Schools are embracing the use of data-driven information to track student achievement and performance. Conventional ways of tracking performances are not effective in the intricate nature of the relationships among diverse academic contributions. This research works on the necessity of an efficient, but simple, artificial intelligence based framework to examine and visualize the factors mentioned and to take proactive measures that will help find out students who might not need more than a top-quality academic assistance. The main purpose of the research is to come up with a machine learning model that is easy to interpret, has the predictive strength of the end-of-year academic scores and classifies students into groups of \\\"pass\\\" and \\\"fail.\\\" Also, the research will visualise the relationship between particular inputs (ex: hours of study, attendance) and performance in general and characterize a feature importance analysis to determine which factors have the most profound impact on student achievement. The research works with the artificial data including major academic variables: study hours, attendance, assignment grades, internal grades, and past GPA. The methodology will use two different machine learning models: Linear Regression to predict continuous performance scores and Decision Tree Classification to perform binary categorisation (Pass/Fail). Visualisation tools were combined to plot interactions among variables, and parameter analysis was performed in terms of standard accuracy measurements of regression as well as classification problems. The results indicate that the most important predictors of academic success are study hours, internal marks and attendance. Linear Regression model largely was able to predict final scores with high correlation to input data whereas the Decision Tree classifier offered a simple, interpretable logic with which students can be categorised. Analysis of feature importance provided a reason on why the consistent engagement and incremental assessment has a greater influence on the outcome rather than just the previous GPA. The offered AI-based framework is a scalable and understandable research proposal method of analysing educational data. The system facilitates informed, data-driven decisions made by educators by highlighting its critical performance drivers, and it helps to deliver timely interventions to at-risk students. Further work would entail the application of the framework on bigger datasets, and real-life contexts to improve predictive accuracy in diverse education settings.
Khushi et al. (Thu,) studied this question.