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Cognitive Factors in Students' Academic Performance Evaluation using Artificial Neural Networks

机译:人工神经网络评估学生学习成绩的认知因素

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Performance evaluation based on some cognitive factors especially Students’ Intelligent Quotient rating (IQR), Confidence Level (CoL) and Time Management ability gives an equal platform for better evaluation of students’ performance using Artificial Neural Network. Artificial Neural Networks (ANN) models, which has the advantage of being trained, offers a more robust methodology and tool for predicting, forecasting and modeling phenomena to ascertain conformance to desired standards as well as assist in decision making. This work employs Machine Learning and cognitive science which uses Artificial Neural networks (ANNs) to evaluated students’ academic performance in the Department of Computer Science, Akwa Ibom State University. It presents a survey of the design, building and functionalities of Artificial Neural Network for the evaluation of students’ academic performance using cognitive factors that could affect student’s performances.
机译:基于一些认知因素的绩效评估,特别是学生的智能商评分(IQR),置信度(CoL)和时间管理能力,为使用人工神经网络更好地评估学生的绩效提供了一个平等的平台。人工神经网络(ANN)模型具有受过训练的优势,它为预测,预测和建模现象提供了更强大的方法和工具,以确保符合所需标准并有助于决策。这项工作采用了机器学习和认知科学,后者使用人工神经网络(ANN)评估了阿夸伊博姆州立大学计算机科学系的学生的学习成绩。它对人工神经网络的设计,构建和功能进行了调查,以使用可能影响学生表现的认知因素来评估学生的学习成绩。

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