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The study of educational innovations has attracted increasing attention from academics around the world. Educational innovation proposes the implementation of new approaches or practices that are beneficial and make an impact on individuals or academic communities. The current educational model of many designed for this generation of “digital natives”. For this reason face the challenge of building teaching strategies that generate meaningful educational experiences. This research seeks to address this issue through a systematic mapping that includes empirical process that study innovations in educational practices. A qualitative and quantitative approach was applied using a four-stage research methodology to evidence innovation in higher education. After employing the selected methodology and applying all the exclusion criteria, related to the topic were identified. The proposed system using hybridization of linear vector quantization algorithm of learning, the context of learning, the role of the teacher, the role of the learner, and the performance improved and testing accuracy high. The meta-heuristic models to assess the performance of the students, then hybridization of linear vector quantization model for predicting the educational results and employability chances of the students is designed and developed an Ant Colony Optimization (ACO) with feature subset selection and Random Forest (RF) model for classifying educational DM.

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Vaishnavi Dali.A, Ganeshen.P, & Vijaya KumarV. (2022). Artificial Intelligence Aided Innovation Education Based On Multiple Intelligence. International Journal of Intellectual Advancements and Research in Engineering Computations, 10(2), 42–48. Retrieved from