A student performance prediction model based on multimodal generative adversarial networks

To analyse the student's academic performance, a new prediction model is developed. This proposed model collects the student's data from standard online sources. At first, these gathered data are pre-processed by certain methods like outlier removal and filling. Then, the filtered data undergoes feature mining by developing the entropy weighted fuzzy rough set mining (EW-FRSM), where certain parameters are tuned with the proposed hybrid optimisation algorithm with probability-based coyote electric fish optimisation (P-CEFO). The refined features are utilised for performance prediction, where the deep belief-Bi-LSTM (DB-Bi-LSTM) is used for student performance prediction. The parameters in DBN and Bi-LSTM are tuned with the same P-CEFO algorithm to improve the prediction performance. Simulation is performed to examine the efficacy of the designed prediction method with diverse performance metrics by evaluating with diverse baseline approaches to prove the usefulness of the model.

Performance prediction models have been proposed countless times due to the benefits that they can provide to educational stakeholders. While many factors have been taken into account when predicting student performance, teachers’ assessment or observation reports have not been commonly used. A teacher’s assessment is a fundamental part of the educational process and has a direct impact on students’ success. In this study, we analyze the topics, and psychological features in teachers’ daily written reports and apply them to the student performance prediction model. Experimental results show the capability of this approach in contributing to the accuracy of performance prediction models. KeywordsTeacher reportsTopic modellingSentiment analysisPerformance predictionText miningMachine 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

Student performance prediction is one of the educational data mining tasks that has received great deal of attentions. It enables the educators to improve the quality and effectiveness of classroom management and to help students achieve better academic performance. However, one of the major challenges in developing a prediction model in a traditional classroom setting is the limited amount of information available to be used as student attributes. In this research, we focus on developing a performance prediction models in information-lacking environment for identifying at-risk students who have tendency to receive low grades. To construct the models, five algorithms with different learning paradigms were investigated. The constructed models were evaluated on 5 different subjects. The results have shown that even though the prediction performances for the subjects that less depended on acquirable previous performance information were quite low, the performances for the subjects offered during higher year of the educational program were quite promising.

Higher educational institutions (HEIs) are striving to improve their student's performance. Classification of students to predict student's performance could be useful in identifying bad performers or late completers to improve at an early stage by forecasting their final outcome. In this study, three student's performance prediction models were investigated. J48 decision tree algorithm was used for the study. When the performance of these models was evaluated, it was found that the third prediction model provided better performance compared to the other models. Also, there was variation in the course-taking patterns of students and contextual attribute course difficulty depending on their completion time. This demonstrates the significance of students course-taking patterns on performance and time-to-degree. The most significant finding is that unlike easy course delivering high GPAs interestingly some very difficult courses too lead to optimum time and GPA which could be attributed to other hidden factors which need to be investigated.

Student performance modelling is one of the challenging and popular research topics in educational data mining (EDM). Multiple factors influence the performance in non-linear ways; thus making this field more attractive to the researchers. The widespread availability of e ducational datasets further catalyse this interestingness, especially in online learning. Although several EDM surveys are available in the literature, we could find only a few specific surveys on student performance analysis and prediction. These specific surveys are limited in nature and primarily focus on studies that try to identify possible predictor or model student performance. However, the previous works do not address the temporal aspect of prediction. Moreover, we could not find any such specific survey which focuses only on classroom-based education. In this paper, we present a systematic review of EDM studies on student performance in classroom learning. It focuses on identifying the predictors, methods used for such identification, time and aim of prediction. It is significantly the first systematic survey of EDM studies that consider only classroom learning and focuses on the temporal aspect as well. This paper presents a review of 140 studies in this area. The meta-analysis indicates that the researchers achieve significant prediction efficiency during the tenure of the course. However, performance prediction before course commencement needs special attention.

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The students’ performance prediction (SPP) problem is a challenging problem that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning educational systems, and other resources is not a simple task. Even after collecting data, we might face imbalanced data, missing data, biased data, and different data types such as strings, numbers, and letters. One of the most common challenges in this area is the large number of attributes (features). Determining the highly valuable features is needed to improve the overall students’ performance. This paper proposes an evolutionary-based SPP model utilizing an enhanced form of the Whale Optimization Algorithm (EWOA) as a wrapper feature selection to keep the most informative features and enhance the prediction quality. The proposed EWOA combines the Whale Optimization Algorithm (WOA) with Sine Cosine Algorithm (SCA) and Logistic Chaotic Map (LCM) to improve the overall performance of WOA. The SCA will empower the exploitation process inside WOA and minimize the probability of being stuck in local optima. The main idea is to enhance the worst half of the population in WOA using SCA. Besides, LCM strategy is employed to control the population diversity and improve the exploration process. As such, we handled the imbalanced data using the Adaptive Synthetic (ADASYN) sampling technique and converting WOA to binary variant employing transfer functions (TFs) that belong to different families (S-shaped and V-shaped). Two real educational datasets are used, and five different classifiers are employed: the Decision Trees (DT), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and LogitBoost (LB). The obtained results show that the LDA classifier is the most reliable classifier with both datasets. In addition, the proposed EWOA outperforms other methods in the literature as wrapper feature selection with selected transfer functions.

