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The effective learning requires putting down various associations of new ideas to old ones to integrate some innovative thoughts. The learners must change the associations among the things they already know, or even reject some long-held attitude about the world. The choice to the essential reformation is to deform the new information to fit their old ideas or to reject the new information entirely. Learners come to the classroom with their own ideas, some may be correct and some may not be, concerning roughly each topic they are expected to come across. If their perception and misunderstanding are unnoticed or discharged out of control, it affects the learning of a learner. The learners must be encouraged to build up new observation by seeing how such observation helps them make better sense of the world. The objective of this research paper is to put down the fundamentals of learning that promotes effective learning in an instructor-led virtual classroom and to analyze the learners' learning performance using the Discriminant Analysis, a data mining technique. The Discriminant Analysis uses statistically significant determinants to predict learners' learning in a classroom.

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Emotions undergone unusual stages all through pandemic situation creating a traumatic in the minds of learners that extirpate the growth of a learner. The main objective of the proposed work is to identify the face emotions of a learner in the learning environment that helps to estimate the attention of learners during lectures in the learning environment. The identification of facial emotions is performed using Naïve Bayes algorithm. Based on the emotional records, the academic achievement is analyzed with random forest algorithm. The findings of the research are that the attention of the learners in the learning environment with positive emotions produces good academic results.