Development of Machine Learning Based Framework for Classification and Prediction of Students in Virtual Classroom Environment

B. Loganathan, Indrajit Patra, Vipul Garchar, Harikumar Pallathadka, M. Naved, Sanjeev Gour
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引用次数: 8

Abstract

MOOCs provide a new way to train students, reshape the way students learn, and attract students from all over the world to participate in their courses. Machine learning is a key component of artificial intelligence. Machine learning may be used to classify and predict outcomes. In order to aid the underachieving or average student, educational institutions need to know how much work they need to put in. The importance of EDM models can't be overstated, since they make use of past student performance data to forecast future student success. Educational institutions utilize a variety of methods to collect data on the characteristics of students who are actively involved in the learning process in order to help them and their pupils improve their performance. In a virtual classroom, pupils may be classified and predicted using the methodology presented in this article.
基于机器学习的虚拟课堂环境下学生分类与预测框架的开发
mooc提供了一种培训学生的新方式,重塑了学生的学习方式,并吸引了来自世界各地的学生参与他们的课程。机器学习是人工智能的关键组成部分。机器学习可以用来分类和预测结果。为了帮助成绩不佳或一般的学生,教育机构需要知道他们需要付出多少努力。EDM模型的重要性怎么强调都不为过,因为它们利用过去学生的表现数据来预测未来学生的成功。教育机构利用各种方法收集积极参与学习过程的学生的特征数据,以帮助他们和他们的学生提高他们的表现。在虚拟教室中,可以使用本文提出的方法对学生进行分类和预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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