Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample

Authors

  • Bareen Haval Duhok Polytechnic University, Technical Institute of Administration, Dept. of Management Information System, Iraq - Kurdistan
  • Karwan Jameel Abdulrahman Duhok Polytechnic University, Technical College of Administration, Dept. of Information Technology Management, Iraq - Kurdistan
  • Araz Rajab Abrahim Duhok Polytechnic University, Technical College of Administration, Dept. of Information Technology Management, Iraq - Kurdistan

DOI:

https://doi.org/10.25007/ajnu.v10n3a875

Keywords:

Mining Educational Data, Models of Classification, Prediction, Data Set, Estimation

Abstract

This article presents the results of connecting an educational data mining techniques to the academic performance of students. Three classification models (Decision Tree, Random Forest and Deep Learning) have been developed to analyze data sets and predict the performance of students. The projected submission of the three classificatory was calculated and matched. The academic history and data of the students from the Office of the Registrar were used to train the models. Our analysis aims to evaluate the results of students using various variables such as the student's grade. Data from (221) students with (9) different attributes were used. The results of this study are very important, provide a better understanding of student success assessments and stress the importance of data mining in education. The main purpose of this study is to show the student successful forecast using data mining techniques to improve academic programs. The results of this research indicate that the Decision Tree classifier overtakes two other classifiers by achieving a total prediction accuracy of 97%.

Downloads

Download data is not yet available.

References

[1] D. J. Prajapati and J. H. Prajapati, "Handling Missing Values: Application to University Data Set," Int. J. Emerg. trends Eng. Dev., vol. 1, 2011.
[2] A. Feelders, H. Daniels, and M. Holsheimer, "Methodological and practical aspects of data mining," Inf. Manag., vol. 37, no. 5, pp. 271–281, 2000.
[3] L. K. Long and M. D. Troutt, "Data mining for human resource information systems," in Data mining: Opportunities and challenges, IGI Global, 2003, pp. 366–381.
[4] J. Chamizo-Gonzalez, E. I. Cano-Montero, E. Urquia-Grande, and C. I. Muñoz-Colomina, "Educational data mining for improving learning outcomes in teaching accounting within higher education," Int. J. Inf. Learn. Technol., 2015.
[5] A. Mueen, B. Zafar, and U. Manzoor, "Modeling and predicting students' academic performance using data mining techniques," Int. J. Mod. Educ. Comput. Sci., vol. 8, no. 11, p. 36, 2016.
[6] W. Punlumjeak and N. Rachburee, "A comparative study of feature selection techniques for classify student performance," in 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE), 2015, pp. 425–429.
[7] S. O. da Fonseca and A. A. Namen, "Data mining on inep databases: An initial analysis aiming to improve brazilian educational system," Educ. em Rev., vol. 32, no. 1, pp. 133–157, 2016.
[8] A. Dutt, M. A. Ismail, and T. Herawan, "A systematic review on educational data mining," Ieee Access, vol. 5, pp. 15991–16005, 2017.
[9] S. Slater, S. Joksimović, V. Kovanovic, R. S. Baker, and D. Gasevic, "Tools for educational data mining: A review," J. Educ. Behav. Stat., vol. 42, no. 1, pp. 85–106, 2017.
[10] R. Asif, A. Merceron, S. A. Ali, and N. G. Haider, "Analyzing undergraduate students' performance using educational data mining," Comput. Educ., vol. 113, pp. 177–194, 2017.
[11] Y. Ma, B. Liu, C. K. Wong, P. S. Yu, and S. M. Lee, "Targeting the right students using data mining," in Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 2000, pp. 457–464.
[12] J. Luan, "Data mining applications in higher education," SPSS Exec., vol. 7, 2004.
[13] D. Kabakchieva, "Predicting student performance by using data mining methods for classification," Cybern. Inf. Technol., vol. 13, no. 1, pp. 61–72, 2013.
[14] S. Kotsiantis, C. Pierrakeas, and P. Pintelas, "PREDICTING STUDENTS'PERFORMANCE IN DISTANCE LEARNING USING MACHINE LEARNING TECHNIQUES," Appl. Artif. Intell., vol. 18, no. 5, pp. 411–426, 2004.
[15] Z. A. Pardos, N. T. Heffernan, B. Anderson, C. L. Heffernan, and W. P. Schools, "Using fine-grained skill models to fit student performance with Bayesian networks," Handb. Educ. data Min., vol. 417, 2010.
[16] J.-F. Superby, J. P. Vandamme, and N. Meskens, "Determination of factors influencing the achievement of the first-year university students using data mining methods," in Workshop on educational data mining, 2006, vol. 32, p. 234.
[17] Romero, C., Lopez, M.-I., Luna, J.-M. and Ventura, S. (2013). Predicting students’´ final performance from participation in online discussion forums. Computers & Education. 68, 458–472.
[18] Romero, C. and Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert systems with applications. 33(1), 135–146
[19] B. Kapur, N. Ahluwalia, and R. Sathyaraj, "Comparative study on marks prediction using data mining and classification algorithms," Int. J. Adv. Res. Comput. Sci., vol. 8, no. 3, 2017.
[20] A. M. Shahiri and W. Husain, "A review on predicting student's performance using data mining techniques," Procedia Comput. Sci., vol. 72, pp. 414–422, 2015.
[21] S. S. Abu-Naser, I. S. Zaqout, M. Abu Ghosh, R. R. Atallah, and E. Alajrami, "Predicting student performance using artificial neural network: In the faculty of engineering and information technology," 2015.

Published

2021-08-12

How to Cite

Haval, B., Abdulrahman, K. J., & Rajab Abrahim, A. (2021). Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample. Academic Journal of Nawroz University, 10(3), 121–127. https://doi.org/10.25007/ajnu.v10n3a875

Issue

Section

Articles