Prediction Of Student Academic Performance Research Articles

An Efficient Hybrid Deep Learning Framework for Predicting Student Academic Performance

An Efficient Hybrid Deep Learning Framework for Predicting Student Academic Performance

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.

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

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

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.

Enhanced Machine Learning model for Student Academic Performance Prediction using Genetic algorithm and compare with CNN model

Enhanced Machine Learning model for Student Academic Performance Prediction using Genetic algorithm and compare with CNN model

Prediction of student academic performance based on their emotional wellbeing and interaction on various e-learning platforms

Prediction of student academic performance based on their emotional wellbeing and interaction on various e-learning platforms

ProbSAP: A comprehensive and high-performance system for student academic performance prediction

The student academic performance prediction is becoming an indispensable service in the computer supported intelligent education system. But conventional machine learning-based methods can only exploit the sparse discriminative features of student behaviors in imbalanced academic datasets to predict student academic performance (SAP). Furthermore, there is a lack of imbalanced data processing mechanisms that can efficiently capture student characteristics and achievement. Therefore, we propose a comprehensive and high-performance prediction framework to probe SAP characteristics (ProbSAP) on massive educational data, which can resolve imbalanced data issue and improve academic prediction performance for making course final mark prediction. It consists of three main components: collaborative data processing module for enhancing the data quality, scalable metadata clustering module for alleviating the imbalance of academic features, and XGBoost-enhanced SAP prediction module for academic performance forecasting. The collaborative data processing module integrates multi-dimensional academic data, which sustains a good supply for clustering and modeling in the ProbSAP framework. The comparative evaluation results demonstrate that ProbSAP delivers superior accuracy and efficiency improvement for the course final mark prediction of college students over other state-of-the-art methods such as CNN, SVR, RFR, XGBoost, Catboost-SHAP, and AS-SAN. On average, ProbSAP reduces the mean absolute error (MAE) by 84.76%, 72.11%, and 66.49% compared with XGBoost, Catboost-SHAP, and AS-SAN, respectively. It also leads to a better out-sample fit that minimizes prediction errors between 1% and 9% with over 98% of actual samples.

An Enhanced Student Engagement and Academic Performance Predictive System

Student engagement is one of the important constructs that is used to understand the behavior of the student towards the teaching-learning process, and it determines the students’ academic performance. The aim of this study is for predictive analytics to work on the comprehension of student commitment and scholarly execution and anticipate students who are in danger of low execution or commitment right on time before the evaluation to work with conceivable mediation to further develop the learning results in advanced education. This research adopts the process of machine learning such as linear regression, decision tree, naïve Bayes, KNN, Kmeans in order to identify the most effective determinants for student academic performance prediction. The result of this study shows that after testing the five attributes, we discovered so far that the attributes that has impact on student evaluation are their Race/Ethnicity and Parental level of Education. Thus, the early prediction of student performance can trigger educators to track student dropouts in a particular course at an early stage. The model can also be used as an early warning system to identify failure students in the classroom by the course coordinators and educators, to take strategic decisions to improve student performance.

Research on Student Academic Performance Prediction Methods

Student academic performance prediction can not only detect students' academic problems in advance, but also optimize teaching methods and provide students with personalized teaching methods, considering the complex relationship between academic performance and other factors, this paper uses linear regression and random forest to predict student academic performance.

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

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.

Metaheuristics Method for Classification and Prediction of Student Performance Using Machine Learning Predictors

Over the last few decades, there has been a gradual deterioration in higher education in all three areas: the academic setting (both staff and students), as well as research and development output (including graduates). All colleges and universities are essentially focused on improving management decision-making and educating pupils. High-quality higher education can be obtained through a variety of methods. One method is to accurately forecast pupils’ achievement in their chosen educational context. There are numerous prediction models from which to pick. While it is unclear whether there are any markers that can predict whether a kid will be an academic genius, a dropout, or an average performer, the researcher reports student achievement. This article presents a metaheuristics and machine learning-based method for the classification and prediction of student performance. Firstly, features are selected using a relief algorithm. Machine learning classifiers such as BPNN, RF, and NB are used to classify student academic performance data. BPNN is having better accuracy for classification and prediction of student academic performance.

Multi-view hypergraph neural networks for student academic performance prediction

Academic performance prediction is a fundamental and hot issue in educational data mining (EDM). Recently, researchers have proposed a series of effective machine learning (ML) based classification strategies to predict students’ academic performance. However, prior arts are typically concerned about individual models but neglect the association among students, which might considerably have an effect on the integrity of the academic performance-related representations. Meanwhile, students’ multi-viewing behavior contains complex relations among students. Therefore, we propose a Multi-View Hypergraph Neural Network (MVHGNN) for predicting students’ academic performance. MVHGNN uses hypergraphs to construct high-order relations among students. The semantic information implied by multiple behaviors is consolidated through meta-paths. Further, a Cascade Attention Transformer (CAT) module is introduced to mine the weight of different behaviors by the self-attention mechanism. Our method is evaluated on real campus student behavioral datasets. The experimental results demonstrate that our method outperforms the state-of-the-art ones.

Enhancing the Learning Experience of Students by Early Prediction of Student Academic Performance using Machine Learning

Machine learning (ML) is expected to provide a variety of ways and effective tools to improve education in general in the future. Digital data tracks from different sources covering various aspects of students' lives are stored daily on most modern university and college campuses. However, it is very challenging to get a complete view of a student and combine that data, use this data to accurately predict a student's academic performance, and use such predictions to enhance students' positive engagement with universities or colleges. First of all, data from previous year’s students (name, year, department, category, mark, etc.) such as academic performance and behavioral measurements are entered using online forms. Subsequent features were extracted and machine learning-based training was provided and machine learning-based taxonomy algorithms will be developed to predict educational performance. Based on the accuracy obtained through the analyzed and tested Machine Learning algorithms, will provide a set of recommendations for teachers to improve students' quality and learning ability.