TECHNOLOGY AIDED BY ARTIFICIAL INTELLIGENCE A MULTI-INTELLIGENCES-BASED APPROACH TO EDUCATION

Author (s) : SUBHASHINI.J, S.SOWMIYA, J.SHANBAGAM, M.BRINDHA

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Abstract :
The deployment of novel educational techniques and procedures that are advantageous and have an impact on both individuals and academic communities is proposed. Many people's present educational approach was created with this generation of "digital natives" in mind. Face the challenge of developing instructional practices that result in worthwhile educational experiences for this reason. This work aims to address this problem by a systematic mapping that incorporates an empirical procedure that investigates variations in educational practices.Utilizing a four-stage study technique, a qualitative and quantitative approach was used to demonstrate innovation in higher education. Related to the issue were found after utilizing the methodology of choice and all exclusion criteria. The performance and testing accuracy have improved because to the suggested system's hybridization of the linear vector quantization algorithm of learning, the learning environment, the role of the instructor, and the role of the learner.An Ant Colony Optimization (ACO) with feature is designed and built using the meta-heuristic models to evaluate the performance of the students, followed by a hybridization of the linear vector quantization model to forecast the educational outcomes and employability prospects of the students. The performance and testing accuracy have improved because to the suggested system's hybridization of the linear vector quantization algorithm of learning, the learning environment, the role of the instructor, and the role of the learner.The hybridization of the linear vector quantization model and the ant colony optimization (ACO) with feature subset selection and random forest (RF) model for classifying educational DM are designed and developed to predict the educational outcomes and employability chances of the students.

Keywords : RFModel,AntColonyOptimization,HybridizationofLinearVectorQuantization Algorithm.

Conference Date : 30/08/2019

Pages : 214

Paper ID : 19205