Comprehensive analysis of factors influencing the real-world application of machine learning for student success rate calculation and their impacts on student achievement & educational institutions

Abstract

The successful application of machine learning for student success rate calculation is contingent upon a comprehensive analysis of several interrelated factors that span the educational landscape. Educational institutions must ensure that they have access to accurate and relevant data, including student demographics, academic performance records, and even extracurricular activities, to build effective predictive models. Moreover, data privacy and security concerns must be addressed to maintain the integrity of sensitive student information. Next, the choice of machine learning algorithms is crucial. The selection should align with the specific objectives of predicting student success, taking into account the type and volume of data available. Factors such as classification algorithms, regression techniques, and deep learning models must be carefully considered, and their performance must be assessed through rigorous testing and validation. Addressing potential biases in machine learning models is crucial to ensure equitable outcomes. Careful attention must be paid to the training data, as biased data can lead to discriminatory predictions. Ongoing monitoring and model refinement are necessary to minimize these biases and promote fairness in student success predictions. Thus present paper is focused on a comprehensive analysis of factors influencing the real-world application of machine learning for student success rate. In order to achieve this goal, relevant researches on machine learning is considered and further enhancement has been made to make the proposed work more efficient.