A proposed Smart System for Determining Appropriate Educational path for Secondary Education Students | ||||
| المجلة العلمية لكلية التربية النوعية جامعة دمياط | ||||
| Volume 2024, Issue 10, December 2024, Page 301-317 PDF (988.74 K) | ||||
| Document Type: البحوث العلمية الأصيلة | ||||
| DOI: 10.21608/sjeud.2024.300318.1048 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
محمد فرح الجلاد1; السعيد السعيد محمد عبد الرازق  2; احمد طلعت سحلول3
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| 1كلية التربية النوعية جامعه دمياط | ||||
| 2كلية التربية النوعية جامعة دمياط | ||||
| 3كليه التربيه النوعيه جامعه دمياط | ||||
| Abstract | ||||
| It is difficult to (predict) determine a student's academic path due to the intervention that is not based on the scientific analysis of his previous grades and his scientific level in the previous scientific stages, for several reasons, including There is no modern scientific system based on analyzing and monitoring the progress and delay of students in grades and their performance, relying on the student's wishes without regard to the level of scientific, the main objective of this research paper is to build an intelligent system capable of analyzing the grades of scientific and literary subjects from the preparatory stage to the secondary stage. This paper also focused on how to use a prediction algorithm to determine the most appropriate scientific path for the student. Data of students from both numerical and secondary stages were collected and various operations were performed on the data to improve and revise them, as well as operations of attribute engineering were performed, and finally KNN algorithm was used to classify students according to one of the two groups, scientific or literary. The proposed machine learning approach achieved 92.5% of recognition accuracy which is the highest compared to other algorithms. | ||||
| Keywords | ||||
| KNN - Correlation; Quantization; Data Scaling; Feature Engineering | ||||
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