A Role of Machine Learning for Students’ Academic Success Prediction

Volume: 10 | Issue: 01 | Year 2024 | Subscription
International Journal of Computer Aided Manufacturing
Received Date: 04/12/2024
Acceptance Date: 04/24/2024
Published On: 2024-06-11
First Page: 1
Last Page: 6

Journal Menu

By: Balasaheb Kalhapure

Head, Department in Commerce, Dr. Babasaheb Ambedkar College Aundh ,Pune, India.

Abstract

This research paper aims to identify early predictions of students’ academic success with the help of different indicators. One of the crucial parts of the study is identifying additional indicators that play an essential role in defining students’ academic success. Early prediction of success will be helpful for various stakeholders such as students, parents, educators/teachers, institutions, universities, and the government. Machine Learning is a part of Artificial Intelligence (AI) and is a growing technology that enables computers to learn automatically from past data. With different mathematical algorithms, Ma- chine Learning develops prediction models which predict output with the help of historical data. We can apply Machine learning techniques in the education domain to help educators as well as education seekers. We can align teaching and learning methods in skill-based education. With the help of different machine learning techniques, we can identify factors that play major roles in student success.

Keywords: Education, Machine learning, Artificial Intelligence, different mathematical algorithms,
SASP

Loading

Citation:

How to cite this article: Balasaheb Kalhapure, A Role of Machine Learning for Students’ Academic Success Prediction. International Journal of Computer Aided Manufacturing. 2024; 10(01): 1-6p.

How to cite this URL: Balasaheb Kalhapure, A Role of Machine Learning for Students’ Academic Success Prediction. International Journal of Computer Aided Manufacturing. 2024; 10(01): 1-6p. Available from:https://journalspub.com/publication/a-role-of-machine-learning-for-students-academic-success-prediction/

Refrences:

