Volume- 5
Issue- 1
Year- 2017
DOI: 10.21276/ijircst.2017.5.1.4 | DOI URL: https://doi.org/10.21276/ijircst.2017.5.1.4
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
Article Tools: Print the Abstract | Indexing metadata | How to cite item | Email this article | Post a Comment
Kalaivani. S , Priyadharshini. B , Selva Nalini
Career building is the most cherished part of every engineering student. For an engineering graduate it is necessary to have immense knowledge in their domain to get placed in a reputed company. Data Mining is used to gain knowledge, find the hidden information and also this system applies data mining techniques to the academic dataset. The Academic data includes the Internal (CCET 1, CCET2 and CCET3) marks and the Assignment marks. The final semester marks are predicted from the analyzed result of each student. In order to increase the accuracy, this system introduces reweight enhanced boosting algorithm.
[1] Amirah Mohamed Shahiria, WahidahHusaina, Nur‘aini Abdul Rashida,”A Review on Predicting Student‘s Performance using Data Mining Techniques”, Science Direct,pp. 414 – 422, 2015.
[2] AsmaaElbadrawy, AgoritsaPolyzou, ZhiyunRen, Mackenzie Sweeney, George Karypis, HuzefaRangwala,”Predicting Student Performance Using Personalized Analytics”, IEEE , pp. 61-69, April 2016.
[3] Camilo Ernesto LópezGuarín, Elizabeth León Guzmán, and Fabio A. González,”A Model to Predict Low Academic Performanceat a Specific Enrollment Using Data Mining”, IEEE RevistaIberoamericana De Tecnologias Del Aprendizaje, vol. 10, pp. 3, August 2015.
[4] GhadaBadra,b*, AfnanAlgobaila, HanadiAlmutairia, ManalAlmuterya, “Predicting Students‘ Performance in University Courses: A Case Study and Tool in KSU Mathematics Department”, Science Direct,pp. 80-89, 2016
[5] Harwatia*,ArditaPermataAlfiania, FebrianaAyuWulandari, ”Mapping Student‘s Performance Based on Data Mining Approach", Science Direct,pp. 173 – 177, 2015
[6] ManolisChalaris*,StefanosGritzalis, ManolisMaragoudakis, Cleo Sgouropoulou and AnastasiosTsolakidis,”Improving Quality of Educational Processes Providing New Knowledge using Data Mining Techniques”, Science Direct,pp. 390 – 397,2014
[7] Syed TanveerJishan, Raisul Islam Rashu, NaheenaHaque and Rashedur M Rahman*, “Improving accuracy of students‘ final grade prediction model using optimal equal widthbinning and synthetic minority over-sampling technique”,Springer, pp. 2:1,2015
[8] Yannick Meier, JieXu, OnurAtan, and Mihaela van der Schaar,”Predicting Grades”, IEEE, pp. 20-29, 2014
[9] C.Romeo, M-I Lopez, J-M Luna and S.Venture, “Classification via clustering for predicting final marks based on student participication in forums”, Comput Ed., vol 68, pp 458-472, 2015
Information Technology, Dr Mahalingam College of Engineering and Technology, Pollachi, India, 9487443674.
No. of Downloads: 10 | No. of Views: 3497
Anmol Chauhan, Sana Rabbani, Devendra Agarwal, Nikhat Akhtar, Yusuf Perwej.
July 2024 - Vol 12, Issue 4
Dr S. A. Talekar, Shravani A. Lajurkar, Divya S. Patil, Rutika A. Benke, Pranjal A. Kunde.
May 2024 - Vol 12, Issue 3
Dr. Deepika Rani.
May 2024 - Vol 12, Issue 3