Predicting Student Academic Performance Via Deep Learning: PyTorch-Based Study on UCI Dataset

Predicting students’ academic performance is a crucial initiative in the field of education, as it allows educators and administrators to spot students who may require extra assistance, customize educational resources to meet individual requirements, and improve overall educational results. Conventional approaches to forecasting academic achievement, such as statistical analysis and expert evaluation, have certain drawbacks in terms of precision and scalability. The emergence of machine learning (ML) methods provides a possible alternative by utilizing extensive datasets and advanced algorithms to reveal patterns and generate more precise predictions. This investigation’s primary goal is to explore the predictive modelling of student academic performance by improving the accuracy of predictions through the utilization of machine learning techniques. The study was conducted utilizing the Python programming environment. The prediction of student academic performance was carried out utilising the Bidirectional long short-term memory (Bi-LSTM) based Weighted Cost Effective Random Forest algorithm. The study utilized the Deep Encoder CNN-Bi-LSTM for optimal feature extraction to foresee student academic performance. The extracted features were then classified using the Weighted Cost Effective Random Forest (WECRF) classifier, and the classification was evaluated in terms of accuracy, precision, specificity, sensitivity, and recall. The issues addressed include the class imbalance, computational complexity, cost, and huge dimensional issue, among others. The random forest method achieved Precision Score - 0.72, Recall Score - 0.68, F1 Score - 0.69, and Accuracy - 0.77 in this study. Moreover, the suggested technique facilitates the automated forecasting and enhancement of students' future academic performance. Keywords: Predictive Modelling; Students; Academic Performance; Machine Learning; Bi-LSTM; CNN-Bi-LSTM; Deep Learning.

The aim of this study is to undertake an empirical inquiry and comparison of the effectiveness of various classifiers with ensembles classifiers in the prediction of student academic performance. A single classifier algorithm will be compared against the performance and efficiency of ensemble classifiers. Reducing student attrition is a major problem for educational institutions all over the world. The search for solutions to increase student retention and graduation rates continues for educators. This is only possible if at-risk students are identified and intervened with as soon as possible. However, the majority of regularly used prediction models are inefficient and inaccurate as a result of inherent classifier limitations and the inclusion of insignificant inputs in their calculations. The majority of data mining and machine learning researcher focused on developing an algorithm that can extract useful information from massive amounts of data after being processed by a computer. The most difficult problem in predictive modelling is identifying the most effective prediction algorithms that are also accurate enough to be useful. Therefore, a multi-level homogeneous ensemble predictive (MLHoEP) model is designed, which uses the different techniques of data mining like feature selection, ensemble learning techniques like boosting and bagging. Seven distinct machine learning algorithms were used on this model to predict and analyse the academic performance of the students. The performance of the classification algorithms in terms of prediction was evaluated using k-fold cross-validation. The study contributes to the body of knowledge by suggesting the development of homogeneous classifiers that may be used to accurately predict students’ academic success. It also proposes the construction of homogeneous classifiers, which may be deployed for accurate student performance prediction, in order to provide a better explanation for the poor performance prediction. As a result of this research, it has been demonstrated that the technique of applying homogeneous ensemble approaches is incredibly efficient and accurate in terms of predicting student performance and assisting in identifying students, who are in danger of dropping out of school. The study compared the accuracy and efficiency of single classifiers to ensembles of classifiers in terms of performance. It was discovered in the research that a homogeneous model with excellent accuracy and efficiency might be developed for anticipating student performance. These key problems have been successfully addressed by the findings of this research study: Which characteristics of students are the most effective predictors of academic performance? How accurate are approaches such as bagging and boosting ensembles for predicting student academic performance? The approach offered in this study will aid educational administrators and policymakers in designing new policies and curriculum-linked to student retention in higher education. This research can also aid in the identification of students who are at risk of dropping out of school early, providing for timely intervention and support. Prospective research will examine the creation and implementation of an automated prediction system known as the students’ academic performance forecast framework, which will collect data from students via online submission and produce a prediction result for their academic performance.KeywordsEducational data miningEnsemble learningMultilayer perceptronRandom forestNaïve BayesCorrelation attribute evaluationInformation gainGain ratio

