###### Abstract  
Educational institutions are increasingly focused on supporting students who may be facing academic challenges, aiming to enhance their educational outcomes through targeted interventions. Within this framework, leveraging advanced deep learning techniques to develop recommendation systems becomes essential. These systems are designed to identify students at risk of underperforming by analyzing patterns in their historical academic data, thereby facilitating personalized support strategies. This research introduces an innovative deep learning model tailored for pinpointing students in need of academic assistance. Utilizing a Gated Recurrent Neural Network (GRU) architecture, the model is rich with features such as a dense layer, max-pooling layer, and the ADAM optimization method used to optimize performance. The effectiveness of this model was tested using a comprehensive dataset containing 15,165 records of student assessments collected across several academic institutions. A comparative analysis with existing educational recommendation models, like Recurrent Neural Network (RNN), AdaBoost, and Artificial Immune Recognition System v2, highlights the superior accuracy of the proposed GRU model, which achieved an impressive overall accuracy of 99.70%. This breakthrough underscores the model's potential in aiding educational institutions to proactively support students, thereby mitigating the risks of underachievement and dropout. GRU; max pooling; deep learning; students' performance; classification; ADAM optimization algorithm +
Footnote †: journal: Medical  
As time progresses, the data repositories of educational organizations have expanded, transforming into massive pools of latent knowledge. This hidden information is brimming with potential yet poses significant challenges in terms of storage, capture, analysis, and representation. These complexities have necessitated the reclassification of these databases as big data [4; 5; 6]. Faced with this paradigm shift, educational institutions are now seeking advanced analytical tools capable of deciphering both student and institutional performances [7]. Data centers in educational settings often exhibit big data characteristics and apply specific data mining methods to extract hidden insights. The synergy between data mining and educational systems, as illustrated in Figure 2, reveals the advantageous impact these insights can have on students, enriching their educational experience with knowledge gleaned from expansive and complex datasets. This research contributes significantly by employing a Gated Recurrent Neural Network (GRU). The GRU model excels in identifying crucial hidden patterns--the key features within learner records at educational institutions. Given the typically large and intricate nature of these datasets, the GRU model serves as an ideal foundation for learning recom  
Figure 1: Student performance factors. Figure 2: The relationship between an educational system and data mining techniques. mendation systems, which aim to boost student performance through in-depth internal assessments, moving beyond the scope of conventional statistical models. The GRU is particularly proficient in handling non-stationary sequences and effectively assessing student performance. Its comparative advantage over other deep learning models, such as RNNs, and various machine learning algorithms lies in its ability to bypass long-term dependency challenges and offer superior interpretability. This study aimed to unveil and assess a cutting-edge deep learning model engineered to identify students who are not meeting academic expectations in educational environments. By incorporating a sophisticated Gated Recurrent Neural Network (GRU) complemented with features such as dense layers, max-pooling layers, and the ADAM optimization algorithm, the objective was to enable educational institutions to pinpoint students in need of additional academic support.