Predicting students’ performance in English and Mathematics using data mining techniques

Roslan, Muhammad Haziq Bin; Chen, Chwen Jen

doi:10.1007/s10639-022-11259-2

Cited by 11 publications

(8 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study emphasized the importance of previous academic success, demography, and psychological characteristics in predicting performance. The study also demonstrated that a student's previous Mathematics performance can predict their MCE English performance concluding that the two disciplines were interrelated [13].…”

Section: Related Workmentioning

confidence: 80%

See 1 more Smart Citation

Enhancing Student's Performance Classification Using Ensemble Modeling

Nafea,

Mishlish,

Shaban

et al. 2023

Iraqi Journal For Computer Science and Mathematics

View full text Add to dashboard Cite

A precise prediction of student performance is an important aspect within educational institutions toimprove results and provide personalized support of students. However, the predication accuracy of studentperformance considers an open issue within education field. Therefore, this paper proposes a developed approachto identify performance of students using a group modeling. This approach combines the strengths of multiplealgorithms including random forest (RF), decision tree (DT), AdaBoosts, and support vector machine (SVM).Afterward, the last ensemble estimates as one of the bets logistic regression methods was utilized to create a robustand reliable predictive model because it considers The experiments were evaluated using the Open UniversityLearning Analytics Dataset (OULAD) benchmark dataset. The OULAD dataset considers a comprehensive datasetcontaining various characteristics related to the student’s activities thereby five cases based on the utilized datasetwere investigated. The experiment results showed that the proposed ensemble model presented its ability withaccurate results to classify student performance by achieving 95% of accuracy rate. As a result, the proposed modelexceeded the accuracy of individual basic models by using the strengths of various algorithms to improve thegeneralization by reducing the potential weaknesses of individual models. Consequently, the education institutescan easily identify students who may need additional support and interventions to improve their academicperformance.

show abstract

Section: Related Workmentioning

confidence: 80%

“…Author year Method Accuracy [5] 2019 DANN 93 [6] 2020 DM 88.3 [7] 2021 ANN 0.82 [8] 2022 RF, XGBOOST, KNN, SVM 78 [9] 2022 RF, SVM, KNN, LR, NB 86.4 [10] 2023 ANN-LSTM 71.35 [11] 2023 DT 76.19 [12] 2023 DEEP LSTM 84.57 [13] 2023 DT, NB, SVM, NN 87.1 -204 213 [14] 2023…”

Section: Table 2: a Comparison Results With Related Workmentioning

confidence: 99%

Enhancing Student's Performance Classification Using Ensemble Modeling

Nafea,

Mishlish,

Shaban

et al. 2023

Iraqi Journal For Computer Science and Mathematics

View full text Add to dashboard Cite

show abstract

“…Their data included 18 attributes in four dimensions: basic information, interests, classroom behavior, and extracurricular behavior, and had diverse data types. Muhammad Hazique Bin Roslan et al [4] predicted English and mathematics scores of 16-year-old Malaysian students using their past academic performance, demographics, and psychological information. They also explored the main factors that affect students' English and mathematics scores and the relationship between them.…”

Section: Related Workmentioning

confidence: 99%

Multi-Output Based Hybrid Integrated Models for Student Performance Prediction

Xue

Niu

2023

Applied Sciences

View full text Add to dashboard Cite

In higher education, student learning relies increasingly on autonomy. With the rise in blended learning, both online and offline, students need to further improve their online learning effectiveness. Therefore, predicting students’ performance and identifying students who are struggling in real time to intervene is an important way to improve learning outcomes. However, currently, machine learning in grade prediction applications typically only employs a single-output prediction method and has lagging issues. To advance the prediction of time and enhance the predictive attributes, as well as address the aforementioned issues, this study proposes a multi-output hybrid ensemble model that utilizes data from the Superstar Learning Communication Platform (SLCP) to predict grades. Experimental results show that using the first six weeks of SLCP data and the Xgboost model to predict mid-term and final grades meant that accuracy reached 78.37%, which was 3–8% higher than the comparison models. Using the Gdbt model to predict homework and experiment grades, the average mean squared error was 16.76, which is better than the comparison models. This study uses a multi-output hybrid ensemble model to predict how grades can help improve student learning quality and teacher teaching effectiveness.

show abstract

“…The authors proposed that performance can be predicted by classroom activities, midterm scores and tests that can be held quarterly in a year [6]. The authors revealed that students' previous academic records played an important role in performance prediction while demographic and behavioral variables have less impact [7]. The authors also analyzed the current academic grades, attendance and assignment scores of 44 students but the accuracy of the prediction model was not so high [8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Student’s Academic Performance through Data Mining Approach

Hussain,

Akbar,

Hassan

et al. 2024

JIWE

View full text Add to dashboard Cite

The universities and institutes produce a large amount of student data that can be used in a disciplinary way and useful information can be extracted by using an automated approach. Educational Data Mining (EDM) is an emerging discipline used in the educational environment to deal with big student data and extract useful information. The data mining of students’ data can help the At-risk students as well as the stakeholders by the early warning. This study aims to predict the performance of the students based on student-related data to increase the overall performance. In existing studies, insufficient attributes and complexity of network models is a problem. The student’s current records and grades need to be analyzed. In this approach, the Levenberg Marquardt Algorithm (MLA) deep learning algorithm is used. The data consists of the class test, attendance, assignment and midterm scores. The neural network model consists of four input variables, three hidden and one output layer. The performance of the deep neural network is evaluated by accuracy, precision, recall and F1 score. The proposed model gained a higher accuracy of 88.6% than existing studies. The study successfully predicts the student's final grades using current academic records. This research will be beneficial to the students, educators and educational authorities as a whole.

show abstract

Predicting students’ performance in English and Mathematics using data mining techniques

Cited by 11 publications

References 72 publications

Enhancing Student's Performance Classification Using Ensemble Modeling

Enhancing Student's Performance Classification Using Ensemble Modeling

Multi-Output Based Hybrid Integrated Models for Student Performance Prediction

Prediction of Student’s Academic Performance through Data Mining Approach

Contact Info

Product

Resources

About