Data Mining
M. Ghodoosi; F. Mirsaeedi; H. Koosha
Abstract
Background and Objectives: Nowadays, significant advancements in information technology and communication field in different societies are seen. Given that these advancements, universities as a leading institution in the field of science, have moved towards electronic processes in the management of education ...
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Background and Objectives: Nowadays, significant advancements in information technology and communication field in different societies are seen. Given that these advancements, universities as a leading institution in the field of science, have moved towards electronic processes in the management of education and educational environments, there are databases with a large amount of information. By analyzing this massive data of educational systems, methods can be provided to improve the educational status of students. Educational data mining has sought to discover the knowledge contained in the data of the educational system. One of the applications of educational data mining is to predict students' academic performance. Predicting students' academic performance and providing useful solutions is of particular importance in the success of educational systems and can help managers make the right decisions to increase the efficiency of the educational system and better student performance. The purpose of this paper is to identify the effective indicators on academic performance, predict students' academic status using data mining techniques, and finally present a new trend for modifying unit selection and educational strategies to increase the efficiency of the education system. Methods: steps of this research are determined according to CRISP model. In current research, Databases containing 9 datasets of specialized courses in industrial engineering were used. The students' grade was bachelor's degree. Indicators affecting student performance have been identified based on previous researches and expert opinions. Demographic data and academic records of undergraduate students are entered in database. After data preprocessing, 13 attributes are selected, different models were proposed to predict student's academic status in the next semester. Then, a comparison between the results of 4 different algorithms has been done. Findings: All 13 attributes are identified to be effective according to information gain and gain ratio. This 13 attributes as follow: GPA, Total passed units, Number of conditional terms, Type of admission, Marital status, Gender, University admission year, Living place , Age, Current semester, Prerequisite course score, instructor of the course, Repeat the course. Between of 4 considered models, the Logit Boost algorithm is known as the best model in categorizing in two class and multi-class according to the accuracy rate and ROC. Conclusion: Because of acceptable performance of data mining algorithms, the use of these algorithms in predicting student performance is appropriate and the proposed model can be used as a support tool for decision making in educational systems. Finally, according to the obtained results and the opinion of academic experts, the unit selection process was redesigned. The proposed model can be used as a decision support tool in educational systems. Finally, due to the results obtained and the opinions of the academic experts, the process of unit selection was redesigned. The presented process uses the available data in educational systems and data mining science, provides useful knowledge to decision-makers to make the right and appropriate decision. Decision makers can make appropriate decisions by examining the predictions made by the data mining algorithm and obtaining useful information, in order to make the educational system more efficient.
Data Mining
H. Koosha; S. Dangkoub; A. Barzanooni
Abstract
Background and Objective: Student mental health data has been recorded in the information systems of the universities across the country for several years, and due to its high volume, conventional statistical and psychoanalytic methods to predict patterns and factors affecting students' mental health ...
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Background and Objective: Student mental health data has been recorded in the information systems of the universities across the country for several years, and due to its high volume, conventional statistical and psychoanalytic methods to predict patterns and factors affecting students' mental health are not effective. This is where data mining technology comes in handy and helps to predict and identify those at high risk based on the recorded data set of students 'physical and especially mental health status, and to make appropriate and timely decisions to improve students' condition. One of the main objectives of every managers of educational centers is making improvements in students’ educational performance. Besides the educational factors, physical and mental health is considerable which has a significant effect on students’ behavior. Therefore, some rules and patterns are required to make the best decisions, based on the prediction of students’ mental health state. This paper proposes a data mining approach for analyzing and extracting patterns in terms of new students’ mental health, which means whether they need to visit a psychologist. Our effort was on extracting hidden rules in new students’ mental health examination by employing classification approach. Methods:Techniques used in this study are decision tree, rule based classifier, neural network, logistic regression and support vector machine. Moreover, a parameter tuning process is done for all the techniques mentioned and the results presents the list of symptoms of individuals who need detailed examination. Findings:The results of the research represent that one can predict the status of students’ mental helath based on propsed model. One of the outcomes of decision tree is that if a person severely feels disappointed or seems to be obsessive by others, or feels that life is worthless, definitely a consultaion is needed. Conclusion: Considering that most of the existing research in the field of health data mining have focused on physical health, it is suggested that for future studies, all levels of health, i.e dimensions of students' health, including physical, social and spiritual health, as well as a combination of these dimensions be considered. In addition, a review of the various approaches and techniques appropriate to the psychological data set should be conducted with the aim of creating an appropriate classification for the existing techniques in this field. It is also suggested that the present data set or similar data sets (student health monitoring information) be examined with other classification techniques and the results be compared with the results of the present study. In general, it is suggested that data mining technology be used to extract hidden patterns in the mental health data set of school students at different levels of education, office workers and organizations. Finally, it is recommended that future research in this field first implement the clustering approach on the psychological data set and then use the classification and forecasting approaches.
