教育系统预测模型的发展:使用Naïve贝叶斯分类器

M. Sharma, Monali Mavani
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引用次数: 10

摘要

随着信息通信技术(ICT)的出现,教育部门的教学过程也在发生变化。使用信息通信技术和数字内容的不同交付模式使电子学习和混合学习的概念更容易被接受。但并非所有可用的技术都能充分发挥潜力,有时甚至根本没有引入。商业智能(BI)就是其中之一。教育部门也有大量分散在不同形式的数据,这些数据可以重复使用,以做出更明智的决策。为了从教育数据中获得智能信息,可以使用各种数据挖掘技术。此外,随着越来越多的人意识到使用开源技术的好处,教育机构已经有可能以低成本或无成本使用各种技术。在本文中,我们使用开源软件Knime使用Naïve贝叶斯学习器和Naïve贝叶斯预测器来预测学生的成绩。我们还使用Moodle学生活动日志数据作为属性之一,以便使用Naïve贝叶斯理论预测结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of predictive model in education system: using Naïve Bayes classifier
With the advent of ICT (Information and Communication Technologies) education sector is also experiencing change in teaching process. Different mode of delivery with the use of ICT and digital content has made concept of E-learning and Blended learning more acceptable. But all the available technologies are not used with full potential, sometimes even not introduced at all. Business Intelligence (BI) is one of them. Educational sector also has got vast amount of data scattered in different forms which can be reused to make more intelligent decisions. Various data mining techniques are available which can be used in order to get intelligent information from educational data. Furthermore with the increasing awareness of benefits due to use of Open Source technologies it has become possible for educational institutes to use various technologies with low cost or no cost. In this paper we have used Open Source software Knime for predicting student's results using Naïve Bayesian Learner and Naïve Bayesian predictor. We also have used Moodle logs data of student's activities as one of the attributes in order to predict results using Naïve Bayes theory.
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