The impact of artificial intelligence with multi-head self-attention based deep learning model for students' academic performance monitoring

Vollständiger Abstract

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Introduction Educational data mining has been applied to examine students' data collected from different educational organizations to forecast academic students' performance, which could assist them in accomplishing improved results in their upcoming courses. Methods This study presents the impact of artificial intelligence with multi-head self-attention on students' academic development using metaheuristic optimization algorithms (IAIMSA-SADMOA) model. The study aims to advance an effective method for students' academic performance using artificial intelligence tools to improve learning outcomes, engagement, and personalized education. The min-max normalization is employed in the data normalization phase for transforming input data into a beneficial format. The fruit fly optimization algorithm is deployed for the feature selection process to select the most related features from a dataset. Moreover, the proposed model designs bidirectional long short-term memory and multi-head self-attention mechanisms for the student's academic performance classification process. The parameter tuning process is performed through a sparrow search algorithm to enhance the classification performance. Results and Discussion The experimental evaluation of the IAIMSA-SADMOA technique occurs using a benchmark dataset. The empirical results indicated the enhanced performance of the proposed method in comparison with recent approaches.