Predicting Achievement of Students in Smart Campus
Predicting Achievement of Students in Smart Campus
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DOI：
10.1109/access.2018.2875742
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发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Wang, Yongchao
中科院分区：
文献类型：
--
作者：
Qu, Shaojie;Li, Kan;Wang, Yongchao
不同校园信息系统之间的数据相互隔离，这些系统产生的大数据中没有太多有效信息，这使得预测学生的成绩是一个挑战。本文设计了一个学生成绩预测框架，包括数据处理和学生成绩预测两部分。在数据处理中，设计了数据抽取、数据清洗、特征提取等功能。利用数据仓库中的这些数据，我们提出了一种基于层监督多层感知器（MLP）的方法来预测学生的成绩。监督被馈送到MLP的每个相应的隐藏层，以提高学生成绩预测的性能。与支持向量机、朴素贝叶斯、逻辑回归和MLP等方法相比，该方法具有更好的性能。
Isolate data among different campus information systems and not much effective information among the big data generated by these systems cause that it is a challenge for predicting achievement of students. This paper designs a student achievement predicting framework, which includes data processing and student achievement predicting. In the data processing, data extraction, data cleaning, and feature extrac-tion are designed. Using these data in data warehouse, we propose a layer-supervised multi-layer perceptron (MLP)-based method to predict the achievement of students. Supervisions are fed to each corresponding hidden layer of MLP to improve the performance of student achievement prediction. Compared with SVM, Naive Bayes, logistic regression, and MLP, our method gets a better performance.