International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

Study Factors for Student Performance Applying Data Mining Regression Model Approach

  • Khan, Shakir (College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU))
  • Received : 2021.02.05
  • Published : 2021.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we apply data mining techniques and machine learning algorithms using R software, which is used to predict, here we applied a regression model to test some factor on the dataset for which we assumed that it effects student performance. Model was built on an existing dataset which contains many factors and the final grades. The factors tested are the attention to higher education, absences, study time, parent's education level, parent's jobs, and the number of failures in the past. The result shows that only study time and absences can affect the students' performance. Prediction of student academic performance helps instructors develop a good understanding of how well or how poorly the students in their classes will perform, so instructors can take proactive measures to improve student learning. This paper also focuses on how the prediction algorithm can be used to identify the most important attributes in a student's data.

Keywords

References

  1. Al-Kabi, M. N., & Jirjees, J. M. (2019). Survey of Big Data applications: health, education, business & finance, and security & privacy. Journal of Information Studies & Technology (JIS&T), 2018(2), 12.
  2. UCI, (2014) Student Performance Data Set https://archive.ics.uci.edu/ml/datasets/student+performance
  3. Sampasa-Kanyinga, H., Chaput, J. P., & Hamilton, H. A. (2019). Social media use, school connectedness, and academic performance among adolescents. The journal of primary prevention, 40(2), 189-211. https://doi.org/10.1007/s10935-019-00543-6
  4. Ahmed, A.B.E.D. and Elaraby, I.S., 2014. Data Mining: A prediction for Student's Performance Using Classification Method. World Journal of Computer Application and Technology, 2(2), pp.43-47. https://doi.org/10.13189/wjcat.2014.020203
  5. Saa, A. A. (2016). Educational data mining & students' performance prediction. International Journal of Advanced Computer Science and Applications, 7(5), 212-220
  6. Saxena, K., Jaloree, S., Thakur, R.S., & Kamley, S. (2018). Linear Regression Technique for Student Academic Performance Prediction.
  7. Harwati, Ardita Permata Alfiani, and Febriana Ayu Wulandari. "Mapping Student's Performance Based on Data Mining Approach (A Case Study)." Agriculture and Agricultural Science Procedia 3 (January 1, 2015): 173-77. doi:10.1016/j.aaspro.2015.01.034
  8. Ngai E.W.T. A,, Li Xiu B, Chau D.C.K. (2009) Application Of Data Mining Techniques In Customer Relationship Management: A Literature Review And Classification, Journal Of Expert Systems With Applications 36 (2009) 2592-2602 https://doi.org/10.1016/j.eswa.2008.02.021
  9. Raval M Kalyani ( 2012) Data Mining Techniques, International Journal Of Advanced Research In Computer Science And Software Engineering Volume 2, Issue 10.
  10. Ridwan Mujib, Suyono Hadi, M. Sarosa 2013 Penerapan Data Mining Untuk Evaluasi KinerjaAkademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier, Jurnal EECCIS Vol.7, No. 1
  11. Romero, C.; Ventura, S. Data mining in education. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2013, 3, 12-27. https://doi.org/10.1002/widm.1075
  12. Khan, S., & Alqahtani, S. (2020). Big Data Application and its Impact on Education. International Journal Of Emerging Technologies In Learning (IJET), 15(17), pp. 36-46. http://dx.doi.org/10.3991/ijet.v15i17.14459
  13. Zhang, Y., Ghandour, A., & Shestak, V. (2020). Using Learning Analytics to Predict Students Performance in Moodle LMS. International Journal Of Emerging Technologies In Learning (IJET), 15(20), pp. 102-115. doi: http://dx.doi.org/10.3991/ijet.v15i20.15915