Prediction Of Student Academic Performance Research Articles

Empowering education: Harnessing ensemble machine learning approach and ACO-DT classifier for early student academic performance prediction

Empowering education: Harnessing ensemble machine learning approach and ACO-DT classifier for early student academic performance prediction

Sustainable Educational Data Mining Studies: Identifying Key Factors and Techniques for Predicting Student Academic Performance

Sustainable Educational Data Mining Studies: Identifying Key Factors and Techniques for Predicting Student Academic Performance

A Multi-View Deep Learning Approach for Enhanced Student Academic Performance Prediction

A Multi-View Deep Learning Approach for Enhanced Student Academic Performance Prediction

Predicting Student Academic Performance Using Neural Networks: Analyzing the Impact of Transfer Functions, Momentum and Learning Rate

Artificial Neural Networks (ANN) demonstrate a compelling application of AI in predicting student performance, a critical aspect for both students and educators. Accurate forecasting of student achievements enables educators to monitor progress effectively, allowing educational institutions to optimize outcomes and improve student results. This study focuses on leveraging ANN for predictive analytics in student performance. Through a detailed evaluation of transfer functions, optimizers, learning rates, and momentum values, the model achieves an impressive 98% accuracy with specific configurations: a learning rate of 0.005, momentum of 0.7, Sigmoid transfer function, and SGD optimizer. Additionally, the study performs a comparative analysis of various Machine Learning Algorithms, including ANN, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees (DT), Naive Bayes (NB), and Logistic Regression (LR). Using data from 689 B.Tech students at IP University, the analysis reveals that ANN outperforms other algorithms with an accuracy of 97%. This high accuracy demonstrates the potential of ANN in educational settings, providing a valuable tool for educators to enhance student performance and outcomes.

Online Student Monitoring and Evaluation System using Apriori Algorithm for Predicting Student Academic Performance

The study investigates the current student monitoring and evaluation system of Saint Michael College of Caraga with the goal of easy tracking of student academic performance and progression. The research employs a mixed-methods approach through the conduct of interview and utilizing online survey questionnaire. The perspectives of young female students regarding the current system and their suggestions for improvement were examined and analyzed using descriptive statistical method such as frequency distribution, weighted mean score, and standard deviation. The key findings indicate a generally positive perception of the existing system but highlight areas for improvement including addressing technical issues, accommodating diverse learning styles, ensuring system security and privacy, and providing personalized support to students. By engaging stakeholders and incorporating diverse viewpoints from the respondents, the research output contributes to the development of a robust monitoring and evaluation system capable of optimizing academic performance assessment and enriching the educational experience. The integration of Apriori Algorithm fosters informed decision-making for attaining student’s academic success

Predicting performance of students by optimizing tree components of random forest using genetic algorithm

Prediction of student academic performance is still a problem because of the limitations of the existing methods specifically low generalizability and lack of interpretability. This study suggests a new approach that deals with the current problems and provides more reliable predictions. The proposed approach combines the information gain (IG) and Laplacian score (LS) for feature selection. In this feature selection scheme, combination of IG and LS is used for ranking features and then, Sequential Forward Selection mechanism is used for determining the most relevant indicators. Also, combination of random forest algorithm with a genetic algorithm for is introduced for multi-class classification. This approach strives to attain more accuracy and reliability than current techniques. The case study shows the proposed strategy can predict performance of students with average accuracy of 93.11 % which shows a minimum improvement of 2.25 % compared to the baseline methods. The findings were further confirmed by the analysis of different evaluation metrics (Accuracy, Precision, Recall, F-Measure) to prove the efficiency of the proposed mechanism.

