Educational Data Mining plays a critical role in advancing the learning environment by contributing state-of-the-art methods, techniques, and applications. The recent development provides valuable tools for understanding the student learning environment by exploring and utilizing educational data using machine learning and data mining techniques. Modern academic institutions operate in a highly competitive and complex environment. Analyzing performance, providing high-quality education, strategies for evaluating the students’ performance, and future actions are among the prevailing challenges universities face. Student intervention plans must be implemented in these universities to overcome problems experienced by the students during their studies. In this systematic review, the relevant EDM literature related to identifying student dropouts and students at risk from 2009 to 2021 is reviewed. The review results indicated that various Machine Learning (ML) techniques are used to understand and overcome the underlying challenges; predicting students at risk and students drop out prediction. Moreover, most studies use two types of datasets: data from student colleges/university databases and online learning platforms. ML methods were confirmed to play essential roles in predicting students at risk and dropout rates, thus improving the students’ performance.

Higher education institutions often struggle with increased dropout rates, academic underachievement, and delayed graduations. One way in which these challenges can potentially be addressed is by better leveraging the student data stored in institutional databases and online learning platforms to predict students’ academic performance early using advanced computational techniques. Several research efforts have focused on developing systems that can predict student performance. However, there is a need for a solution that can predict student performance and identify the factors that directly influence it. This paper aims to develop a model that accurately identifies students who are at risk of low performance, while also delineating the factors that contribute to this phenomenon. The model employs explainable machine learning (ML) techniques to delineate the factors that are associated with low performance and integrates rule-based model risk flags with the developed prediction system to improve the accuracy of performance predictions. This helps low-performing students to improve their academic metrics by implementing remedial actions that address the factors of concern. The model suggests proper remedial actions by mapping the students’ performance in each identified checkpoint with the course learning outcomes (CLOs) and topics taught in the course. The list of possible actions is mapped to this checkpoint. The developed model can accurately distinguish students at risk (total grade $$< 70\%$$< 70 % ) from students with good performance. The Area under the ROC Curve (AUC ROC) of binary classification model fed with four checkpoints reached 1.0. Proposed framework may aid the student to perform better, increase the institution’s effectiveness and improve their reputations and rankings.

Technological advances have significantly affected education, leading to the creation of online learning platforms such as virtual learning environments and massive open online courses. While these platforms offer a variety of features, none of them incorporates a module that accurately predicts students’ academic performance and commitment. Consequently, it is crucial to design machine learning (ML) methods that predict student performance and identify at-risk students as early as possible. Graph representations of student data provide new insights into this area. This paper describes a simple but highly accurate technique for converting tabulated data into graphs. We employ distance measures (Euclidean and cosine) to calculate the similarities between students’ data and construct a graph. We extract graph topological features (GF) to enhance our data. This allows us to capture structural correlations among the data and gain deeper insights than isolated data analysis. The initial dataset (DS) and GF can be used alone or jointly to improve the predictive power of the ML method. The proposed method is tested on an educational dataset and returns superior results. The use of DS alone is compared with the use of $$DS + GF$$D S + G F in the classification of students into three classes: “failed”,“at risk”, and “good”. The area under the receiver operating characteristic curve (AUC) reaches 0.948 using DS, compared with 0.964 for $$DS + GF$$D S + G F . The accuracy in the case of $$DS + GF$$D S + G F varies from 84.5 to 87.3%. Adding GF improves the performance by 2.019% in terms of AUC and 3.261% in terms of accuracy. Moreover, by incorporating graph topological features through a graph convolutional network (GCN), the prediction performance can be enhanced by 0.5% in terms of accuracy and 0.9% in terms of AUC under the cosine distance matrix. With the Euclidean distance matrix, adding the GCN improves the prediction accuracy by 3.7% and the AUC by 2.4%. By adding graph embedding features to ML models, at-risk students can be identified with 87.4% accuracy and 0.97 AUC. The proposed solution provides a tool for the early detection of at-risk students. This will benefit universities and enhance their prediction performance, improving both effectiveness and reputation.

Detecting at-risk students provides advanced benefits for improving student retention rates, effective enrollment management, alumni engagement, targeted marketing improvement, and institutional effectiveness advancement. One of the success factors of educational institutes is based on accurate and timely identification and prioritization of the students requiring assistance. The main objective of this paper is to detect at-risk students as early as possible in order to take appropriate correction measures taking into consideration the most important and influential attributes in students’ data. This paper emphasizes the use of a customized rule-based system (RBS) to identify and visualize at-risk students in early stages throughout the course delivery using the Risk Flag ($RF$). Moreover, it can serve as a warning tool for instructors to identify those students that may struggle to grasp learning outcomes. The module allows the instructor to have a dashboard that graphically depicts the students’ performance in different coursework components. The at-risk student will be distinguished (flagged), and remedial actions will be communicated to the student, instructor, and stakeholders. The system suggests remedial actions based on the severity of the case and the time the student is flagged. It is expected to improve students’ achievement and success, and it could also have positive impacts on under-performing students, educators, and academic institutions in general.