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An Adaptive Educational Data Mining Technique for Mining Educational Data Models in Elearning Systems

机译：用于学习系统中的教育数据模型的自适应教育数据挖掘技术

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Background/Objectives: After a deep survey carried out by the National data premises with stats which reveals that the insufficient models which are existing in the real world scenario for modelling the data values in educational CMS, educational websites etc. are insufficient. In this paper a new adaptive data mining techniques is used to model the educational data using the DM classifiers. Methods/Statistical analysis: A deep study and interactive graphical representation for e-commerce in educational system has been defined in a refined view. Here the proposed data model using advanced classifier introduces a new agent based intelligent system which construct the data model in form of 3D cubes for every classification. For example, the classifier classifies the data model with subject to tutor, faculty, students, syllabi, academia orientation. Findings: The parameter mentioned is modelled in the cubical view. These cubical data model are given as the inbound inputs to the processing tools like OLAP, OPAC. Experimental results in the form of data model are demonstrated in the result section. During the experiment, the data's are read into the matrix and then processed. Applications/Improvements: Finally the cubic model can be used to any short of modelling techniques which supports all the relational models, logical models and physical models, whereas the proposed model has been demonstrated as the result for the selective datasets and has not been tested with any universal data mining DB's.
机译：背景/目标：国家数据机构对统计数据进行了深入调查后发现，现实世界中存在的模型不足以对教育CMS，教育网站等中的数据值进行建模。在本文中，一种新的自适应数据挖掘技术被用于使用DM分类器对教育数据进行建模。方法/统计分析：在精致的视图中定义了对教育系统电子商务的深入研究和交互式图形表示。在这里，使用高级分类器提出的数据模型引入了一个新的基于智能体的智能系统，该智能系统为每个分类以3D立方体的形式构建数据模型。例如，分类器根据教师，教职员工，学生，教学大纲，学术界的取向对数据模型进行分类。结果：提到的参数是在立体视图中建模的。这些三次数据模型作为OLAP，OPAC等处理工具的入站输入给出。结果部分展示了数据模型形式的实验结果。在实验过程中，数据被读入矩阵，然后进行处理。应用/改进：最后，三次模型可用于支持所有关系模型，逻辑模型和物理模型的任何缺乏建模能力的模型，而所提出的模型已作为选择性数据集的结果进行了证明，尚未经过测试。任何通用数据挖掘数据库。

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