Predictive Models for Educational Purposes: A Systematic Review

This systematic literature review evaluates predictive models in education, focusing on their role in forecasting student performance, identifying at-risk students, and personalising learning experiences. The review compares the effectiveness of machine learning (ML) algorithms such as Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Decision Trees with traditional statistical models, assessing their ability to manage complex educational data and improve decision-making. The search, conducted across databases including ScienceDirect, IEEE Xplore, ACM Digital Library, and Google Scholar, yielded 400 records. After screening and removing duplicates, 124 studies were included in the final review. The findings show that ML algorithms consistently outperform traditional models due to their capacity to handle large, non-linear datasets and continuously enhance predictive accuracy as new patterns emerge. These models effectively incorporate socio-economic, demographic, and academic data, making them valuable tools for improving student retention and performance. However, the review also identifies key challenges, including the risk of perpetuating biases present in historical data, issues of transparency, and the complexity of interpreting AI-driven decisions. In addition, reliance on varying data processing methods across studies reduces the generalisability of current models. Future research should focus on developing more transparent, interpretable, and equitable models while standardising data collection and incorporating non-traditional variables, such as cognitive and motivational factors. Ensuring transparency and ethical standards in handling student data is essential for fostering trust in AI-driven models.