Accurate prediction of student academic performance plays a vital role in enabling early intervention, adaptive instruction, and data-driven educational decision-making. However, the effectiveness of deep learning models such as Long Short-Term Memory (LSTM) networks is often constrained by suboptimal hyper-parameter tuning and insufficient exploration of the search space. To overcome these limitations, this paper introduces a novel hybrid learning framework, CNAE-LSTM, which integrates Chaotic initialization, a Niche-based evolutionary mechanism, and Alpha Evolution (AE) into a unified optimization paradigm termed Chaotic Niche Alpha Evolution (CNAE). Specifically, a chaotic operator is first employed to enhance population diversity during initialization; then a niche-based grouping strategy partitions the population into three subgroups with distinct mutation probabilities to preserve diversity and avoid premature convergence; finally, an Alpha Evolution mechanism guides the evolutionary search toward promising regions with improved exploration–exploitation balance. The proposed CNAE is utilized to optimize key LSTM hyper-parameters, including network architecture and training settings. Extensive experiments conducted on a real-world secondary education dataset demonstrate that CNAE-LSTM consistently outperforms conventional LSTM, SVM, CNN, Transformer models, as well as LSTM enhanced by QGA, QPSO, CWOA, QGWO, and grid/random search strategies. Results show that CNAE-LSTM achieves superior predictive accuracy, stability, and generalization capability, validating the effectiveness of the proposed hybrid framework for improving academic performance prediction.

Introduction: Educational Data Mining and Machine Learning Models gained more interest among researchers and academicians in recent years. These are used to extract meaningful data from the educational database can be applied to predict the student academic performance. Objective: The main objective of this research shows the capability to predict the student's performance and is highly beneficial to take remedial actions in the present educational system. It also offers suitable student assistance, allows an educational institution or teacher to help the students in gaining extra marks. Methods: The presented model operates in two stages, namely classification and outlier detection. Initially, a multilayer perceptron is employed for data classification. Later, a stochastic gradient descent classifier and multi-layer perceptron are integrated to effectively classify the data. For further improvement in the classification, Student academic performance, outlier detection method namely radial basis function is applied to remove the misclassified instances. Results: The proposed model achieves superior classification performance with the maximum precision of 79.30, recall of 79.30, and accuracy of 79.30, F-score of 79.30 and kappa value of 52.40 respectively. The simulation outcome exhibited that the multilayer perceptron and stochastic gradient descentmodel offers better results over the other classifiers. However, the usage of outlier detection using the radial basis function model takes the classifier results to the next level. Conclusion: Using classification techniques a new student academic performance model is proposed by addressing some issues in the existing model which is used to predict the student academic performance assist inproper time.

In recent years , research evolution in domain of education focus on analytics and which provides insights on students academic performance. The tremendous growth of instructional institutions’ electronic information provides the chance to extract info which will be wont to predict students’ overall success, predict students’ dropout rate, appraise the performance of academics and instructors, improve the learning material in step with students’ desires, and far a lot of. This paper aims to review the latest trends in predicting students’ performance in educational activity. we explain the measures of determinant educational success and highlight the strengths and weaknesses of the foremost common data processing (DM) tools and strategies used today. Moreover, we offer a fashionable literature review of the EDM work that has been revealed during the past years with target the prediction of educational performance in educational activity. we tend to analyze the foremost normally used options and strategies in predicting educational accomplishment, and highlight the advantages of the principally used DM tools in EDM. The results of this paper might assist researchers who are working to hold out EDM solutions within the domain of education as we tend to highlight the type of options that the previous researches found to possess important impact on the prediction, likewise because the edges and downsides of the DM strategies and tools used for predicting educational outcomes.

