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The growth and development of predictive models in the current world has influenced considerable changes. Today, predictive modelling of academic performance has transformed more than a few institutions by improving their students' academic performance. This paper presents a computational predictive model using artificial neural networks to predict whether a student will pass or fail. The model is unique in the current literature as it is specifically designed to evaluate the effectiveness of the predictive strategies on neural networks as well as on five additional algorithms. The analysis of the experimental results shows that Artificial Neural Networks outperformed the eXtremeGBoost, Linear Regression, Support Vector Machine, Naive Bayes, and Random Forest algorithms for academic performance prediction.

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Literature review Web service selection (MCDM problem) Def ine criteria and sub-criteria for evaluation Construct pairwise comparison matrix Computer weights, normalized decision matrix Calculate the eigen value and eigen vector Perform consistency tests Consistency Ratio (CR<0.1)