Analyzing Student’s Academic Performance Based on Data Mining Approach

Authors

  • Kalaivani S Information Technology, Dr Mahalingam College of Engineering and Technology, Pollachi, India, Author
  • Priyadharshini B, Information Technology, Dr Mahalingam College of Engineering and Technology, Pollachi, India, Author
  • Selva Nalini 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. Keywords— Data mining, Balanced Boosting, Educational data mining, Re-weight enhanced boosting, Ada-boosting I. INTRODUCTION A. Overview Data mining is extraction of interesting patterns or knowledge from huge amount of data. It is a step in knowledge discovery process. The major components of any data mining system are data source, data warehouse server, data mining engine, pattern evaluation module, graphical user interface and knowledge base. B. Educational Data Mining Educational Data Mining(EDM) is an emerging discipline, concerned with developing methods for exploring the unique types of data and using those methods to better understand the educational field entities. C. Data Preprocessing Data preprocessing is a data mining technique that involves transforming raw data into an understandable format. D. Class Imbalance Problem Class imbalance problem exist in most of the real time data set. In imbalanced datasets, the classes having more examples are majority classes and the ones having fewer examples are minority classes. The class imbalance problem typically occurs in a classification problem. When the algorithm is applied only the majority class instances are considered for classification neglecting the minority class. This reduces the overall accuracy of the result which affects the future predictions. Hence this problem, existing Manuscript received January 20, 2017. Kalaivani. S, Information Technology, Dr Mahalingam College of Engineering and Technology, Pollachi, India, 9487443674. Priyadharshini. B, Information Technology, Dr Mahalingam College of Engineering and Technology, Pollachi, India, 9892345135 Selva Nalini. B Information Technology, Dr Mahalingam College of Engineering and Technology, Pollachi, India Author

Keywords:

Data mining, Balanced Boosting, Educational data mining, Re-weight enhanced boosting, Ada-boosting

Abstract

 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.  

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References

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.

AsmaaElbadrawy, AgoritsaPolyzou, ZhiyunRen, Mackenzie Sweeney, George Karypis, HuzefaRangwala,”Predicting Student Performance Using Personalized Analytics”, IEEE , pp. 61-69, April 2016.

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.

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

Harwatia*,ArditaPermataAlfiania, FebrianaAyuWulandari, ”Mapping Student‘s Performance Based on Data Mining Approach", Science Direct,pp. 173 – 177, 2015

ManolisChalaris*,StefanosGritzalis, ManolisMaragoudakis, Cleo Sgouropoulou and AnastasiosTsolakidis,”Improving Quality of Educational Processes Providing New Knowledge using Data Mining Techniques”, Science Direct,pp. 390 –

,2014

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

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

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Published

2017-01-01

Issue

Vol. 5 No. 1 (2017): International Journal of Innovative Research in Computer Science & Technology(jan )2017

Section

Articles

How to Cite

Analyzing Student’s Academic Performance Based on Data Mining Approach . (2017). International Journal of Innovative Research in Computer Science & Technology, 5(1), 194–197. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/13508