Abstract
In the educational institutions, research on educational data is on demand due to its predictive power and decision-making process using machine learning approach. The present research work can be broadly categorized into two modules. Firstly, data pre-processing directed on real-time data of Government Polytechnic College Ambala, Haryana, India. Secondly, desired data collected along with exploratory data analysis and human-interpretable features is tested on six different classifier to predict student third-year performance as binary classification based on first- and second-year performance, and maximum accuracy of 98.7% was achieved. This generates a chance to identify low-performing students, and accordingly, early interventions can be deployed to prevent them from failing or dropping. This study also suggests a viable direction to use educational data for getting insights by using the machine learning approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
S. Liao, P. Chu, Hsiao, Data mining techniques and applications—a decade review. Exp. Syst. Appl. 39, 11303–11311 (2012)
S.D. Gheware, A.S. Kejkar, Tondare, Data mining: task, tools, techniques and applications. Int. J. Adv. Res. Comput. Commun. Eng. 3, 8095–8098 (2014)
R. Baker, K. Yacef, The state of educational data mining: a review and future visions. J. Educ. Data Mining 1, 3–16 (2009)
J. Zimmerman, K.H. Brodersen, H.R. Heinimann, A model-based approach to predicting graduate-level performance using indicators of undergraduate-level performance. J. Educ. Data Mining 7, 151–176 (2015)
M.G. Asogbon, O.W. Samuel, M.O. Omisore, A multi-class support vector machine approach for students’ academic performance prediction. Int. J. Multidiscip. Current Res. 4, 210–215 (2016)
S.M. Merchan, J.A. Duarte, Analysis of data mining techniques for constructing a predictive model for academic performance. IEEE Lat. Am. Trans. 14, 2783–2788 (2016)
A. Zollanvari, R.C. Kizilirmak, Y.H. Kho, Predicting students’ GPA and developing intervention strategies based on self-regulatory learning behaviors. IEEE Access 5, 23792–23802 (2017)
E.B. Costa, B. Fonseca, M.A. Santana, F.F. Araujo, J. Rego, Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure. Comput. Hum. Behav. 73, 247–256 (2017)
R. Asif, A. Merceron, S.A. Ali, N.G. Haider, Analysing undergraduate students’ performance using educational data mining. Comput. Educ. 113, 177–194 (2017)
S. Qu, K. Li, S. Zhang, Y. Wang, Predicting achievement of students in smart campus. IEEE Access 6, 60264–60273 (2018)
F. Yanga, F.W.B. Li, Study on student performance estimation, student progress analysis and student potential based on data mining. Comput. Educ. 123, 90–108 (2018)
E. Fernandes, M. Holanda, V. Borges, Educational data mining: predictive analysis of academic performance of public-school students in the capital of Brazil. J. Bus. Res. 95, 335–343 (2019)
G. Kostopoulos, S. Karlos, Multiview learning for early prognosis of academic performance: a case study. IEEE Trans. Learn. Technol. 12, 212–224 (2019)
A. Cano, J.D. Leonard, Interpretable multiview early warning system adapted to student populations. IEEE Trans. Learn. Technol. 12, 198–211 (2019)
A. Polyzou, G. Karypis, Feature extraction for next-term prediction of poor student performance. IEEE Trans. Learn. Technol. 12, 237–248 (2019)
D. Baneres, M. Seera, Rodríguez-Gonzalez, An early feedback prediction system for learners at-risk within a first-year higher education course. IEEE Trans. Learn. Technol. 12, 249–263 (2019)
A.I. Adekitan, O. Salau, The impact of engineering students’ performance in the first three years on their graduation result using educational data mining. Heliyon 5, 12–32 (2019)
L. Eglington, P. Pavlik, Predictiveness of prior failures is improved by incorporating trial duration. J. Educ. Data Mining 11, 1–19 (2019)
M. Hussain, W. Zhu, W. Zhang, Using machine learning to predict student difficulties from learning session data. Artif. Intell. Rev. 52, 381–407 (2019)
T. Toivonen, I. Jormanainen, Augmented intelligence in educational data mining. Smart Learn. Environ. 6, 1–25 (2019)
A. Yusuf, A. John, Classifiers ensemble and synthetic minority oversampling techniques for academic performance prediction. Int. J. Inform. Commun. Technol. 8(122), 127 (2019)
S. Tsai, C. Chen, Y. Shiao, Precision education with statistical learning and deep learning: a case study in Taiwan. Int. J. Educ. Technol. High. Educ. 17, 20–33 (2020)
Acknowledgements
I would like to thank Directorate of Technical Education Haryana for providing academic data of diploma students. I would also like to thank my guide for her continuous support and encouragement for carrying out this research.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Anamika, Dutta, M. (2022). Predicting Prior Academic Failure of Students’ Using Machine Learning Approach. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1394. Springer, Singapore. https://doi.org/10.1007/978-981-16-3071-2_12
Download citation
DOI: https://doi.org/10.1007/978-981-16-3071-2_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3070-5
Online ISBN: 978-981-16-3071-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)