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首页> 外文会议>2012 4th International Congress on Engineering Education: Improving Engineering Education: Towards Sustainable Development >Neural network model to predict electrical students' academic performance
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Neural network model to predict electrical students' academic performance

机译：神经网络模型预测电气专业学生的学习成绩

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摘要

Students performance is very crucial to any educational institution particularly in the engineering field. This paper describes a neural network based model (NN model) for academic performance prediction of Electrical Engineering Degree students at the Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), Malaysia. The study was conducted on student intakes from Matriculation entry level. The performance was measured based on their cumulative grade point average (CGPA) upon graduation. The students' results for fundamentals subjects at first semester are used as predictor variables (initial values) for predicting the expected (projected) final CGPA upon graduation using Artificial Neural Network (ANN). The outcomes of the study indicated that there appears to be a direct correlation between students' results for core subjects at semester one with the final overall academic performance irrespective of their gender. It can be ascertained that the analysis on strong students' abilities in engineering fundamentals contributed strongly in influencing the overall academic performance in Engineering. Based on the outcomes of this study, we believe that strategic interventions can be done during their study period to improve their final performance, which can be extracted from this prediction model.
机译：学生的表现对任何教育机构都至关重要，特别是在工程领域。本文介绍了一种基于神经网络的模型（NN模型），用于预测马来西亚Teknologi MARA大学（UiTM）电机工程学院的电机工程学位学生的学业成绩。该研究是针对入学入门级学生的。成绩是根据毕业时的累积平均成绩（CGPA）来衡量的。第一学期学生的基础知识课程成绩将用作预测变量（初始值），以便使用人工神经网络（ANN）预测毕业后的预期（预计）最终CGPA。研究结果表明，第一学期学生的核心科目成绩与最终的整体学业成绩之间存在直接的相关性，而不论其性别如何。可以确定的是，对强大学生的工程基础知识能力的分析对影响工程学的整体学业成绩有很大的贡献。根据这项研究的结果，我们认为可以在研究期间进行战略干预以提高其最终绩效，这可以从此预测模型中提取。

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