The week's pick
Reseach Article
Machine Learning based Marks Prediction to Support Recommendation of Optimum Specialization and Study Track
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 181 - Number 49 |
| Year of Publication: 2019 |
| Authors: Gibrael Abosamra, Ahmad Faloudah |
10.5120/ijca2019918672
|
Gibrael Abosamra, Ahmad Faloudah . Machine Learning based Marks Prediction to Support Recommendation of Optimum Specialization and Study Track. International Journal of Computer Applications. 181, 49 ( Apr 2019), 15-25. DOI=10.5120/ijca2019918672
Abstract
Due to the spread of educational management information systems (EMIS), it become necessary to add intelligent layers to improve the educational process. One of the important tasks when the student moves from one stage to the other within the educational system of a university is the determination of the appropriate department if the transition is from the first level of a faculty to a certain department or the determination of the specialization track within a certain department in higher levels. These transition moments are crucial because they affect the degree of success of the student in the selected specialization and the quality of the educational process as a whole. In this research, different machine learning (ML) techniques have been tested to predict students' marks based on their marks in the preceded courses to guide them in the selection of the most suitable specialization or track. A variety of ML prediction models have been studied, experimented and evaluated on a propriety dataset, which resulted in the selection of a neural network (NN) architecture that gives an average root mean squared error of 6.26 and a mean absolute error of 5.74 based on a scale of 0 to 100. The accuracy is comparable to the state-of-the-art work and a practical example has been given that proves the ability of the proposed system to recommend certain tracks and/or specializations based on the marks of the already studied courses. Moreover, indirect prediction using cascaded networks has been proven to generate acceptable results that can facilitate building a hierarchy of networks using a short-term dataset to draw a weighted course road map that helps students to select the best path and help institutions to perform early measures to deal with weaknesses and anomalies.
References
- A Shahiri, W. H. 2015. A Review on Predicting Student’s Performance using Data Mining Techniques. Procedia Computer Science, Vol 72, pp.414-422.
- Sin, K., and Muthu, L. 2015. Application of big data in education data mining and learning analytics – a literature review. ICTACT journal on soft computing. Vol 5-No.4.
- Romero,C., Ventura, S., Espejo, P. G., and Hervas, C. 2008. Data Mining Algorithms to Classify Students. In Educational Data Mining 2008.
- Angeline, D. 2013. Association Rule Generation for Student Performance Analysis using Apriori Algorithm. The SIJ Transactions on Computer Science Engineering & its Applications (CSEA). Vol.1-No.1., pp.12-16.
- Naren J. 2014. Application of Data Mining in Educational Database for Predicting Behavioural Patterns of the Students. International Journal of Computer Science and Information Technologies, Vol.5-No.3, pp. 4649-4652.
- Mayilvaganan, M., and Kalpanadevi, D. 2014. Comparison of classification techniques for predicting the performance of students’ academic environment. Communication and Network Technologies (ICCNT), (pp. 113–118). Coimbatore: IEEE.
- Parack S., Zahid, Z., and Merchant, F. 2012. Application of data mining in educational databases for predicting academic trends and patterns. Technology Enhanced Education (ICTEE), (pp. 1–4). India: IEEE International Conference.
- Osmanbegovic, E., and Suljic, M. 2012. Data mining approach for predicting student performance. Economic Review - Journal of Economics and Business,Vol.10-No.1 pp.3-12.
- Natek, S., and Zwilling, M. 2014. Student data mining solution–knowledge management system related to higher education institutions. Expert Systems with Applications, Vol.41-No.14, pp.6400–6407.
- Arsad, P., Buniyamin, N., and Manan, J. 2013. A neural network students' performance prediction model (NNSPPM). Smart Instrumentation, Measurement and Applications (ICSIMA) (pp. 1–5). IEEE International Conference.
- Gray, G., McGuinness, C., Owende, P. 2014. An application of classification models to predict learner progression in tertiary education. International Advance Computing Conference (IACC), (pp. 549–554). Dublin, IEEE.
- Kotsiantis, S., and Pintelas, P. E. 2004. Comparing Regression Algorithms for Predicting Students’ Marks in Hellenic Open University. Accessed through http://www.etpe.gr/custom/pdf/etpe55.pdf
- Yadav, S., and Pal, S. 2012. Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification. World of Computer Science and Information Technology Journal (WCSIT), Vol.51.
- Kovacic, Z. 2010. Early Prediction of Student Success: Mining Students’ Enrolment Data. Proceedings of Informing Science & IT Education Conference (InSITE) (p. 648). Informing Science Institute.
- Tu, J. V. 1996. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of clinical epidemiology,Vol. 49-No.11,pp. 1225-1231.
- Minaei-Bidgoli, B., Kashy, D. A., Kortemeyer, G., and Punch, W. F. 2003. Predicting student performance: An application of data mining methods with the educational web-based system. Frontiers in Education, 2003. FIE 2003 33rd Annual,Vol.1, pp. T2A-13. 1, USA: IEEE.
- Aha, D. W., Kibler, D., and Albert, M. K. 1991. Instance-based learning algorithms. Machine learning, Vol.6-No.1, pp. 37-66.
- Hamalainen, W., and Vinni, M. 2006. Comparison of machine learning methods for intelligent tutoring systems. In International Conference on Intelligent Tutoring Systems (pp. 525-534). Springer, Berlin, Heidelberg.
- Wikipedia contributors. (2018, November 25). Feature scaling. In Wikipedia, the Free Encyclopedia. Retrieved 12:10, March 3, 2019, https://en.wikipedia.org/w/index.php?title=Feature_scaling&oldid=870599160
- Grus, Joel. 2015. Data Science from Scratch. Sebastopol, CA: O'Reilly. pp. 99-100. ISBN 978-1-491-90142-7.
- Predictive analytics today. (2017, March 18). http://www.predictiveanalyticstoday.com/top-free-data-mining-software/
- Wikipedia contributors. (2019, March 1). R (programming language). In Wikipedia, the Free Encyclopedia. Retrieved 12:14, March 3, 2019, form https://en.wikipedia.org/w/index.php?title=R_(programming_language)&oldid=885639401
- G. Abosamra, A Faloudah. 2019, 02 28). EDdataset. Retrieved from King Abdulaziz University (Files/Other): URL:https://gabosamra.kau.edu.sa/Show_files.aspx?Site_ID=0052079&Lng=EN
- Fritsch,S., and Guenther,F. 2016. neuralnet: Training of Neural Networks. R package version 1.33. URL: https://CRAN.R-project.org/package=neuralnet
- Rong, X. 2014. deepnet: deep learning toolkit in R. R package version 0.2. URL: https://CRAN.R-project.org/package=deepnet Z.
- Iqbal, Z., Qadir, J., Mian,A., and Kamiran, F., 2017. Machine learning based student grade prediction: A case study. arXiv preprint arXiv:1708.08744, pp 1–22.
Index Terms
Keywords