A Prediction Model for Online Student Academic Performance Using Machine Learning
Abstract
Academic data mining impacts a large number of educational institutions, significantly, playing a prime role in accumulating, studying, and analyzing the academic data. The accumulated academic data can be processed and analyzed for various purposes such as predicting the student academic performance and broadening the retention rate. The prediction of students’ academic performance at the initial stage helps the students to identify their lacking subjects. Accordingly, the predicted results can assist the students to focus more on their deficient subjects so as to improvise their academic performance. Currently, numerous machine learning techniques are being used by the academic institutions to extract and analyze the valuable information ranging from predicting students’ academic performance to the identification of fast and slow learners. This paper intends to propose an ensemble model, using the voting method for preclusive prediction of the student academic performance. The predicted results are being further utilized by the poor performers to concentrate more on their deficit courses. Hence, the instructors can focus on creating and implementing novel strategies or amending the existing pedagogical tools and approaches to aid the slow learners in improvising their performance. The proposed model has been tested on the academic data of an educational institution using the RapidMiner tool.
Full Text:
PDFReferences
V.L. Miguéi, A. Freitas, P.J.V. Garcia and A. Silva, "Early segmentation of students according to their academic performance: A predictive modelling approach," Decision Support System,vol. 6, no. 5, pp. 65-78, 2018.
S. J. Lakshmi and M. Thangaraj, "Recommender system for stimulating the learning skill of slow learner in higher educational institution using EDM," International Journal on Recent Technolofical Engineering, vol. 5, pp. 98-109, 2019.
D. T. Ha, P. T. T. Loan, C. N. Giap and N. T. L. Huong, "An empirical study for student academic performance prediction using machine learning techniques," International Journal of Computer Science and Information Security (IJCSIS), vol. 18, no. 3, pp. 75-82, 2020
R. Umer, T. Susnjak, A. Mathrani and S. Suriadi,"On predicting academic performance with process mining in learning analytics," Journal of Resource Innovation and Teach Learnearning, vol. 78, pp. 155-168, 2017.
O.H.T. Lu, A.Y.Q. Huang, J.C.H. Huang, A.J.Q Lin, H. Ogata et al., "Applying learning analytics for the early prediction of students’ academic performance in blended learning," Educational Technological Socoety,vol. 55, pp. 111-123, 2018.
O. Viberg, M. Hatakka, O. Bälter, A. Mavroudi,"The current landscape of learning analytics in higher education,"Computers in Human Behavior, vol. 18, pp. 1001-1222, 2018.
M. S. B. M. Azmi and I. H. B. M. Paris, “Academic performance prediction based on voting technique,” in 2011 IEEE 3rd International Conference on Communication Software and Networks , Calcuta, India, pp. 24-27, 2011.
Tarandeep Kaur, Harjinder Kaur, "Machine Learning: An Internal Review", Journal of Emerging Technologies and Innovative Research, 5, no. 11, 6, 2018.
C. Romero, P.G. Espejo, A. Zafra, J.R. Romero and S. Ventura, "Web usage mining for predicting final marks of students that use Moodle courses," Computer Application in Engineering and Education,vol. 65, pp. 555-578, 2013.
A. M. Shahiri and W. Husain, "A review on predicting student's performance using data mining techniques," Procedia Computer Science, vol. 72, pp. 414-422, 2015.
N. Thai-Nghe, L. Drumond, A. Krohn-Grimberghe and L. Schmidt-Thieme, "Recommender system for predicting student performance," Procedia Computer Science, vol. 20, pp. 55-65, 2010.
S. Huang and N. Fang, "Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models,"Comput Education, vol. 55, no. 6, pp. 33-42, 2013.
M. Imran, S. Latif, D. Mehmood and M. S. Shah,"Student academic performance prediction using supervised learning techniques," International Journal on Emerging Technologies in Learning, vol. 77, pp. 102-120, 2019.
P. Strecht, L. Cruz, C. Soares, J. Mendes-Moreira and R. Abreu,"A Comparative Study of Classification and Regression Algorithms for Modelling Students’ Academic Performance,", in Proc. ICEDM, Noida, India, pp. 55-64, 2015.
P. Kamal and S. Ahuja,"An ensemble-based model for prediction of academic performance of students in undergrad professional course," Journal of Engineering Design and Technology, vol. 98, pp. 654-672, 2019.
V. Skrbinjek and V. Dermol, "Predicting students’ satisfaction using a decision tree,"Tert Education and Management,vol. 64, pp. 210-218, 2019.
Dr. Antino Marelino. (2014). Customer Satisfaction Analysis based on Customer Relationship Management. International Journal of New Practices in Management and Engineering, 3(01), 07 - 12. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/26
Dr. Sandip Kadam. (2014). An Experimental Analysis on performance of Content Management Tools in an Organization. International Journal of New Practices in Management and Engineering, 3(02), 01 - 07. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/27
Ms. Nora Zilam Runera. (2014). Performance Analysis On Knowledge Management System on Project Management. International Journal of New Practices in Management and Engineering, 3(02), 08 - 13. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/28
Mrs. Leena Rathi. (2014). Ancient Vedic Multiplication Based Optimized High Speed Arithmetic Logic . International Journal of New Practices in Management and Engineering, 3(03), 01 - 06. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/29Kaur H, Kushwaha AS., “An elicit elucidation on the process of education data mining” , International Conference on Intelligent Computing and Control Systems, ICCS 2019.
S. Roy and A. Garg, "Predicting academic performance of student using classification techniques," in 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), Korat, Thailand, pp. 568-572, 2017.
S. Poonam, S. Ahuja, V. Jaitly and S. Jain, "A framework to alleviate common problems from recommender system ,"A case study for technical course recommendation," Journal of Discrete Mathematical Sciences and Cryptography, vol. 23, no.2, pp. 451-460, 2020.
A. Rajak, A. K. Shrivastava and V. Vidushi, “Applying and comparing machine learning classification algorithms for predicting the results of students,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 23, no.2, pp. 419-427, 2020.
H. Guruler, A. Istanbullu and M. Karahasan, "A new student performance analysing system using knowledge discovery in higher educational databases," Computer Education, vol. 6, no. 5, pp. 125-138, 2010.
A. Rajak, A. K. Shrivastava and V. Vidushi, “Applying and comparing machine learning classification algorithms for predicting the results of students,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 23, no.2, pp. 419-427, 2020.
A. Siddique, A. Jan, F. Majeed, A.I. Qahmash, N.N. Quadri et al., “ Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers,” Applied Sciences, vol. 11, no. 24, pp. 11845, 2021.
A. S. Hoffait and M. Schyns,"Early detection of university students with potential difficulties," Decision Support System, vol. 9, no. 5, pp. 5-20, 2017.
DOI: https://doi.org/10.31449/inf.v47i1.4297
This work is licensed under a Creative Commons Attribution 3.0 License.