  1. , V., D., P., & V., M. (2017). Predicting Student’s Performance using Machine Learning. Com- munications on Applied Electronics, 7(11), 11–15. https://doi.org/10.5120/cae2017652730
  2. 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
  3. 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 Depart- ment of Computer Science & Engineering , Daffodil International University . Current Research in Behavioral Sciences, 100057. https://doi.org/10.1016/j.crbeha.2021.100057
  4. 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
  5. Anuradha, , & 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
  6. Asif, , Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ per- formance using educational data mining. Computers and Education, 113, 177–194. https://doi.org/10.1016/j.compedu.2017.05.007
  7. Aviso, K. , 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
  8. Borah, , 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
  9. Cortez, , & Silva, A. (2008). Using data mining to predict secondary school student performance. 15th European Concurrent Engineering Conference 2008, ECEC 2008 – 5th Future Business Tech- nology Conference, FUBUTEC 2008, 2003(2000), 5–12.
  10. Dhilipan, J., Vijayalakshmi, N., Suriya, S., & Christopher, A. (2021). Prediction of Students Per- formance using Machine learning. IOP Conference Series: Materials Science and Engineering, 1055(1), 012122. https://doi.org/10.1088/1757-899x/1055/1/012122
  11. Hamsa, H., Indiradevi, S., & Kizhakkethottam, J. J. (2016). Student Academic Performance Pre- diction Model Using Decision Tree and Fuzzy Genetic Procedia Technology, 25, 326–332 https://doi.org/10.1016/j.protcy.2016.08.114
  12. 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
  13. Khan, , Al Sadiri, A., Ahmad, A. R., & Jabeur, N. (2019). Tracking student performance in intro- ductory 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
  14. Kishan Das Menon, , & Janardhan, V. (2020). Machine learning approaches in education. Mate- rials Today: Proceedings, 43(xxxx), 3470–3480. https://doi.org/10.1016/j.matpr.2020.09.566
  15. Kovacic, J. (2010). Early Prediction of Student Success: Mining Students Enrolment Data. Pro- ceedings of the 2010 InSITE Conference, 647–665. https://doi.org/10.28945/1281
  16. Kuh, 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.
  17. Kumar, , Singh, A. J., & Handa, D. (2017). Literature Survey on Student’s Performance Predic- tion in Education using Data Mining Techniques. International Journal of Education and Manage- ment Engineering, 7(6), 40–49. https://doi.org/10.5815/ijeme.2017.06.05
  18. Lau, 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
  19. Mardis, A., Ma, J., Jones, F. R., Ambavarapu, C. R., Kelleher, H. M., Spears, L. I., & McClure,R. (2018). Assessing alignment between information technology educational opportunities, pro- fessional requirements, and industry demands. Education and Information Technologies, 23(4), 1547–1584. https://doi.org/10.1007/s10639-017-9678-y
  20. 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
  21. Mesarić, , & Šebalj, D. (2016). Decision trees for predicting the academic success of students. 7, 367–388. https://doi.org/10.17535/crorr.2016.0025
  22. Miguéis, V. L., Freitas, A., Garcia, P. J. V, & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling Decision Support Sys- tems, 115, 36–51. https://doi.org/https://doi.org/10.1016/j.dss.2018.09.001
  23. Nhamo, G., & Mjimba, V. (n.d.). Sustainable Development Goals and Institutions of Higher Education.
  24. Pallathadka, , 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
  25. Rathee, , Mining, D., Mining, E. D., & Algorithm, C. (2013). Survey on Decision Tree Classifi- cation algorithms for the Evaluation of Student Performance ID3 Algorithm. International Journal of Computers & Technology, 4(2), 244–247.
  26. Rodríguez-Hernández, C. F., Musso, M., Kyndt, E., & Cascallar, E. (2021). Artificial neural net- works in academic performance prediction: Systematic implementation and predictor evaluation. Computers and   Education:   Artificial   Intelligence,   2(March), https://doi.org/10.1016/j.caeai.2021.100018
  27. Roksa, J., & Kinsley, P. (2018). The Role of Family Support in Facilitating Academic Success of Low ‑             Income                    Research      in      Higher      Education,      0123456789. https://doi.org/10.1007/s11162-018-9517-z
  28. Salah Hashim, A., Akeel Awadh, W., & Khalaf Hamoud, A. (2020). Student Performance Predic- tion Model based on Supervised Machine Learning IOP Conference Series: Materials Science and Engineering, 928, 032019. https://doi.org/10.1088/1757-899x/928/3/032019
  29. Sekeroglu, B., Dimililer, K., & Tuncal, K. (2019). Student performance prediction and classifica- tion using machine learning algorithms. ACM International Conference Proceeding Series, Part F1481, 7–11. https://doi.org/10.1145/3318396.3318419
  30. Sripath Roy, , 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
  31. Suresh, A., S, B. , 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
  32. 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
  33. Wakelam, E., Jefferies, A., Davey, N., & Sun, Y. (2020). The potential for student performance prediction in small cohorts with minimal available British Journal of Educational Tech- nology, 51(2), 347–370. https://doi.org/10.1111/bjet.12836
  34. Xu, J., Moon, K. H., & Van Der Schaar, M. (2017). A Machine Learning Approach for Tracking and Predicting Student Performance in Degree IEEE Journal on Selected Topics in Sig- nal Processing, 11(5), 742–753. https://doi.org/10.1109/JSTSP.2017.2692560
  35. 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).
  36. Yıldız Aybek, H. S., & Okur, M. R. (2018). Predicting Achievement with Artificial Neural Net- works: The Case of Anadolu University Open Education System. International Journal of Assess- ment Tools in Education, 5(3), 474–490. https://doi.org/10.21449/ijate.435507
  37. York, T., Gibson, C., & Rankin, S. (2015). Defining and measuring academic success. Practical Assessment, Research and Evaluation, 20(5), 1–20.
  38. Zeineddine, H., Braendle, , & Farah, A. (2021). Enhancing prediction of student success: Auto- mated machine learning approach. Computers and Electrical Engineering, 89(November 2020), 1–https://doi.org/10.1016/j.compeleceng.2020.106903