Abstract
The aim of paper is to show the benefits of the educational data mining (EDM) techniques, in order to understand about of the factors which lead to technical student’s success and failure, and predict their performance and determine the individual learning ability in engineering sciences. For these goals, we use the individual data of 311 student and their grades that were collected in Industrial Institute of Al-Diwaniyah city (Iraq) during 2015–2017 academic years, in order to predict the results of final theoretical exam in industrial drawing by applying EDM techniques, such as association rules mining, classification with decision tree algorithm learning, clustering with Apriori algorithm and anomaly detection implemented as the output model of the clustering. Using Microsoft SQL Server Business Intelligence Development Studio 2012 platform and based on Cross Industry Standard Process for Data Mining, we prepare of 13 nominal and numerical attributes for each student and consequently apply and finally evaluate all 4 EDM techniques. We conclude that: 1) association rules were revealed that the most important factor which contribute to the failure of the student is the “project” attribute; 2) decision tree classification permit to the teacher predict the future students and to correct the student's prediction path, but 3) clustering collects of the students into successful and failure groups and helps to the teacher to guide each group separately, and 4) to detect anomaly by аn extension DMX for SQL and correct the education process for students located on the border of the cluster.
Highlights
The vast amount of data needs special tools to analyze and extract the hidden knowledge
This paper investigates of the Educational Data Mining (EDM) using mining techniques as clustering, classification, association rules detection, and anomaly detection, a case study of data collected from the industrial institute in Iraq, for possible to draw an individual learning trajectory, the verification of the individual characteristics
Methodology and Methods There are four EDM techniques applied to achieve the purpose for this paper: 1. Association rules detection: to understand the most closely related features that lead to the suc
Summary
The vast amount of data needs special tools to analyze and extract the hidden knowledge. Educational institutions like any other institutions, needs to analyze data in order to increase the number of graduates and improve the educational process as a whole, one of the most promising ways to achieve this goal is to apply data mining techniques on educational field. This paper investigates of the EDM using mining techniques as clustering, classification, association rules detection, and anomaly detection, a case study of data collected from the industrial institute in Iraq, for possible to draw an individual learning trajectory, the verification of the individual characteristics. It is considered a problem by itself and needs solutions [4]. We will answer the following questions: how can we preprocess the data, how to apply data mining methods on the dataset, and how can we benefit from the discovered knowledge
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