## 6 Future Work  
Some future research opportunities regarding this topic might circulate around areas such as development of the adaptive models that can be adjusted to fit the changing data pattern over time. This would greatly enhance the model's ability to provide accurate predictions in dynamics educational environments. Another area to consider could be to explore the advanced machine learning techniques through investigating advanced machine learning techniques such as deep learning, ensemble methods, and reinforcement learning, which could further improve the accuracy and robustness of predictive models. Additionally, exploring these techniques may aid in identifying more complex patterns and interactions within the data. Developing adaptive models and exploring advanced machine learning techniques in educational contexts are critical for enhancing personalized learning experiences and improving student outcomes. Adaptive models leverage real-time data to dynamically adjust learning content, catering to individual student needs and learning spaces. Techniques such as deep learning, reinforcement learning, and ensemble methods provide robust frameworks for analyzing complex educational data. Deep learning models,with their ability to process large datasets and capture intricate patterns, are particularly effective in predicting student performance and identifying at-risk learners. Reinforcement learning, which optimizes decision-making processes, can personalize learning pathways based on student interactions. Ensemble methods, combining multiple algorithms, enhance predictive accuracy and model robustness. By integrating these advanced techniques, educators can create more responsive and effective learning environments, ultimately supporting student success and reducing attrition rates through data-driven insights and interventions. We agree that including socio-economic, psychological, and external factors can indeed provide a more comprehensive analysis and enhance the overall study here; however, we would like to note that obtaining this sensitive information may present challenges due to privacy concerns and data availability. Despite these difficulties, in our future work, we aim to gather and utilize this data where possible to enhance the accuracy and robustness of the predictive model. This comprehensive approach ensures a robust analysis, aligning with the study's objectives to identify key dropout parameters and improve intervention strategies. Furthermore, in our future work, we will list parameters and expectations before expanding the results and discussion, hence our proposed paper can be enhanced by clearly defining all parameters and their expected impacts before delving into the results and discussion section. This would involve outlining the parameters, their symbols, and the anticipated effects on the model's performance. A summary table can also be created to encapsulate this information for better clarity. Therefore, our future work will include a more thorough exploration of different algorithms which could have provided deeper insights into the strengths and weaknesses of each method in the context of dropout prediction. Our proposed paper identifies academic performance as the primary factor influencing student attrition and overlooks other important variables such as socio-economic status, mental health, and external commitments that can significantly affect student outcomes. Students from lower socio-economic backgrounds may struggle with financial difficulties or limited resources, which can increase the likelihood of dropout. Similarly, mental health issues like anxiety and depression can hinder a student's ability to perform academically, attend classes, or engage with peers, leading to higher attrition rates. Additionally, students with external commitments, such as part-time jobs or family responsibilities, may find it difficult to balance academic demands with their personal lives, increasing the risk of dropping out.