The utilization of students artificial neutral network (SANNs) to predict academic performance has gained traction in educational research, effectively combining sophisticated computational methods with valuable pedagogical perspectives. This emerging field integrates modern technology with crucial educational principles to enhance the understanding of students’ academic achievements.
A comprehensive literature review reveals that several studies have effectively utilized students artificial neutral network (SANNs) to model and forecast academic outcomes, integrating a myriad of variables such as socio-economic status, attendance patterns, engagement levels, and individual learning styles. These models leverage the inherent ability to learn from complex, non-linear relationships within data, thereby providing more nuanced predictions compared to traditional statistical methods.
Research underscores the significance of feature selection and data preprocessing in improving model accuracy and interpretability. Moreover, various studies have shown differing levels of success, with performance metrics frequently reflecting high accuracy in predicting student performance. Consequently, this reinforces the potential of students’ artificial neural networks (SANNs) as effective tools for educators and administrators to identify at-risk students and customize interventions.
In addition to the necessity of refining algorithms continuously, it is imperative to merge a variety of datasets seamlessly and develop ethical principles to mitigate worries related to data privacy and the impact of algorithmic bias. This underscores the importance of adopting a comprehensive approach towards enhancing algorithms and ensuring ethical practices to address emerging challenges in data science.
Overall, the findings highlight the potential and positive outlook within educational environments. It becomes evident that an interdisciplinary strategy merging data science, psychology, and academic theory can drive additional progress and innovation in this specialized area of study. By leveraging the collective expertise from different fields, it is plausible to anticipate further advancements in improving educational practices and student outcomes.