Educational institutes carried out a huge research to investigate student academic performance and analyses the major factors that may affect student performance. Educational institutes care about how many students will continue their studies successfully and their appreciation. There is a crucial need to investigate and address the main challenges that may affect the student academic performance. In this paper, we propose a new approach to analyses student academic performance using Gradient Boosting algorithms. In particular, the proposed approach implements the XGBoost, CaBoost and LighGBM Gradient Boosting algorithms. The proposed approach consists of two prediction models: the student final status model and student appreciation model. The achieved results show that the CaBoost algorithm gets the best prediction results when it reaches 92.16% for student final status model and 86.89% for student appreciation model. On the other hand, the LightGBM gets the best results in terms of speed.

Increasingly educational providers are being challenged to use their data stores to improve teaching and learning outcomes for their students. A common source of such data is learning management systems which enable providers to manage a virtual platform or space where learning materials and activities can be provided for students to engage with. This study investigated whether data from the learning management system Moodle can be used to predict academic performance of students in a blended learning further education setting. This was achieved by constructing measures of student activity from Moodle logs of further education courses. These were used to predict alphabetic student grade and whether a student would pass or fail the course. A key focus was classifiers that could predict likelihood of failure from data available early in the term. The results showed that classifiers built on all course data predicted student grade moderately well (accuracy= 60.5%, kappa = 0.43) and whether a student would pass or fail very well (accuracy= 92.2%, kappa=0.79). However, classifiers built on the first six weeks of data did not predict failing students well. Classifiers trained on the first ten weeks of data improved significantly on a no-information rate (p<0.008) though more than half of failing students were still misclassified. The evidence indicates that measures of Moodle activity on further education courses could be useful as part of on an early-warning system at ten weeks.

Academic evaluation is an important process to know how well the learning process was conducted and also one of the decisive factors that can determine the quality of the higher education institution. Though it usually curative, the preventive effort is needed by predicting the performance of the student before the semester begin. This effort aimed to reduce the failure rate of the students in certain subjects and make it easier for the PBL tutor to create appropiate learning strategies before the tutorial class begin. The purpose of this work is to find the best data mining technique to predict student academic performance on PBL system between two data mining classification algorithms. This work applied and compared the performance of the classifier models built from Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). After preprocessed the dataset, the classifier models were developed and validated. The result shows that both algorithms were giving good accuracy by 97% and 95,52% respectively though SVM showing the best performance compared to KNN in F-Measure with 80%. The further deployment is needed to integrate the model with academic information system, so that academic evaluation can be easily done.

학생들이 학교교육을 통해 기초학력을 갖추도록 돕는 것은 공교육이 담당해야 할 중요한 책무이다. 기초학력에 도달하지 못한 상태에서 다음 학년에 진입하게 되면 기초학력 미달이 장기화할 수 있고 결과적으로 학업적 실패로 이어질 수 있기 때문이다. 따라서 학생의 미래 성취 수준을 조기에 예측하고 적시에 개입하는 것은 매우 중요하다. 이에 본 연구에서는 개별 학생의 학업성취 수준을 예측하기 위한 방법으로서, 최근 다양한 분야에서 활용되고 있는 순환신경망(RNN)을 국가수준 학업성취도평가 자료에 적용함으로써, 교육 종단 자료를 바탕으로 미래의 학업 성취를 예측할 수 있는 방안을 탐색하였다. 본 연구에서는 2011년 초등 6학년부터 2016년 고등 2학년까지 세 시점의 국가수준 학업성취도평가 자료를 사용하였으며, RNN을 적용하여 미래 시점의 기초 이하 여부를 예측한 결과를 HGLM 모형 적용 결과와 비교하였다. 두 모형은 대체로 유사한 예측 성능을 보였으며, HGLM으로는 결과 산출이 어려운 구조에서도 RNN을 적용하여 기초 이하 여부를 예측할 수 있었다. 본 연구 결과를 토대로 학업성취의 조기예측과 관련한 순환신경망의 장점과 제한점에 대해 제안하였다.