Data Analysis of Student Academic Performance and Prediction of Student Academic Performance Based on Machine Learning Algorithms

With the development and popularization of education, the quality of education has become one of the key factors in the development of a country. And students' academic performance, as one of the important indicators of education quality, has been attracting much attention. This paper mines and analyzes the data affecting students' academic performance, and also conducts a predictive study of students' academic performance using logistic regression model. In this study, 30 indicators such as gender, age, family size, parental education, parental occupation, family relationship, health, and the number of drinks per this paperek and per month this paperre used as input variables, and students' academic performance was categorized into SUCCESS and FAIL, and the training and test sets this paperre divided according to the ratio of 7:3, and the logistic regression model was used for training and prediction. The results show that the logistic regression model has high prediction accuracy in predicting students' academic performance (whether they fail or not), with an accuracy of 95.8%, precision of 96.7%, recall of 95.1%, and F1 of 95.8%. This indicates that the logistic regression model has high accuracy and reliability in predicting students' academic performance. The results of this study are important for schools and educational organizations. Through the prediction of students' academic performance, schools can identify students' learning problems in time and take targeted measures to help students improve their academic performance. Meanwhile, this study also provides some useful reference information for individual students to help them better understand their learning situation, adjust their learning strategies in time and improve their learning efficiency. In the future, the method can be further explored and improved to enhance the accuracy and reliability of the prediction and to provide better support and assistance for students' learning and development.

Bibliometric insights into data mining in education research: A decade in review

This bibliometric study on data mining in education synonymous with big educational data utilizes VOSviewer and Harzing’s Publish and Perish to analyze the metadata of 1,439 journal articles found in Scopus from 2010 to 2022. As bibliometric analyses in this field are lacking, this study aims to provide a comprehensive outlook on the current developments and impact of research in this field. This study employs descriptive and trends analysis, co-authorship analysis, co-citation analysis, co-occurrences of keywords, terms map analysis, and analysis of the impact and performance of publications. It also partially replicates a similar study conducted by Wang et al. (2022), who used the Web of Science (WoS) database. The study is reported in an article entitled ‘Big data and data mining in education: A bibliometrics study from 2010 to 2022’. Results show that data mining in education is a growing research field. There is also a significant difference between the publications in Scopus and WoS. The study found several research areas and topics, such as student academic performance prediction, e-learning, machine learning, and innovative data mining techniques, to be the core basis for collaborating and continuing current research in this field. These results highlight the importance of continuing research on data mining in education, guiding future research in tackling educational challenges.

An Intelligent Analytic Framework for Predicting Students Academic Performance Using Multiple Linear Regression and Random Forest

In the contemporary educational landscape, data-driven decision-making has become pivotal for enhancing student success. This article explores an intelligent analytic framework leveraging Multiple Linear Regression (MLR) and Random Forest (RF) algorithms to predict student performance, providing a comparative analysis of their predictive capabilities. MLR, a statistical technique, models the relationship between students' grades and various factors such as attendance and socio-economic background, offering transparency and interpretability of the impact of each predictor. RF, an ensemble learning method, excels in handling large datasets and capturing non-linear interactions among variables, offering higher accuracy in prediction. The study was conducted using 664 datasets from eight departments of Federal Polytechnic Ukana, following rigorous data preprocessing and normalization. The performance of both models was evaluated based on metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), R-squared Score (R²), and Explained Variance Score (EVS). The results revealed that RF outperformed MLR significantly, with lower error rates and higher predictive accuracy. Scatter plots and bar charts further illustrated the robust performance of RF over MLR. This research underscores the potential of integrating advanced machine learning techniques in educational settings to provide deeper insights into student performance, enabling timely and targeted interventions. The findings advocate for the adoption of RF for more accurate predictions and improved educational outcomes. Future research should explore hybrid models and expand the dataset to validate the applicability of these findings across diverse educational contexts.

