文章基本信息

标题：Predictive Assessment of Learner’s Performance Using Decision Trees with Genetic Algorithms
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作者：S Neelima ; Bodapati Prajna
期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2016
卷号：7
期号：4
页码：119-124
语种：English
出版社：Ayushmaan Technologies
摘要：We recommend that the configuration and execution of compelling Social Learning Analytics present critical difficulties and open doors for both exploration and endeavor, in three imperative regards. The first is that the learning scene is exceptionally turbulent at present, in no little part because of mechanical drivers. Online social learning is rising as a huge wonder for an assortment of reasons, which we survey, so as to spur the idea of social learning. We finish up by returning to the drivers and inclines, and consider future situations that we may see unfurl as SLA devices and administrations full grown. Conceptual Data Mining is a famous information revelation procedure. In information mining decision trees are of the straightforward and capable basic leadership models. In this anticipate, an algorithm is proposed for predicting a learner’s performance utilizing decision trees and genetic algorithm, GDADT algorithm. Id3 algorithm is utilized to make numerous decision trees, each of which predicts the performance of an understudy in view of an alternate list of capabilities. Since every decision tree furnishes us with an understanding to the plausible performance of every understudy; and diverse trees give distinctive results, we are ready to foresee the performance as well as distinguish regions or elements that are in charge of the anticipated result. For higher precision of the acquired results, genetic algorithm is additionally joined. The genetic algorithm is executed on the n-ary trees, by computing the wellness of every tree and applying hybrid operations to acquire numerous eras, each adding to making trees with a superior wellness as the eras increment, lastly bringing about the decision tree with the best exactness. The outcomes so acquired are very reassuring.
关键词：Genetic Algorithm;Decision Tree Algorithm;GDADT;Inductive Learning