Engaging in Student-Centered Educational Data Science Through Learning Engineering

Campenhout, Rachel Van; Jerome, Bill; Johnson, Benny G.

doi:10.1007/978-981-99-0026-8_1

Cited by 3 publications

(1 citation statement)

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“…By leveraging these advanced computational techniques, this study aims to transcend the limitations of traditional assessment methods, providing educators and policymakers with actionable insights to optimize student placement and support strategies. Furthermore, the research underscores the importance of embracing technological innovation in educational assessment, paving the way for a more data-driven and student-centered approach to academic classification [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Classification of Students' Academic Performance Using Neural Network and C4.5 Model

Sulika,

Kusumawati,

Arif

2024

International Journal of Advances in Data and Information Syste

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ducation involves deliberately creating an environment and learning process to empower students to fully utilize their academic and non-academic potential. It encompasses fostering spiritual qualities, religious understanding, self-discipline, cognitive abilities, and skills necessary for personal, societal, national, and state development. Madrasah Aliyah, in particular, emphasizes preparing participants for higher studies in areas of their interest, thereby showcasing their academic prowess. The evaluation of educational models like Neural Networks is crucial for ensuring their effectiveness in problem-solving. This involves testing and assessing the performance of the Neural Network model to ensure its accuracy and reliability. Similarly, the C4.5 method, based on condition data mining, is utilized to measure classification performance by assessing accuracy, precision, and recall. Research findings indicate that the neural network algorithm is more adept at accurately classifying students' academic abilities compared to the C4.5 algorithm. With an accuracy of 92.6% for the neural network algorithm and 80.6% for the C4.5 algorithm, it is evident that the former is more precise in determining the classification of students' academic abilities. This highlights the suitability of the neural network approach for classifying academic abilities in Madrasah Aliyah. Furthermore, the insights gained from this classification process can be extrapolated to benefit other madrasas.

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Section: Introductionmentioning

confidence: 99%