Comparison of Predicting Student’s Performance using Machine Learning Algorithms

Comparison of Predicting Student’s Performance using Machine Learning Algorithms

Автор: V. Vijayalakshmi, K. Venkatachalapathy

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 12 vol.11, 2019 года.

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Predicting the student performance is playing vital role in educational sector so that the analysis of student’s status helps to improve for better performance. Applying data mining concepts and algorithms in the field of education is Educational Data Mining. In recent days, Machine learning algorithms are very much useful in almost all the fields. Many researchers used machine learning algorithms only. In this paper we proposed the student performance prediction system using Deep Neural Network. We trained the model and tested with Kaggle dataset using different algorithms such as Decision Tree (C5.0), Naïve Bayes, Random Forest, Support Vector Machine, K-Nearest Neighbor and Deep neural network in R Programming and compared the accuracy of all other algorithms. Among six algorithms Deep Neural Network outperformed with 84% as accuracy.

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Educational data mining, Decision Tree, K-Nearest Neighbor, Neural Network, Random Forest, Support Vector Machine

Короткий адрес: https://sciup.org/15017115

IDR: 15017115   |   DOI: 10.5815/ijisa.2019.12.04

Список литературы Comparison of Predicting Student’s Performance using Machine Learning Algorithms

  • Marquec-vera, C. Cano, A. Romero, C., and Ventura, S. Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data. Applied Intelligence, 2013, 1:1-16.
  • Han, J., Kamber, M., and Pei, J. (2013). Data Mining Concepts and Techniques, third edition. Morgan Kaufmann.
  • Cristobal Romero (2010), “Educational Data Mining: A Review of the State-of-the-Art”, IEEE Transactions on systems, man and cybernetics- Part C: Applications and Reviews vol. 40 issue 6, pp 601 – 618.
  • Baker, R.S., Corbett, A.T., Koedinger, K.R. (2004), “Detecting Student Misuse of Intelligent Tutoring Systems”. Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 531-540.
  • Merceron, A., Yacef, K. (2003),” A web-based tutoring tool with mining facilities to improve learning and teaching”. Proceedings of the 11th International Conference on Artificial Intelligence in Education, 201–208
  • Beck, J., & Woolf, B. (2000).” High-level student modelling with machine learning”. Proceedings of the 5th International Conference on Intelligent Tutoring Systems, 584–593.
  • Kaur, Parneet, Manpreet Singh, and Gurpreet Singh Josan. "Classification and prediction based data mining algorithms to predict slow learners in education sector." Procedia Computer Science 57 (2015): 500-508.
  • Lakshmi, T. M., Martin, A., Begum, R. M., & Venkatesan, V. P. (2013). An analysis on performance of decision tree algorithms using student's qualitative data. International Journal of Modern Education and Computer Science, 5(5), 18.
  • Ogwoka, Thaddeus Matundura, Wilson Cheruiyot, and George Okeyo. "A Model for predicting Students’ Academic Performance using a Hybrid K-means and Decision tree Algorithms." International Journal of Computer Applications Technology and Research 4.9 (2015): 693-697.
  • Kabra, R. R., and R. S. Bichkar. "Performance prediction of engineering students using decision trees." International Journal of computer applications 36.11 (2011): 8-12.
  • Kumar, S. Anupama. "Efficiency of decision trees in predicting student’s academic performance." (2011).
  • Mesarić, Josip, and Dario Šebalj. "Decision trees for predicting the academic success of students." Croatian Operational Research Review 7.2 (2016): 367-388.
  • Osmanbegović, Edin, and Mirza Suljić. "Data mining approach for predicting student performance." Economic Review 10.1 (2012): 3-12.
  • Livieris, Ioannis E., Tassos A. Mikropoulos, and Panagiotis Pintelas. "A decision support system for predicting students’ performance." Themes in Science and Technology Education 9.1 (2016): 43-57.
  • Kolo, Kolo David, Solomon A. Adepoju, and John Kolo Alhassan. "A decision tree approach for predicting students academic performance." International Journal of Education and Management Engineering 5.5 (2015): 12.
  • Al-Barrak, Mashael A., and Muna Al-Razgan. "Predicting students final GPA using decision trees: a case study." International Journal of Information and Education Technology 6.7 (2016): 528.
  • Ruby, Jai, and K. David. "Predicting the Performance of Students in Higher Education Using Data Mining Classification Algorithms-A Case Study." IJRASET International Journal for Research in Applied Science & Engineering Technology 2 (2014).
  • Ramesh, V. A. M. A. N. A. N., P. Parkavi, and K. Ramar. "Predicting student performance: a statistical and data mining approach." International journal of computer applications 63.8 (2013): 35-39.
  • Guleria, Pratiyush, Niveditta Thakur, and Manu Sood. "Predicting student performance using decision tree classifiers and information gain." 2014 International Conference on Parallel, Distributed and Grid Computing. IEEE, 2014.
  • Cheewaprakobkit, Pimpa. "Predicting student academic achievement by using the decision tree and neural network techniques." catalyst 12.2 (2015): 34-43.
  • Khan, Bashir, Malik Sikandar Hayat Khiyal, and Muhammad Daud Khattak. "Final grade prediction of secondary school student using decision tree." International Journal of Computer Applications 115.21 (2015).
  • Sorour, Shaymaa E., and Tsunenori Mine. "Building an Interpretable Model of Predicting Student Performance Using Comment Data Mining." 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2016.
  • Buniyamin, Norlida, Usamah bin Mat, and Pauziah Mohd Arshad. "Educational data mining for prediction and classification of engineering students achievement." 2015 IEEE 7th International Conference on Engineering Education (ICEED). IEEE, 2015.
  • Quadri, Mr MN, and N. V. Kalyankar. "Drop out feature of student data for academic performance using decision tree techniques." Global Journal of Computer Science and Technology (2010).
  • Christian, Tjioe Marvin, and Mewati Ayub. "Exploration of classification using NBTree for predicting students' performance." 2014 International Conference on Data and Software Engineering (ICODSE). IEEE, 2014.
  • Wook, M., Yahaya, Y. H., Wahab, N., Isa, M. R. M., Awang, N. F., & Seong, H. Y. (2009, December). Predicting NDUM student's academic performance using data mining techniques. In 2009 Second International Conference on Computer and Electrical Engineering (Vol. 2, pp. 357-361). IEEE.
  • Shaleena, K. P., and Shaiju Paul. "Data mining techniques for predicting student performance." 2015 IEEE International Conference on Engineering and Technology (ICETECH). IEEE, 2015.
  • Pandey, Mrinal, and Vivek Kumar Sharma. "A decision tree algorithm pertaining to the student performance analysis and prediction." International Journal of Computer Applications 61.13 (2013).
  • Guo, Bo, Rui Zhang, Guang Xu, Chuangming Shi, and Li Yang. "Predicting students performance in educational data mining." In 2015 International Symposium on Educational Technology (ISET), pp. 125-128. IEEE, 2015.
  • Bendangnuksung and Dr. Prabu P, “Students' Performance Prediction Using Deep Neural Network”, Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 2 (2018) pp. 1171-1176.
  • Ciaburro, Giuseppe, and Balaji Venkateswaran. Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles. Packt Publishing Ltd, 2017.
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