Student academic performance prediction enhancement using t-SIDSBO and Triple Voter Network

Student academic performance prediction enhancement using t-SIDSBO and Triple Voter Network

An Efficient Hybrid Deep Learning Framework for Predicting Student Academic Performance

Introduction: educational data analysis with data mining techniques for enhanced learning is increasing. Voluminous data available through institutions, online educational resources and virtual educational courses could be useful in tracking learning patterns of students. Data mining techniques could be helpful for predicting students’ academic performance from raw data. Conventional Machine Learning (ML) techniques have so far been widely used for predicting this. Methods: however, research available on the Convolutional Neural Networks (CNNs) architecture is very scarce in the context of the academic domain. Therefore, in this work a hybrid CNN model involving 2 different CNN models for forecasting academic performance. The one-dimensional data is converted into two-dimensional equivalent to determine efficiency of the hybrid model which is subsequently compared with many existing. Result: the experimental results are evaluated using various performance metrics like precision, accuracy, recall and F-Score. Conclusion: the proposed hybrid model outperforms-Nearest Neighbour (K-NN), Decision Trees (DTs), and Artificial Neural Network (ANN) in terms of precision, accuracy, recall and F-Score

Quantum support vector machine: a novel approach for predicting students academic performance in higher education

Quantum support vector machine: a novel approach for predicting students academic performance in higher education

Predicting Student Academic Performance Based on Psychological Test using Machine Learning

It is essential to consider the psychological aspect of selecting new students to determine the success of prospective students. The psychological aspect is measured by a psychological test that shows the level of prospective students' abilities in social, emotional, personality, and potential to live at university. This paper proposes an approach to predicting student performance based on their psychological test scores using the Decision Tree and Random Forest algorithms. The dataset used in this study was taken from the student academic record at Institut Teknologi Del, which includes years of psychological test scores and the Grade Point Average (GPA) from studying at the Institute. More specifically, the dataset used includes the 2019, 2020, and 2021 class years. However, there are gaps in the dataset used, including missing values and psychological test attributes such as TIU, TIU Category, Work Achievement, Work Tempo, Accuracy, and Consistency, which are unavailable in other datasets. This is shown in the correlation heatmap, which shows the level of correlation for each attribute, which is still classified as a very weak correlation. Therefore, we came up with two approaches. The first approach is to use as many records as possible (Analysis on records), and the opposite of the second is to take advantage of more features (Analysis based on features). The two approaches are compared to determine which performs better for the classification model. Our results show that studies that emphasize the use of records produce slightly better performance than analyses that emphasize features. In more detail, the random forest algorithm produces the best performance compared to the decision tree algorithm in each Analysis, the RMSE value is 0.4552, and the MAE value is 0.3514. Moreover, none of the psychological test attributes strongly correlate to GPA and hence do not guarantee student performance.

A Systematic Literature Review on Predicting Students Academic Performance By Using Data Mining Techniques

A Systematic Literature Review on Predicting Students Academic Performance By Using Data Mining Techniques

Early prediction of Student academic performance based on Machine Learning algorithms: A case study of bachelor’s degree students in KSA

Early prediction of Student academic performance based on Machine Learning algorithms: A case study of bachelor’s degree students in KSA

Dimensionality Reduction using Feature Selection Techniques on EDM for Student Academic Performance Prediction

The aim of this study is to perform dimensionality reduction on student datasets using different feature selection strategies in order to better forecast students' academic progress and minimize high-dimensionality concerns. The effectiveness of models built utilizing classification algorithms and pertinent features chosen using different feature selection strategies has been experimentally evaluated in this study. Nine feature selection methods, including Chi-Square, Pearson Correlation, Variance Threshold, Feature Importance, Recursive Feature Elimination (RFE), Lasso, Ridge, Random Forest, and XGBoost, as well as one classification method, logistic regression, were applied to the student dataset to determine the results of the analysis. In this experiment, dimensionality reduction techniques were used to reduce the number of features from 70 to the optimal 21 feature subsets for analysis of student academic performance prediction. Five measures were used to assess the effectiveness of the techniques: r2, precision, recall, f-score, and accuracy. With the logistic regression classifier, the feature subset chosen by the chi-square had the highest accuracy of 72.4%, precision of 71.4%, recall of 72.4%, f-score of 70%, and r-square of -0.221%.

