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Harnessing AI for a Comprehensive Student Performance Analysis: A Research Prospect in Educational Data Mining

  • Unique Paper ID: 164572
  • Volume: 10
  • Issue: 12
  • PageNo: 1894-1902
  • Abstract:
  • In the field of education, there are many different criteria and elements used to assess pupils as they go through their career. The responsibility of a learner is to introduce the mechanisms that will enable them to modify the way they shape this process. The process of analysing and developing a system such as student performance analysis may be made easier with the use of a system that incorporates multilabel classification, educational data mining, and the infused AI model. We attempted to do so by using the method of training a neural network with information gathered about the pupils' present educational trajectory. This model will provide multilabel categorization using machine learning techniques by integrating survey data with real-time educational data that is collected within the system. By utilising the OCEAN big five model features, student personality traits may be assessed through the integration of survey data. When developing the more complex models around these ideas, keep in mind Kolb's learning styles as a guide. Gaining a comprehensive knowledge of a student's talents and potential requires evaluating them outside of the usual classroom setting. The traditional measures, which include grades, certificates, attendance records, and proficiency in practical exams, sometimes fall short of capturing the complex character of students' accomplishments. This research also attempts to offer greater insights into characteristics and parameters like study habits, learning styles, home environments, academic relationships, coping strategies, cognitive factors, etc. by integrating multilabel categorization with the EDM framework. It facilitates the development of a tracking system for students' academic progress and aids in navigating the possibilities and difficulties presented by the educational environment.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 12
  • PageNo: 1894-1902

Harnessing AI for a Comprehensive Student Performance Analysis: A Research Prospect in Educational Data Mining

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