This study introduces an optimized ensemble deep neural network (Optimized Ensemble Deep-NN) to enhance the accuracy of predicting student grades. This model solves the problem of different and complicated student performance data by using deep neural networks, ensemble learning, and a number of optimization algorithms, such as Adam, SGD, and RMS…

Predicting academic performance in Educational Data Mining has been a significant research area. This involves utilizing machine learning techniques to analyze data from educational settings. Predicting student academic performance is a complex task due to the influence of multiple factors. This research uses supervised machine-learning approaches…

Student academic performance prediction is a significant area of study in the realm of education that has drawn the interest and investigation of numerous scholars. The current approaches for student academic performance prediction mainly rely on the educational information provided by educational system, ignoring the information on students'…

Higher education institutions have consistently strived to provide students with top-notch education. To achieve better outcomes, machine learning (ML) algorithms greatly simplify the prediction process. ML can be utilized by academicians to obtain insight into student data and mine data for forecasting the performance. In this paper, the authors…

As is rapidly becoming clear, data science increasingly permeates many aspects of life. Educational research recognizes the importance and complexity of learning data science. In line with this imperative, there is a growing need to investigate the factors that influence student performance in data science tasks. In this paper, we aimed to apply…

Predictive modelling in the education domain can be utilised to significantly improve teaching and learning experiences. Massive Open Online Courses (MOOCs) generate a large volume of data that can be exploited to predict and evaluate student performance based on various factors. This paper has two broad aims. Firstly, to develop and tune several…

In the current educational landscape, where large amounts of data are being produced by institutions, Educational Data Mining (EDM) emerges as a critical discipline that plays a crucial role in extracting knowledge from this data to help academic policymakers make decisions. EDM has a primary focus on predicting students' academic performance.…

The higher the level of development of higher education, the larger its contribution to socioeconomic development. In order to predict the trend of higher education development in a country more accurately, a new methodology is employed in this study. A weakening buffer operator-based GM (1, 1) model is constructed using Kazakhstan's gross…

Massive open online courses (MOOCs) have become one of the most popular ways of learning in recent years due to their flexibility and convenience. However, high dropout rate has become a prominent problem that hinders the further development of MOOCs. Therefore, the prediction of student dropouts is the key to further enhance the MOOCs platform.…

Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…

Project-based Learning (PBL) provides an effective environment for collaborative engineering design education. However, it is difficult to assess students' engagement and provide process-oriented feedback on their collaboration due to limited resources and scalability challenges. This paper presents an empirical study examining the application of…

Teaching Institutions could benefit from Early Warning Systems to identify at-risk students before learning difficulties affect the quality of their acquired knowledge. An Early Warning System can help preemptively identify learners at risk of dropping out by monitoring them and analyzing their traces to promptly react to them so they can continue…

Providing educators with understandable, actionable, and trustworthy insights drawn from large-scope heterogeneous learning data is of paramount importance in achieving the full potential of artificial intelligence (AI) in educational settings. Explainable AI (XAI)--contrary to the traditional "black-box" approach--helps fulfilling this…

Neural and symbolic architectures are key techniques in AI for learner modelling, enhancing adaptive educational services. Symbolic models offer explanation and reasoning for decisions but require significant human effort. On the other hand, neural architectures demand less human input and yield better predictions, yet lack interpretability. Given…

Learning analytics dashboards (LADs) are emerging tools that convert abstract, complex information with visualizations to facilitate teachers' data-driven pedagogical decision-making. While many LADs have been designed, teachers' capacities for using such LADs are not well articulated in the literature. To fill the gap, this study provided a…