Forecasting student performance with precision in the educational space is paramount for creating tailor-made interventions capable to boost learning effectiveness. It means most of the traditional student performance prediction models have difficulty in dealing with multi-dimensional academic data, can cause sub-optimal classification and generate a simple generalized insight. To address these challenges of the existing system, in this research we propose a new model Multi-dimensional Student Performance Prediction Model (MSPP) that is inspired by advanced data preprocessing and feature engineering techniques using deep learning. We developed a method that targets the common issues associated with educational datasets over imbalanced and temporal settings which is also explainable through AI features. Moreover, through adaptive hyper-parameter tuning and advanced graph neural network layers in the MSPP model allow to make output more dense representation for predictions resulting more accurate classification. The experiments results show that MSPP outperforms the other EAI&ML, MTSDA, XAI, DGNN and DLM with high accuracy 76 %, precision score of 0.79 and macro F1-score of 0.73. The model also helps to bring down the False Positive Rate (FPR) substantially at a 0.15 level, which ensures more reliable predictions for student classification.•The model of the MSPP includes contextual information and multi-layered analysis in order to improve prediction accuracy, placing a sound basis for predicting students in different performance categories such as distinction, pass, fail or withdrawn.•Our approach is obviously to generalize and extract those sparse, heterogeneous academic data in the form of structured training record using domain specific preprocessing integrating with multi-class classification mechanisms that improves on precision-recall across multiple categories.

Modeling student learning and further predicting the performance is a well-established task in online learning and is crucial to personalized education by recommending different learning resources to different students based on their needs. Interactive online question pools (e.g., educational game platforms), an important component of online education, have become increasingly popular in recent years. However, most existing work on student performance prediction targets at online learning platforms with a well-structured curriculum, predefined question order and accurate knowledge tags provided by domain experts. It remains unclear how to conduct student performance prediction in interactive online question pools without such well-organized question orders or knowledge tags by experts. In this paper, we propose a novel approach to boost student performance prediction in interactive online question pools by further considering student interaction features and the similarity between questions. Specifically, we introduce new features (e.g., think time, first attempt, and first drag-and-drop) based on student mouse movement trajectories to delineate students' problem-solving details. In addition, heterogeneous information network is applied to integrating students' historical problem-solving information on similar questions, enhancing student performance predictions on a new question. We evaluate the proposed approach on the dataset from a real-world interactive question pool using four typical machine learning models. The result shows that our approach can achieve a much higher accuracy for student performance prediction in interactive online question pools than the traditional way of only using the statistical features (e.g., students' historical question scores) in various models. We further discuss the performance consistency of our approach across different prediction models and question classes, as well as the importance of the proposed interaction features in detail. © 2020 Association for Computing Machinery. © 2020 Association for Computing Machinery.

Educational data mining (EDM) is a strategic technique for exploring data in educational environments to gain a deeper understanding of education. One of the goals of EDM is to predict things related to students in the future which can be done using a machine learning approach. In this paper, a regression model is developed to predict student performance in the first semester and the waiting period for graduate employment using machine learning approach based on informatics management (MI) and non-informatics management (non-MI) student data. Four regression models are compared for predicting student performance in the first semester and waiting period for graduate employment, including support vector regression (SVR), random forest regression (RFR), AdaBoost regression (ABR), and XGBoost regression. Based on the experiment, prediction of students' performance in the first semester, the highest R2 result produced by SVR model by value of 0.58 for MI and by RFR by value of 0.34 for non-MI. While, waiting period for graduate employment prediction, the highest R2 result produced by AdaBoost regression by value of 0.44 for MI and SVR by value of 0.39 for non-MI.

Vocational education increasingly adopts blended learning approaches, combining online and traditional instruction to enhance skill development and learning outcomes. As students engage in various digital activities, they generate large volumes of behavioral data that can be leveraged to predict academic performance and enable early intervention for at-risk learners. This research proposes a deep learning-based student performance prediction framework tailored to vocational education environments, utilizing multi-source behavioral data. Data were collected from 2000 vocational learners enrolled in a semester-long program. The framework utilizes multi-source behavioral data, including Learning Management System (LMS) activity logs, internet usage patterns, attendance records, assessments, and demographic information, to enhance predictive accuracy and support early intervention in vocational education. Data were preprocessed by handling missing values, data cleaning, and min-max normalization. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were fused to extract dynamic behavioral features. The proposed model, the Seven-spot Ladybird Optimized Multi-Source Attention-based Gated Recurrent Units (SLO-MSAGRU) model, is applied to predict student performance in vocational education. Implemented in Python, the findings show that the SLO-MSAGRU approach performs better than multimodal baseline architectures, achieving superior results with accuracy, F1-score, recall, and precision ranging from 95% to 98%. These findings reveal that broader internet behavior patterns contribute more significantly to student performance prediction than platform-specific learning data. The framework offers a practical tool for educators to apply personalized learning strategies and timely interventions, thereby improving decision-making in vocational training environments.

<p class="MsoNormal" style="text-align: justify;"><span style="font-family: Times New Roman;">This paper explores machine learning approaches to predicting student performance using artificial neural networks. By employing educational data mining and predictive modeling techniques, accurate predictions of student outcomes were achieved. The results indicate that artificial neural networks exhibit high accuracy and reliability in forecasting student academic performance. Through comprehensive analysis and empirical testing, this approach significantly enhances the effectiveness of student performance predictions. Future research directions may include further optimization of the model's algorithms and expansion of the data sample size to improve prediction accuracy and applicability. The method demonstrated exceptional performance in predicting student outcomes, offering high accuracy and efficacy. By mining and analyzing extensive educational data, a predictive model was established and validated through experiments. We introduce a novel predictive model to the field of education, providing robust support for student learning and educational decision-making. Future enhancements can optimize the model, increase prediction precision, and expand application fields to better serve the development of educational endeavors.</span></p>

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