A Comparative Study of Predicting Student’s Performance by use of Data Mining Techniques

Aysha Ashraf, Sajid Anwer, Muhammad Gufran Khan

Abstract


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 efficient way.


Keywords


Data mining; Predicative models; Classification; Decision tree; Performance prediction.

Full Text:

PDF

References


. Kabakchieva, D., Predicting student performance by using data mining methods for classification. Cybernetics and information technologies, 2013. 13(1): p. 61-72.

. Osmanbegović, E. and M. Suljić, Data mining approach for predicting student performance. Economic Review, 2012. 10(1).

. Käser, T., N.R. Hallinen, and D.L. Schwartz. Modeling exploration strategies to predict student performance within a learning environment and beyond. in Proceedings of the Seventh International Learning Analytics & Knowledge Conference. 2017. ACM.

. Marquez-Vera, C., C.R. Morales, and S.V. Soto, Predicting school failure and dropout by using data mining techniques. Tecnologias del Aprendizaje, IEEE Revista Iberoamericana de, 2013. 8(1): p. 7-14.

. Rovira, S., E. Puertas, and L. Igual, Data-driven system to predict academic grades and dropout. PloS one, 2017. 12(2): p. e0171207.

. Ahmed, A.B.E.D. and I.S. Elaraby, Data Mining: A prediction for Student's Performance Using Classification Method. World Journal of Computer Application and Technology, 2014. 2(2): p. 43-47.

. Tamhane, A., et al. Predicting student risks through longitudinal analysis. in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 2014. ACM.

. Daud, A., et al. Predicting Student Performance using Advanced Learning Analytics. in Proceedings of the 26th International Conference on World Wide Web Companion. 2017. International World Wide Web Conferences Steering Committee.

. Adhatrao, K., et al., Predicting Students' Performance using ID3 and C4. 5 Classification Algorithms. arXiv preprint arXiv:1310.2071, 2013.

. Aher, S.B. and L. Lobo. Applicability of data mining algorithms for recommendation system in e-learning. in Proceedings of the International Conference on Advances in Computing, Communications and Informatics. 2012. ACM.

. Ikbal, S., et al., On early prediction of risks in academic performance for students. IBM Journal of Research and Development, 2015. 59(6): p. 5: 1-5: 14.

. Ktona, A., D. Xhaja, and I. Ninka. Extracting Relationships between Students' Academic Performance and Their Area of Interest Using Data Mining Techniques. in Computational Intelligence, Communication Systems and Networks (CICSyN), 2014 Sixth International Conference on. 2014. IEEE.

. Márquez-Vera, C., et al., Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data. Applied intelligence, 2013. 38(3): p. 315-330.

. Mgala, M. and A. Mbogho. Data-driven intervention-level prediction modeling for academic performance. in ICTD. 2015.

. Shahiri, A.M. and W. Husain, A Review on Predicting Student's Performance Using Data Mining Techniques. Procedia Computer Science, 2015. 72: p. 414-422.

. Bunkar, K., et al. Data mining: Prediction for performance improvement of graduate students using classification. in Wireless and Optical Communications Networks (WOCN), 2012 Ninth International Conference on. 2012. IEEE.

. Jishan, S.T., et al., Improving accuracy of students’ final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique. Decision Analytics, 2015. 2(1): p. 1.

. Mayilvaganan, M. and D. Kalpanadevi. Comparison of classification techniques for predicting the performance of students academic environment. in Communication and Network Technologies (ICCNT), 2014 International Conference on. 2014. IEEE.

. Ramesh, V., P. Parkavi, and K. Ramar, Predicting student performance: a statistical and data mining approach. International journal of computer applications, 2013. 63(8).

. Elakia, G. and N.J. Aarthi, Application of data mining in educational database for predicting behavioural patterns of the students. Elakia et al,/(IJCSIT) International Journal of Computer Science and Information Technologies, 2014. 5(3): p. 4649-4652.

. Mishra, T., D. Kumar, and S. Gupta. Mining Students' Data for Prediction Performance. in Advanced Computing & Communication Technologies (ACCT), 2014 Fourth International Conference on. 2014. IEEE.

. Arsad, P.M., N. Buniyamin, and J.-l.A. Manan. A neural network students' performance prediction model (NNSPPM). in Smart Instrumentation, Measurement and Applications (ICSIMA), 2013 IEEE International Conference on. 2013. IEEE.

. Gray, G., C. McGuinness, and P. Owende. An application of classification models to predict learner progression in tertiary education. in Advance Computing Conference (IACC), 2014 IEEE International. 2014. IEEE.

. Yadav, S.K., B. Bharadwaj, and S. Pal, Mining Education data to predict student's retention: a comparative study. arXiv preprint arXiv:1203.2987, 2012.

. Meedech, P., N. Iam-On, and T. Boongoen, Prediction of Student Dropout Using Personal Profile and Data Mining Approach, in Intelligent and Evolutionary Systems. 2016, Springer. p. 143-155.


Refbacks

  • There are currently no refbacks.


 
 
  
 

 

  


About ASRJETS | Privacy PolicyTerms & Conditions | Contact Us | DisclaimerFAQs 

ASRJETS is published by (GSSRR).