Student success rate is a significant indicator of the quality of the educational services offered at higher education institutions (HEIs). It allows students to make their plans to achieve the set goals and helps teachers to identify the at-risk students and make timely interventions. University decision-makers need reliable data on student success rates to formulate specific and coherent decisions to improve students’ academic performance. In recent years, EDM has become an effective tool for exploring data from student activities to predict their final grades. This study presents a case study for predicting the students’ final grades based on their activities in Moodle Learning Management System (LMS) and attendance in online lectures conducted via Zoom by applying statistical and machine learning techniques. The data set consists of the final grades for 105 students who study Object-Oriented Programming at the University of Plovdiv during the 2021–2022 year, data for their activities in the online course (7057 records), and attendance to lectures (738). The predictions are based on 46 attributes. The Chi-square test is utilized to assess the association between students’ final grades and event context (lectures, source code, exercise, and assignment) and the relationships between attendance at lectures and final results. The logistic regression model is utilized to assess the actual impact of event context on “Fail” students in a multivariate setup. Four machine learning algorithms (Random Forest, XGBoost, KNN, and SVM) are applied using 70% of training data and 30% of test data to predict the students’ final grades. Five-fold cross validation was also utilized. The results show correlations between the students’ final grades and their activity in the online course and between students’ final grades and attendance at lectures. All applied machine learning algorithms performed moderately well predicting the students’ final results, as the Random Forest algorithm obtained the highest prediction accuracy—78%. The findings of the study clearly show that the Random Forest algorithm may be used to predict which students will fail after eight weeks. Such data-driven predictions are significant for teachers and decision-makers and allow them to take measures to reduce the number of failed students and identify which types of learning resources or student activities are better predictors of the student’s academic performance.

Introduction: Educational Data Mining (EDM) involves analysing educational data to identify patterns and trends. By uncovering these insights, educators can better understand student learning, optimise teaching methods, and refine curriculum. One of the main tasks in educational data mining is predicting the student’s academic performance because it makes it possible to provide appropriate interventions supporting students’ achievements. Predicting the student’s academic performance also helps to identify at-risk students and explore the possibility of providing intervention techniques.Methods: In this paper, a deep learning model using a Bi-LSTM network is introduced to predict second term GPA.Results: The model had an average accuracy of 88.23% and was statistically better than traditional machine learning algorithms such as CatBoost, XGBoost, Hist Gradient Boosting, and LightGBM for accuracy, precision, recall, or F1-score metrics. The results are also analysed with the help of SHAP values for model interpretability to understand feature contributions, making the proposed framework more transparent. The performance of models is also compared using various statistical tests.Discussion: The results demonstrate that BI-LSTM performance is significantly different from other models. Hence, the proposed model provides a way to prevent student dropouts and improve academic achievements.

This systematic review comprehensively examines the application and impacts of Educational Data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations.

With the increasing use of Virtual Learning Environments (VLEs), student performance prediction has become a necessary task to better academic outcomes, especially for disabled students who experience specific constraints. Machine learning (ML) models like Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN) have shown high predictive accuracy. However, these models often lack interpretability and making it difficult for educators to understand the key factors influencing student success. In addition, studies only concentrate on general student populations and do not incorporate complete demographic and behavioural data in their analyses, making it difficult to address the needs of disabled students. To address these challenges, this study proposed a novel approach by combining Long Short-Term Memory (LSTM) networks with SHapley Additive exPlanations (SHAP). LSTM captured the temporal dependencies in student interaction data, and SHAP make the model interpretable by identifying the most influential features responsible for achieving good learning performance. The proposed system allows for prediction accuracy and transparent decision making, thereby facilitating targeted interventions for disabled students. The proposed model is tested on the OULAD dataset that contains demographic, engagement, and assessment-related information. The pre-processing techniques are applied to handle missing values, normalize numerical attributes, and balance class distributions. The experimental results demonstrate that the proposed LSTM-SHAP model outperforms traditional machine learning methods in predicting student performance, achieving an accuracy of 92.88%, precision of 92.96%, recall of 92.80%, and F1-score of 92.79% on the OULAD dataset. In a cross-domain evaluation, it maintains strong generalizability with 85.0% accuracy, 98.4% precision, 78.5% recall, and an F1-score of 87.2% when tested on the xAPI dataset. Additionally, SHAP analysis reveals that assessment scores, time spent on course materials, and forum interactions are the most important factors behind academic success. The statistical tests, such as ANOVA, Friedman tests and post hoc Nemenyi, are applied to assess the statistical significance of the differences in performance between the models, and results suggest that LSTM outperforms other models in each performance metric.

An interactive analytical dashboard that analyzes students’ facial expressions during online lectures is crucial for digital learning platforms. This research addresses the need for educational institutions to analyze individual students’ emotions to improve teaching standards. Given the challenge of occluded facial data, we employ a regenerative Generative Adversarial Network (GAN) to reconstruct these occluded regions. Subsequently, the emotions of the students are predicted and analyzed using our proposed interactive dashboard, which incorporates additional inputs such as subject name and teaching faculty. The dashboard visualizes various charts and analytics to support informed decision-making. We validated our deep learning model using the CK+ dataset, achieving notable accuracy in classifying each type of emotion. Our results demonstrate that the model can effectively interpret student emotions, even in the presence of occlusions, thereby providing educators with precise, real-time emotional insights to tailor their teaching methodologies effectively.