A Machine Learning Techniques Used For Students’ Academic Success Predic-tion
Main Article Content
Abstract
Young generation of every country is the future of the country. The country with the highest GER in higher education will be more successful in all the terms (Keller, K.R.I. ,2021). India’s GER in higher education in 2018-19 was 26.3, and in 2019-20 is 27.1.It is observed from statistics that it is which is increased. Students are enrolling for higher education but many fails to complete it (Ministry of Education, Government of India, AISHE Report 2019-20). This leads to the need of identification of reasons of students’ academic success or failure. If we predict students’ academic success or failure at the initial stages of their graduation period will help to take preventive measures and increase passing percentage. Student academic success is one of the criteria for accessing quality of the educational institutions, and it is one of the crucial components. There are different aspects of students' academic success, such as exam-oriented, employment-oriented, and higher study-oriented.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
A., V., D., P., & V., M. (2017). Predicting Student’s Performance using Machine Learning. Communications on Applied Electronics, 7(11), 11–15. https://doi.org/10.5120/cae2017652730
Adekitan, A. I., & Salau, O. (2019). The impact of engineering students’ performance in the first three years on their graduation result using educational data mining. Heliyon, 5(2), e01250. https://doi.org/10.1016/j.heliyon.2019.e01250
Akash, P. P., Parvin, M., Moon, N. N., & Nur, F. N. (2021). Effect of Co-curricular activities on student ’ s academic performance by machine learning Authors : Shaikh Rezwan Rahman 1 Department of Computer Science & Engineering , Daffodil International University . Current Research in Behavioral Sciences, 100057. https://doi.org/10.1016/j.crbeha.2021.100057
Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, 17(1). https://doi.org/10.1186/s41239-020-0177-7
Anuradha, C., & Velmurugan, T. (2015). A comparative analysis on the evaluation of classification algorithms in the prediction of students performance. Indian Journal of Science and Technology, 8(15). https://doi.org/10.17485/ijst/2015/v8i15/74555
Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers and Education, 113, 177–194. https://doi.org/10.1016/j.compedu.2017.05.007
Aviso, K. B., Iii, F. P. A. D., Janairo, J. I. B., Lucas, R. I. G., Promentilla, M. A. B., Tan, R. R., & Yu, D. E. C. (2021). What university attributes predict for graduate employability ? 2(February), 1–8. https://doi.org/10.1016/j.clet.2021.100069
Borah, D., Malik, K., & Massini, S. (2021). Teaching-focused university – industry collaborations : Determinants and impact on graduates ’ employability competencies. 50(March 2020). https://doi.org/10.1016/j.respol.2020.104172
Cortez, P., & Silva, A. (2008). Using data mining to predict secondary school student performance. 15th European Concurrent Engineering Conference 2008, ECEC 2008 - 5th Future Business Technology Conference, FUBUTEC 2008, 2003(2000), 5–12.
Dhilipan, J., Vijayalakshmi, N., Suriya, S., & Christopher, A. (2021). Prediction of Students Performance using Machine learning. IOP Conference Series: Materials Science and Engineering, 1055(1), 012122. https://doi.org/10.1088/1757-899x/1055/1/012122
Hamsa, H., Indiradevi, S., & Kizhakkethottam, J. J. (2016). Student Academic Performance Prediction Model Using Decision Tree and Fuzzy Genetic Algorithm. Procedia Technology, 25, 326–332. https://doi.org/10.1016/j.protcy.2016.08.114
Joshi, K. M., & Ahir, K. V. (2019). Higher education in India: Issues related to access, equity, efficiency, quality and internationalization. Academia (Greece), 2019(14), 71–91. https://doi.org/10.26220/aca.2979
Khan, I., Al Sadiri, A., Ahmad, A. R., & Jabeur, N. (2019). Tracking student performance in introductory programming by means of machine learning. 2019 4th MEC International Conference on Big Data and Smart City, ICBDSC 2019, 1–6. https://doi.org/10.1109/ICBDSC.2019.8645608
Kishan Das Menon, H., & Janardhan, V. (2020). Machine learning approaches in education. Materials Today: Proceedings, 43(xxxx), 3470–3480. https://doi.org/10.1016/j.matpr.2020.09.566
Kovacic, Z. J. (2010). Early Prediction of Student Success: Mining Students Enrolment Data. Proceedings of the 2010 InSITE Conference, 647–665. https://doi.org/10.28945/1281
Kuh, G. D., Cruce, T. M., Shoup, R., Kinzie, J., Gonyea, R. M., & Gonyea, M. (2012). Unmasking the Effects of Student on First-Year College Engagement Grades and Persistence. 79(5), 540–563.
