Predicting Students’ Academic Drop Out and Failures Using Data Mining Techniques (original) (raw)
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Dropout-Permanence Analysis of University Students Using Data Mining
Springer, 2020
Dropout is a rejection method present in every educational system, related to the various selection processes, academic performance, and the efficiency of the system in general, that is, the result of the combination and effect of different variables. In this sense, the dropout of university students related to their academic performance is a matter of concern since several years ago. Academic information is analyzed in order to identify factors that influence students´ dropout at the University of Mumbai, India, by using a data mining technique. The data source contains information provided to the entrance (personal and educational background) and that is generated during the study period. The data selection and cleansing are made using different criteria of representation and implementation of classification algorithms such as decision trees, Bayesian networks, and rules. the following factors are identified as influential variables in the desertion: approved courses, quantity and results of attended courses, origin and age of entry of the student. Through this process, it was possible to identify the attributes that characterize the dropout cases and their relationship with the academic performance, especially in the first year of the career.
Survey on Students’ Academic Failure and Dropout using Data Mining Techniques
International Journal of Advances in Computer Science and Technology
Educational data mining is in the habit of learn the data available in the field of education and show up the hidden knowledge from it. Classification methods like decision trees, machine learning, rule mining, etc can be applied on the educational data for forecasting the student’s behavior, performance of them in examination etc. This prediction will well helpful for tutors to classify the weak students and help them to score improved marks. The classification approach is applied on student’s internal assessment data to predict their performance in the final exam. The result of the classification categorized the number of students who are to be expected to fail or pass. The outcome result is given to the tutor and steps were taken to improve the performance of the students who were predicted as fail in the examination. After the statement of the results in the final examination the marks acquired by the students are provide into the system and the results were investigated. The proportional analysis results states that the prediction has helped the weaker students to improve and brought out betterment in the result. The algorithm is also analyzed by duplicating the same data and the result of the duplication brings no much change in predicting the student’s outcome. The goal of this survey is presented the several data mining techniques in determining of student failure. This article provides a review of the available literature on Educational Data mining, Classification method and different feature selection techniques that we should apply on Student dataset
A Brief Review about Educational Data Mining applied to Predict Student’s Dropout
Anais da V Escola Regional de Sistemas de Informação do Rio de Janeiro, 2018
Educational Data Mining (EDM) may be a very useful technique as much to understand student behavior as to plan and manage government investments in education. EDM helps to analyzes and to expose the hidden information of educational data. Particularly, an important application of EDM is to predict or analyze the students' dropout. This problem affects several educational institutions in Brazil and the world, and identify its origin has been a relevant research motivator. This paper presents a brief introduction about EDM applied to predict students' dropout and analyzes some important articles during the period from 2013 to 2018.
Literature Survey on Educational Dropout Prediction
Educational Data Mining (EDM) is one of the crucial application areas of data mining which helps in predicting educational dropout and hence provides timely help to students. In Indian context, predicting educational dropouts is a major problem. By implementing EDM, we can predict the learning habits of the student. At present EDM has not been introduced at higher education level. Due to this we cannot recognize the genuine problems of students during their education. The objective of this analysis is to find the existing gaps in predicting educational dropout and find the missing attributes if any, which my further contribute for better prediction. After that we try to find the best attributes and DM techniques which are frequently used for dropout prediction. Based on the combination of missing attribute and best attribute of student data thus far, a new algorithm can be tested which may overcome the shortcomings of previous work done.
Mining Educational Data Using Classification to Decrease Dropout Rate of Students
arXiv preprint arXiv:1206.3078, 2012
In the last two decades, number of Higher Education Institutions (HEI) grows rapidly in India. Since most of the institutions are opened in private mode therefore, a cut throat competition rises among these institutions while attracting the student to got admission. This is the reason for institutions to focus on the strength of students not on the quality of education. This paper presents a data mining application to generate predictive models for engineering student's dropout management. Given new records of incoming students, the predictive model can produce short accurate prediction list identifying students who tend to need the support from the student dropout program most. The results show that the machine learning algorithm is able to establish effective predictive model from the existing student dropout data.
JOURNAL OF UNIVERSITY OF BABYLON for pure and applied sciences, 2019
The student's retention rate is one of the challenging issues that represents the quality of the university. A high dropout rate of students affects not only the reputation of the university but also the students' career in the future. Therefore, there is a need of student dropout analysis in order to improve academic plan and management to reduce students drop out from the university as well as to enhance the quality of the higher education system. Data mining technique provides powerful methods for the analysis and the prediction of the dropout. This paper proposes a model for predicting students' dropout using the dataset from the representative of the largest public university in the Southen part of Thailand. In this study, data from Faculty of Science, Prince of Songkla University was collected from academic year of 2013 to 2017. The experiment result shows that JRip rule induction is the best technique to generate a prediction model receiving the highest accuracy value of 77.30%. The results highlight the potential prediction model that can be used to detect the early state of dropping out of the student which the university can provide supporting program to improve the student retention rate.
Data Mining in Higher Education : University Student Dropout Case Study
International Journal of Data Mining & Knowledge Management Process, 2015
In this paper, we apply different data mining approaches for the purpose of examining and predicting students' dropouts through their university programs. For the subject of the study we select a total of 1290 records of computer science students Graduated from ALAQSA University between 2005 and 2011. The collected data included student study history and transcript for courses taught in the first two years of computer science major in addition to student GPA , high school average , and class label of (yes ,No) to indicate whether the student graduated from the chosen major or not. In order to classify and predict dropout students, different classifiers have been trained on our data sets including Decision Tree (DT), Naive Bayes (NB). These methods were tested using 10-fold cross validation. The accuracy of DT, and NlB classifiers were 98.14% and 96.86% respectively. The study also includes discovering hidden relationships between student dropout status and enrolment persistence by mining a frequent cases using FP-growth algorithm.
Educational Data Mining to Reduce Student Dropout Rate by Using Classification
The Educational Data Mining is currently a growing research area with having emphasis on logical methods for especially educationally linked data in order to improve the system and quality of higher education institutions. Overall the Educational Data Mining aims to interact the relevant information from any educational data and further transform into a systematic and understandable knowledge for the sake of decision making. Classification techniques can be highly helpful in predicting student’s performance. The current study aimed to evaluate student performance by using different Decision Tree and Bayes algorithms. The results of the study affirm the usefulness and functionality of the prediction model and these facilitate the institutions to identify weak students having enrollment status at risk and student needing further help. The study found highest accuracy in Naive Bayes among four algorithms as above ninety percent. Naive Bayes was concluded to be the best algorithm and execution of proposed model confirmed the claim.
Mining Educational Data Using Classification to Decrease
2012
In the last two decades, number of Higher Education Institutions (HEI) grows rapidly in India. Since most of the institutions are opened in private mode therefore, a cut throat competition rises among these institutions while attracting the student to got admission. This is the reason for institutions to focus on the strength of students not on the quality of education. This paper presents a data mining application to generate predictive models for engineering student's dropout management. Given new records of incoming students, the predictive model can produce short accurate prediction list identifying students who tend to need the support from the student dropout program most. The results show that the machine learning algorithm is able to establish effective predictive model from the existing student dropout data.