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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Issue: June 2023

Volume 8 | Issue 6

Impact factor: 8.15

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Authors Name: S.G.Bodake , Suvarnalata Hadge , Bhavika Patil , Swapnali Ahire , Pushkar Jadhav
Unique Id: IJSDR2305139
Published In: Volume 8 Issue 5, May-2023
Abstract: This Paper aimed at developing on Online College Prediction System that is of importance to the college as well as students. In recent time for student to select college for higher education is very difficult as per their aggregate percentage. After the complete of diploma for higher education admission limited seats are available. Student don’t know for his/her which college is applicable. This study focuses on ways to support student in admissions decision making using data mining techniques to predict colleges based on previous cut-off performance at institute. In college prediction system based on valid and reliable cut- off criteria is very important to select colleges for higher education. Every college enroll their college in college module then admin have a right to approve or disapprove if the college is centralized then admin approved this college if college decentralized then admin disapprove this college. centralized college is show to student as per their percentage. Several data higher learning institutions, student percentage is the factor most important to a university’s quality. EDM is currently the technique most commonly used by researchers to evaluate and predict student performance due to its significance in decision making. To avoid this type of condition, student required some helping platform which gives them correct knowledge or information about college. In this context, this study focuses on supporting Students in making admissions decisions through the application of data mining techniques to better predict College before designation. Second, through a correlation coefficient analysis, we determine the relation between college cut-off criteria and student’s aggregate of diploma. We also identify admission criterion that most accurately predicts college cut-off performance so that decision makers can assign more weight to this particular criterion. This would support institute decision makers as they set efficient admissions principles. This is because they use only formal statistical methods rather than new and efficient predictive techniques such as Educational Data Mining (EDM), which is the most popular technique to evaluate and predict college cut-off performance. EDM is the process of extracting useful details and models from a huge educational database, which can then be used to predict students’ performance.
Keywords: Data mining techniques, Educational data mining, College Cut-off Prediction, Online System, Database, Student Aggregate
Cite Article: "A WEB BASED APPLICATION FOR TUTORING SUPPORT IN HIGHER EDUCATION USING EDUCATIONAL DATA MINING", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 5, page no.915 - 920, May-2023, Available :http://www.ijsdr.org/papers/IJSDR2305139.pdf
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Publication Details: Published Paper ID: IJSDR2305139
Registration ID:206405
Published In: Volume 8 Issue 5, May-2023
DOI (Digital Object Identifier):
Page No: 915 - 920
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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