Kumar, M., Singh, A. J., & Handa, D. (2017). Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques. International Journal of Education and Management Engineering, 7(6), 40–49. https://doi.org/10.5815/ijeme.2017.06.05
Lau, E. T., Sun, L., & Yang, Q. (2019). Modelling, prediction and classification of student academic performance using artificial neural networks. SN Applied Sciences, 1(9), 1–10. https://doi.org/10.1007/s42452-019-0884-7
Mardis, M. A., Ma, J., Jones, F. R., Ambavarapu, C. R., Kelleher, H. M., Spears, L. I., & McClure, C. R. (2018). Assessing alignment between information technology educational opportunities, professional requirements, and industry demands. Education and Information Technologies, 23(4), 1547–1584. https://doi.org/10.1007/s10639-017-9678-y
Martínez-Carrascal, J. A., Márquez Cebrián, D., Sancho-Vinuesa, T., & Valderrama, E. (2020). Impact of early activity on flipped classroom performance prediction: A case study for a first-year Engineering course. Computer Applications in Engineering Education, 28(3), 590–605. https://doi.org/10.1002/cae.22229
Mesarić, J., & Šebalj, D. (2016). Decision trees for predicting the academic success of students. 7, 367–388. https://doi.org/10.17535/crorr.2016.0025
Miguéis, V. L., Freitas, A., Garcia, P. J. V, & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems, 115, 36–51. https://doi.org/https://doi.org/10.1016/j.dss.2018.09.001
Nhamo, G., & Mjimba, V. (n.d.). Sustainable Development Goals and Institutions of Higher Education.
Pallathadka, H., Wenda, A., Ramirez-asís, E., Asís-lópez, M., Flores-albornoz, J., & Phasinam, K. (2021). Materials Today : Proceedings Classification and prediction of student performance data using various machine learning algorithms. Materials Today: Proceedings, xxxx. https://doi.org/10.1016/j.matpr.2021.07.382
Rathee, A., Mining, D., Mining, E. D., & Algorithm, C. (2013). Survey on Decision Tree Classification algorithms for the Evaluation of Student Performance ID3 Algorithm. International Journal of Computers & Technology, 4(2), 244–247.
Rodríguez-Hernández, C. F., Musso, M., Kyndt, E., & Cascallar, E. (2021). Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation. Computers and Education: Artificial Intelligence, 2(March), 100018. https://doi.org/10.1016/j.caeai.2021.100018
Roksa, J., & Kinsley, P. (2018). The Role of Family Support in Facilitating Academic Success of Low ‑ Income Students. Research in Higher Education, 0123456789. https://doi.org/10.1007/s11162-018-9517-z
Salah Hashim, A., Akeel Awadh, W., & Khalaf Hamoud, A. (2020). Student Performance Prediction Model based on Supervised Machine Learning Algorithms. IOP Conference Series: Materials Science and Engineering, 928, 032019. https://doi.org/10.1088/1757-899x/928/3/032019
Sekeroglu, B., Dimililer, K., & Tuncal, K. (2019). Student performance prediction and classification using machine learning algorithms. ACM International Conference Proceeding Series, Part F1481, 7–11. https://doi.org/10.1145/3318396.3318419
Sripath Roy, K., Roopkanth, K., Uday Teja, V., Bhavana, V., & Priyanka, J. (2018). Student career prediction using advanced machine learning techniques. International Journal of Engineering and Technology(UAE), 7(2), 26–29. https://doi.org/10.14419/ijet.v7i2.20.11738
Suresh, A., S, B. S., R, E. K., & N, G. (2020). Student Performance Prediction using Machine Learning. International Journal of Computer Science and Mobile Computing, 9(9), 38–42. https://doi.org/10.47760/ijcsmc.2020.v09i09.004
Tsiakmaki, M., Kostopoulos, G., Kotsiantis, S., & Ragos, O. (2020). Transfer learning from deep neural networks for predicting student performance. Applied Sciences (Switzerland), 10(6). https://doi.org/10.3390/app10062145
Wakelam, E., Jefferies, A., Davey, N., & Sun, Y. (2020). The potential for student performance prediction in small cohorts with minimal available attributes. British Journal of Educational Technology, 51(2), 347–370. https://doi.org/10.1111/bjet.12836
Xu, J., Moon, K. H., & Van Der Schaar, M. (2017). A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs. IEEE Journal on Selected Topics in Signal Processing, 11(5), 742–753. https://doi.org/10.1109/JSTSP.2017.2692560
Yang, H., Ph, D., Su, J., Ph, D., Bradley, K. D., & Ph, D. (2020). Applying the Rasch Model to Evaluate the Self-Directed Online Learning Scale ( SDOLS ) for Graduate Students. 21(3).
Yıldız Aybek, H. S., & Okur, M. R. (2018). Predicting Achievement with Artificial Neural Networks: The Case of Anadolu University Open Education System. International Journal of Assessment Tools in Education, 5(3), 474–490. https://doi.org/10.21449/ijate.435507
York, T. T., Gibson, C., & Rankin, S. (2015). Defining and measuring academic success. Practical Assessment, Research and Evaluation, 20(5), 1–20.
Zeineddine, H., Braendle, U., & Farah, A. (2021). Enhancing prediction of student success: Automated machine learning approach. Computers and Electrical Engineering, 89(November 2020), 1–10. https://doi.org/10.1016/j.compeleceng.2020.106903