Academic Performance Prediction for Success Rate Improvement in Higher Institutions of Learning : An Application of Data Mining Classification Algorithms (original) (raw)
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One main objective of higher education is to provide quality education to its students. One way to achieve the highest level of quality in the higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, and prediction about students' performance. The knowledge is hidden among the educational data set and is extractable through data mining techniques. The present paper is designed to justify the capabilities of data mining techniques in the context of higher education by offering a data mining model for the higher education system in the university. In this research, the classification task is used to evaluate student's performance, and as many approaches are used for data classification, the decision tree method is used here. By this, we extract data that describes students' summative performance at semester's end, helps to identify the dropouts and students who need special attention, and allows the teacher to provide appropriate advising/counseling.
Higher education is a crucial zone for any successful nation. All exploration and developments for the most part originates from advanced education. Top most profitable experts like designers, supervisors, researchers are originates from the channel of higher education. The supervision of the scholarly execution of designing/understudies is fundamental amid a beginning time of their educational module. In reality, their evaluations in particular center/real courses and additionally their combined General Point Average (GPA) are conclusive when relating to their capacity/condition to seek after higher examinations. Information mining joins machine learning, measurements and perception methods to find and concentrate learning. This paper proposes to apply data mining strategies to anticipate understudies' scholastic execution in higher instructive establishments. In this paper we additionally utilized information mining and factual strategies to enhance students' execution and...
Contributions from Data Mining to Study Academic Performance of Students of a Tertiary Institute
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Education-oriented data mining allows to predict determined type of factor or characteristic of a case, phenomenon or situation. In this article the mining models used are described and the main results are discussed. Mining models of clustering, classification and association are considered especially. In all cases seeks to determine patterns of academic success and failure for students, thus predicting the likelihood of dropping them or having poor academic performance, with the advantage of being able to do it early, allowing addressing action to reverse this situation. This work was done in 2013 with information on the years 2009 to 2013, students of the subject Operating Systems tertiary career Superior Technical Analyst (TSAP) Higher Institute of Curuzú Cuatiá (ISCC), Corrientes, Argentina.
Predicting Student’s Performance Using Data Mining Techniques: A Survey From 2002 To 2020
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Today, many educational institutions suffer from the issue of dropping out students, failing students, recognize poor students because of the lack of a proper framework for assessing and tracking the success and performance of students. This is one of the main challenges of the educational institution, since predicting the performance of students is difficult due to vast volumes of data in educational databases. Predicting student‘s performance at an educational institution is mostly useful in helping the institute management to make strategy and decision making related to improving student performance. Data Mining is one of the efficient methods for predicting student‘s performance in large educational databases. Data Mining is applied in the field of education to predict student‘s performance. Different data mining methods and techniques are used for predicting student‘s performance. This paper present a literature research on data mining methods used to predict student‘s performa...
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.
Data mining approach to predict academic performanceof students
BOHR International Journal of Computer Science
Powerful data mining techniques are available in a variety of educational fields. Educational research is advancingrapidly due to the vast amount of student data that can be used to create insightful patterns related to studentlearning. Educational data mining is a tool that helps universities assess and identify student performance. Well-known classification techniques have been widely used to determine student success in data mining. A decisiveand growing exploration area in educational data mining (EDM) is predicting student academic performance.This area uses data mining and automaton learning approaches to extract data from education repositories.According to relevant research, there are several academic performance prediction methods aimed at improvingadministrative and teaching staff in academic institutions.In the put-forwarded approach, the collected data set is preprocessed to ensure data quality and labeled studenteducation data is used to apply ANN classifiers, support v...
Predicting Student Academic Performance at Degree Level: A Case Study
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Universities gather large volumes of data with reference to their students in electronic form. The advances in the data mining field make it possible to mine these educational data and find information that allow for innovative ways of supporting both teachers and students. This paper presents a case study on predicting performance of students at the end of a university degree at an early stage of the degree program, in order to help universities not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them. The data of four academic cohorts comprising 347 undergraduate students have been mined with different classifiers. The results show that it is possible to predict the graduation performance in 4th year at university using only pre-university marks and marks of 1st and 2nd year courses, no socioeconomic or demographic features, with a reasonable accuracy. Furthermore courses that are indicators of particularly good or poor performance have been identified.