K Nearest Neighbour and Improved Artificial Neural Network Techiniques for Student Academic Performance Prediction

Educational Data Mining has been an emerging topic nowadays due to the growth of educational data. This field makes it possible to develop methods in order to find out hidden patterns from educational data. The methods extracted from Educational Data Mining discipline are then used to understand students including their learning behavior as well as to predict their academic performance. In recent work used artificial neural network to predict academic performance. However in recent work does not used any proper method for missing value imputation and this will affect prediction performance. To mitigate the above mentioned issue in this work introduced an improved framework for student academic performance prediction. In which pre-processing is done by using data formatting, K Nearest Neighbour (KNN) based missing data imputation, min max normalization based data normalization and data filtering methods. Once the data is enhanced using pre-processing, it will be send to the classifier for Student academic performance prediction which is done by using Improved Artificial Neural Network (IANN) method. Experimental results demonstrate the effectiveness of the proposed model interms of precision, recall and f-measure.

SELF-LEARNING MODULE (SLM) DIMENSIONS AND STUDY HABITS AS PREDICTORS OF ACADEMIC PERFORMANCE OF STUDENTS IN MATHEMATICS

This study determined whether self-learning module (SLM) dimensions and study habits significantly predict students academic performance in mathematics. It utilized the descriptive correlational design method of research. There were 337 Grade 10 students selected through stratified random sampling from four public secondary schools of New Corella District in the Division of Davao del Norte. Three sets of research instruments were used in this study; one was a modified survey instrument to determine the level of SLM dimensions, another was an adapted instrument to determine the level of study habits, and a modified test instrument to determine the academic performance of students in mathematics. The Statistical tools which were applied were mean, Pearson r, and regression analysis. The findings suggested that the SLM dimensions meet learners needs on almost all occasions while their study habits were manifested on most occasions. Moreover, the academic performance of students in mathematics was moderately proficient. Accordingly, there was a significant relationship between SLM dimensions and academic performance. However, the data is sufficient, yet it proved the claim that there is no relationship between study habits and academic performance. Therefore, SLM dimensions significantly predicted students academic performance in mathematics, while study habits did not significantly predict students academic performance in mathematics. Teachers and school administrators are encouraged to administer effective learning interventions by promoting students good study habits while implementing SLM. KEYWORDS: Math education, self-learning module dimensions, study habits, academic performance, grade 10 students, descriptive and correlational design, regression analysis, New Corella District, Davao del Norte, Philippines

Development of an Academic Performance Monitoring System Using Least Squares Regression for Predicting Student Academic Performance Status on Professional Courses

This study deals with the development of an academic performance monitoring system using least squares regression to predict a student’s academic performance status on professional courses. The proposed system monitors the student’s academic performance to promote education and achieve learning outcomes. The study aims to design and create a Graphical User Interface (GUI) and its functions, to utilize MATLAB for forecasting and a least squares regression model to show and predict the relationship between two factors, and to evaluate the system’s predicting accuracy using Percentage Forecast Error (PFE) and Mean Absolute Percent Error (MAPE) to compare actual and forecasted data for every semester. The sample size includes 182 students of BS Information Technology, of whom thirteen (13) were successfully chosen using simple random sampling with their GPAs for the purpose of predicting future academic performance.

Forecasting Student Academic Performance Using Neuro-Fuzzy Model

To achieve objective and precise forecasting of students’ academic performance while keeping up with technological improvements and changes, this paper discusses a neuro-fuzzy technique for the prediction of student academic performance. The technique comprises a Neural Network (NN), Fuzzy Logic (FL), Knowledge Base (KB) and Rule Base (RB) subcomponents. The NN subcomponent uses a three-layered feed-forward architecture to acquire a large collection of interconnected units in some pattern for communications. The FL subcomponent is an extension of Boolean logic and it was used for establishing accurate selection processes and precise solutions to multivariable problems. The KB component forms the database of multi-level information while the RB comprises a set of if-then statements for decision-making. The inference engine subcomponent applies a pre-defined procedure on input from the rule base and fuzzy logic interfaces for final prediction. The proposed technique performs predefined procedures that are based on some input sets that store multi-level information derived from several pre-specified scores. Results from the implementation of the technique established its practical function.