noticed that there are few studies which predict the student academic performance using students’ data in arts and humanities major.

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Section: Further Discussionmentioning

confidence: 99%

“…Recently, several studies [15][16][17][52][53][54][55][56][57][58][59] have focused on predicting the student's performance at the end of the academic year. A study published in Ref.…”

Section: Predicting Student's Performance At the End Of The Academic ...mentioning

confidence: 99%

Section: Predicting Student's Performance At the End Of The Academic ...mentioning

confidence: 99%

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Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Alwarthan

Aslam

Khan

2022

Applied Computational Intelligence and Soft Computing

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“…RF is a supervised machine learning algorithm that is used for both classification and regression problems [35]. The RF has proven to be highly extremely successful in predicting student performance as it outperformed other classification techniques such as KNN, SVM, NB, and LR [34] [8] [3] [4] [36].…”

Section: ) Artificial Neural Network (Ann)mentioning

confidence: 99%

An Explainable Model for Identifying At-Risk Student at Higher Education

2022

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Nowadays, researchers from various fields have shown great interest in improving the quality of learning in educational institutes in order to improve student achievement and learning outcomes. The main objective of this study was to predict the at-risk student of failing the preparatory year at an early stage. This study applies several educational data mining algorithms including RF, ANN, and SVM to build three classification models to meet the objectives of this study. Moreover, different features selection methods namely RFE, and GA have been examined to find the best subset of the highest influential features. Furthermore, several sampling approaches are applied to balance the dataset used in this study, including SMOTE, and SMOTE-Tomek Link. Three datasets related to the preparatory year student from the humanities track at IAU were used in this study. The collected datasets are imbalanced datasets, SMOTE-Tomek Link technique has been used to balance the three proposed datasets. The results showed that RF outperformed other techniques as it records the highest performance for building the models. Moreover, RFE with Mutual Information finds the best subset of features to build the first model. Finally, this study not only developed several classification models to identify at-risk students, but it also went a step further by employing XAI techniques such as LIME, SHAP, and the global surrogate model to explain the proposed prediction models, explaining the output and highlighting the reasons for the student failure.

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“…The training data and distinct features are randomly selected into multiple sets. Then the models are constructed using a collection of decision trees [36], where the out-of-bag data are collected into the data test for the prediction. Finally, the model results are brought into voting, and the result with the most votes is the solution.…”

Section: Classification Algorithmsmentioning

confidence: 99%

Weighted Voting Ensemble for Depressive Disorder Analysis with Multi-objective Optimization

Nuankaew

Doenribram

et al. 2022

Curr. Appl. Sci. Technol.

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The Twitter platform is a popular tool that is widely used by researchers to collect data on users’ personal lives, feelings and emotions. These data sets can be further analyzed using text mining techniques to predict the disorder of depression. There are nine symptoms of depression that are classified by American Psychiatric Association using DSM-5 criteria. The symptoms can be difficult to identify effectively. The unweighted vote ensemble is not practical for multi-class data. Therefore, this research proposes the multi-objective optimization algorithms for depressive symptom prediction modeling (MOADSP) for the weighted voting ensemble, which can improve its effectiveness compared to the singer model. The objectives of this research were 1) to find the appropriate number of features; 2) to improve the weights of the prediction models based on the recall of the class for the ensemble; and 3) to compare the performance of the single, unweighted, and weighted voting ensemble models for depressive disorder. An information gain was used to select the features. The single classification techniques used in the experiment that had their frameworks tested were the Naïve Bayes, Random Forest, and K-Nearest techniques, while the vote ensemble models used were the unweighted and weighted models. MOADSP was applied to the weighted vote ensemble models. The results showed that the best recall classifier was KNN (98.60%), and the highest recall classifier was AVG TP weighted (98.43%) for the training model. The highest recall in the class depressive classifier was AVG TP weighted (80.00%) for the testing. This proposed method was beneficial for the prediction of depressive disorder.

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Improving Student Academic Performance Prediction Models using Feature Selection

Cited by 14 publications

References 12 publications

Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review

An Explainable Model for Identifying At-Risk Student at Higher Education

Weighted Voting Ensemble for Depressive Disorder Analysis with Multi-objective Optimization

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