Prediction of students' success by applying data mining algorithams (original) (raw)
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Data Mining Approach for Making Prediction of Students Success
Proceedings of the 2013 International Conference on Advanced ICT, 2013
Although data mining represents the computational method of data processing, its use in education is still relatively new, i.e. its use is intended for discovering implicit, previously unknown, and useful knowledge out of existing data with an aim to make quality decisions in function of improvement of education system. The study was conducted by surveying the population of high school students in Tuzla Canton, Bosnia and Herzegovina (sample included about 10% of the student population, i.e. 1645 student). Using four different data mining algorithms the aim was to develop a model which can derive the conclusion of secondary level students' success.
Predicting Student's Performance in Education using Data Mining Techniques
International Journal of Computer Applications, 2019
In this data world, where users spawn their digital footprint and generate a huge amount of unstructured data continuously with each activity, data mining techniques help in discovering interesting patterns, establishing relationships and unravel the problems through analysis, in different aspects of life. Educational data mining is a multidisciplinary research area, in which data from various educational organizations, is explored and made operational, for various facets concerned with the students, like predicting academic performance, analyse the learning pattern, solving e-learning issues, predict employability, visualize the critical courses affecting performance, investigate the reasons for student's failure or drop out and thus make data-driven decisions to improve the institutions standards. This paper provides a brief overview of Data Mining tools and techniques, and its encroachment in the educational domain. It also proposes a simple framework using different variables which helps in predicting student's academic success using two different algorithms: Decision Trees and Bayesian Network. Finally, a comparative analysis of accuracy is done. The results show that Bayesian Network outperforms the Decision Tress and gives better accuracy.
THE ESTIMATION OF STUDENTS' ACADEMIC SUCCESS BY DATA MINING METHODS
Data mining is a process of getting out useful information from data stacks. It is possible to classify and group the data and to get out association rules between the data mining techniques. One of the most common application scopes is to use classification algorithms that estimate the future events by past experiences. Within this scope, on the student data warehouse, new applications which can make inferences for the future are developed.
The Estimations of Students' Academic Success by Data Mining Methods
— Data mining is a process of getting out useful information from data stacks. One of the most common application areas is to use classification of algorithms that estimate the future events by past experiences. In this context, in order to predict future events, a data warehouse is created by using the background of students which includes demographic, personal, school, and course information of students. On this data warehouse by using classification algorithms, new applications which can make inferences for the future could be developed. Aims of this study are to create student data warehouse which can be used data mining algorithms, to improve an early warning system that may estimate students' the future academic successes for students and also for their families and to find out primary factors affecting their academic success.
This research work has investigated the potential applicability of data mining technology to predict student success and failure cases on University students' datasets. CRISP-DM (Cross Industry Standard Process for Data mining) is a data mining methodology to be used by the research. Classification and prediction data mining functionalities are used to extract hidden patterns from students' data. These patterns can be seen in relation to different variables in the students' records. The classification rule generation process is based on the decision tree and Bayes as a classification technique and the generated rules were studied and evaluated. Data collected from MS_EXCEL files, and it has been preprocessed for model building. Models were built and tested by using a sample dataset of 11,873 regular undergraduate students. Analysis is done by using WEKA 3.7 application software. The research results offer a helpful and constructive recommendations to the academic planners in universities of learning to enhance their decision making process. This will also aid in the curriculum structure and modification in order to improve students' academic performance. Students able to decide about their field of study before they are enrolled in specific field of study based on the previous experience taken from the researchfindings. The research findings indicated that EHEECE (Ethiopian Higher Education Entrance Certificate Examination) result, Sex, Number of students in a class, number of courses given in a semester, and field of study are the major factors affecting the student performances. So, on the bases of the research findings the level of student success will increase and it is possible to prevent educational institutions from serious financial strains.
The prediction of students' academic performance using classification data mining techniques
Applied Mathematical Sciences, 2015
Data Mining provides powerful techniques for various fields including education. The research in the educational field is rapidly increasing due to the massive amount of students' data which can be used to discover valuable pattern pertaining students' learning behaviour. This paper proposes a framework for predicting students' academic performance of first year bachelor students in Computer Science course. The data were collected from 8 year period intakes from July 2006/2007 until July 2013/2014 that contains the students' demographics, previous academic records, and family background information. Decision Tree, Naïve Bayes, and Rule Based classification techniques are applied to the students' data in order to produce the best students' academic performance prediction model. The experiment result shows the Rule Based is a best model among the other techniques by receiving the highest accuracy value of 71.3%. The extracted knowledge from prediction model will be used to identify and profile the student to determine the students' level of success in the first semester.
Data Mining Applications: A comparative Study for Predicting Student's performance
2012
is a multidisciplinary area focusing upon methodologies for extracting useful knowledge from data and there are several useful KDD tools to extracting the knowledge. This knowledge can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Data mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used supervised learning methods used for data exploration based on divide & conquer technique. This paper discusses use of decision trees in educational data mining. Decision tree algorithms are applied on students' past performance data to generate the model and this model can be used to predict the students' performance. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling.
International Journal of Engineering Business Management, 2018
The Sultanate of Oman is one of the first countries in the Middle East to utilize technology in the management of the education process. Over time, education data have accumulated, and at the present, large volumes of data with numerous types of statistics have been collected as operational data. This research article takes the advantage of these data and applied predictive data mining approaches to study the performance of general education diploma students (year 12 of school). The decision tree as a classification method has been applied and a prediction model is constructed with ability to show relatively high accuracy of students' performance prior to year 12 of school, using 30% test data of nearly 6000 student's records. The significant variables that influence students' performance are identified which would help stakeholders and decision makers plan ahead and have more time to prepare for decisions.
A Comparative Study of Predicting Student’s Performance by use of Data Mining Techniques
2018
Educational systems need innovative ways to improve quality of education to achieve the best results and decrease the failure rate. Educational Data Mining (EDM) has boomed in the educational systems recently as it enables to analyze and predict student performance so that measures can be taken in advance. Due to lack of prediction accuracy, improper attribute analysis, and insufficient datasets, the educational systems are facing difficulties and challenges exist to effectively benefit from EDM. In order to improve the prediction process, a thorough study of literature and selection of the best prediction technique is very important. The main objective of this paper is to present a comparative study of various recently used data mining techniques, classification algorithms, their impact on datasets as well as the prediction attribute’s result in a clear and concise way. The paper also identifies the best attributes that will help in predicting the student performance in an efficie...
2020
According to modern era education is the key to achieve success in the future; it develops a human personality, thoughts, and social skills. The purpose of this research work is to focus on educational data mining (EDM) through machine learning algorithms. EDM means to discover hidden knowledge and pattern about student's performance. Machine learning can be useful to predict the learning outcomes of students. From last few years, several tools have been used to judge the student's performance from different points of view like the student's level, objectives, techniques, algorithms, and different methods. In this paper, predicting and analyzing student performance in secondary school is conducted using data mining techniques and machine learning algorithms such as Naive Bayes, Decision Tree algorithm J48, and Logistic Regression. For this the collection of dataset from "Secondary School" and then filtration is applying on desired values using WEKA, tool.