Student academic performance prediction is a crucial topic in Educational Data Mining (EDM) and Learning Analytics, which can be used to help at-risk students by undertaking timely actions. The given paper is a systematic review of machine learning methods used in this field. It analyzes a range of approaches, starting with interpretable models such as Multiple Linear Regression and Decision Trees to ensemble and deep learning high-performance models such as Random Forest and Neural Networks. The review highlights the central role of feature engineering and is discussing predictors of academic and behavioral data, social-economic and psychological conditions. One of the broad implications of this paper is providing a comparative analysis of these methods with an emphasis on the continuing trade-off between predictive accuracy and model inter-pretability. Moreover, the disconnect between theory and real-world, full-stack deployment systems, which are more and more critical when it comes to actual usability, is also critically discussed in this review. Major gaps in the research, such as excessive use of synthetic data, lack of practical testing, and ethics, are determined. Lastly, the paper presents future directions which include the use of Explainable AI (XAI), federated learning in privacy and creation of real-time adaptive feedback systems

Student academic performance prediction is important to improve education with insights into student behavior, learning, and academic achievement by schools. The day-to-day method is non-personalized, and as such, proper learning strategies are challenging to deliver. This work presents a machine learning system for predicting student performance, providing learning recommendations based on personality, and examining student interaction. With learning history, test scores, and personality information, While using machine learning algorithms like Random Forest, Support Vector Machine, Logistic Regression, and K-Nearest Neighbors to obtain the highest rate of prediction. This system goes beyond learning assistance by providing personalized learning recommendations and activity tracking in real-time. The findings of the research show that the method is more accurate than the conventional models, filling the gap between machine learning and adaptive learning. This project is designed to attain the optimal academic assistance and maximize the learning experience of the students.

The ability to predict one's academic performance is a great asset for both the students and the institution administrators. For the students, they can adjust workload, career direction, etc. If they are aware of their capability. For the administrators and instructors, early warnings would facilitate intervention thus enabling a more successful academic environment. In addition, institutional resources can be utilized in an optimal way thus gaining operation efficiency. This work surveys existing literature in student academic performance prediction. Parameters used for predictions are examined. Useful predictors are identified.

Engineering schools worldwide have a relatively high attrition rate. Typically, about 35% of the first-year students in various engineering programs do not make it to the second year. Of the remaining students, quite often they drop out or fail in their second or third year of studies. The purpose of this investigation is to identify the factors that serve as good indicators of whether a student will drop out or fail the program. In order to establish early warning indicators, principal component analysis is used to analyze, in the first instance, first-year engineering student academic records. These performance predictors, if identified, can then be used effectively to formulate corrective action plans to improve the attrition rate.

Along with the spreading of online education, the importance of active support of students involved in online learning processes has grown. The application of artificial intelligence in education allows instructors to analyze data extracted from university servers, identify patterns of student behavior and develop interventions for struggling students. This study used student data stored in a Moodle server and predicted student success in course, based on four learning activities -communication via emails, collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to predict student performance on a blended learning course environment. The model predicted the performance of students with correct classification rate, CCR, of 98.3%.

The primary goal of upper education foundations is to offer quality instruction to its understudies. One approach to accomplish most elevated level of import during advanced education framework is by finding learning for forecast with reference to enrollment of understudies in a specific course, estrangement of typical study hall showing model, discovery of out of line means that used in on-line assessment, identification of surprising qualities within the outcome sheets of the understudies, expectation concerning understudies' exhibition, etc. the educational is tucked away among the instructive infoal index and it's removable through information mining ways. This paper is intended to let the limits of data mining methodologies in setting of cutting edge training by giving a data mining model to higher education framework inside institute. During this exploration, the classification and prediction tasks are employed to assess understudy’s presentation and as there are various methodologies that are utilized; decision tree, clustering and neural network techniques are used here.