A framework for smart academic guidance using educational data mining
A framework for smart academic guidance using educational data mining
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DOI：
10.1007/s10639-018-9838-8
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发表时间：
2019-03-01
影响因子：
5.5
通讯作者：
Mammass, Driss
中科院分区：
文献类型：
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作者：
Mimis, Mohamed;El Hajji, Mohamed;Mammass, Driss
教育推荐系统为学业指导和适应性学习提供支持一直是智能教育研究的重要课题。不良的指导会给继续学习带来困难，并可能导致辍学。本文通过分析学生的成绩、社会经济数据以及学生的学习动机来预测学生的学习成绩，探索教育数据挖掘在学业指导推荐中的潜力。所提出的模型进行了分析和测试，使用学生的数据收集的预备班为大学校Reda Slaoui（CPGE）-摩洛哥。更具体地说，它提出了使用三个模型，适用于真实的数据：决策树，朴素贝叶斯和神经网络。数据包括类期间（2012-2014年和2013-2015年）的330名学生在专业级数学物理（MP）和工程科学（MPSI）。结果表明，我们的框架可以更准确地预测学生的表现。
The educational recommendation system to provide support for academic guidance and adaptive learning has always been an important issue of research for smart education. A bad guidance can give rise to difficulties in further studies and can be extended to school dropout. This paper explores the potential of Educational Data Mining for academic guidance recommendation by predicting students' performance which involves analyzing data of students' records, socio-economic data and of course the student's motivation. The proposed model was analyzed and tested using student's data collected from the preparatory classes for Grandes Ecoles Reda Slaoui (CPGE) - Morocco. More specifically, it proposes the use of three models that were applied on real data: Decision tree, Naive Bayes, and Neural networks. The data include the classes period (2012-2014 and 2013-2015) of 330 students in specialty the grade Mathematical Physics (MP) and Engineering Sciences (MPSI). The performance results indicate that our framework can make more accurate predictions of students' performance.