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Predicting Students' Performance Using Machine Learning Algorithms

Authors:

Charalampos Dervenis,

Vasileios Kyriatzis,

Spyros Stoufis,

Panos FitsilisAuthors Info & Claims

ICACS '22: Proceedings of the 6th International Conference on Algorithms, Computing and Systems

Article No.: 6, Pages 1 - 7

https://doi.org/10.1145/3564982.3564990

Published: 30 January 2023 Publication History

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Abstract

Learning analytics (LA), defined as “the measurement, collection, analysis and reporting of data about learners and their contexts for the purposes of understanding and optimizing learning and the environments in which it occurs”, is a research topic that has gained significant importance and visibility among researchers during the past few decades. It is a research domain where the use of modern machine-learning (ML) algorithms and big data management provide timely and actionable information that can transform the overall learning experience for both students and educational institutions. In this paper we use ML algorithms in order to predict the performance of students, taking into account both past semester grades and socioeconomic factors. We run two models; a 2-class one predicting a “pass” or “fail” result and then we expanded this to a 5-class model, where we predict in which grading group the student will fall in the next semester. The results acquired indicate that it is possible to accurately predict the student's performance in both cases, with the 2-class model performing better than the 5-class one, which of course opts in providing more fine grain results.

References

[1]

Lias, T. E., & Elias, T. (2011). Learning analytics: The definitions, the processes, and the potential.

[2]

Johnson, L., Brown, S., Cummins, M., & Estrada, V. (2012). The technology outlook for STEM+ education 2012-2017: an NMC horizon report sector analysis (pp. 1-23). The New Media Consortium.

[3]

Siemens, G. (2010). What are learning analytics? eLearnspace. Recuperado de http://www. elearnspace. org/blog/2010/08/25/what-are-learning-analytics/. Consultado el, 1(07), 2012.

[4]

Greller, W., Ebner, M., & Schön, M. (2014, June). Learning analytics: From theory to practice–data support for learning and teaching. In International Computer Assisted Assessment Conference (pp. 79-87). Springer, Cham.

[5]

Wise, A. F., & Shaffer, D. W. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics, 2(2), 5-13.

[6]

Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018). Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8759-8768).

[7]

Musso, M. F., Kyndt, E., Cascallar, E. C., & Dochy, F. (2013). Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks. Frontline Learning Research, 1(1), 42-71.

[8]

Kotsiantis, S. B. (2012). Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades. Artificial Intelligence Review, 37(4), 331-344.

Digital Library

[9]

Wolff, A., Zdrahal, Z., Herrmannova, D., & Knoth, P. (2014). Predicting student performance from combined data sources. In Educational data mining (pp. 175-202). Springer, Cham.

[10]

Bengio, Y. (2009). Learning deep architectures for AI. Foundations and trends® in Machine Learning, 2(1), 1-127.

[11]

Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, 4(4), 253-278.

[12]

Liu, R., & Koedinger, K. R. (2015). Variations in Learning Rate: Student Classification Based on Systematic Residual Error Patterns across Practice Opportunities. International educational data mining society.

[13]

Atif, A., Richards, D., Bilgin, A., & Marrone, M. (2013). Learning analytics in higher education: a summary of tools and approaches. In ASCILITE-Australian Society for Computers in Learning in Tertiary Education Annual Conference (pp. 68-72). Australasian Society for Computers in Learning in Tertiary Education.

[14]

Iatrellis, O., Savvas, I. K., Fitsilis, P., & Gerogiannis, V. C. (2021). A two-phase machine learning approach for predicting student outcomes. Education and Information Technologies, 26(1), 69-88.

[15]

Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27.

[16]

Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355.

[17]

Cortez, P., & Silva, A. M. G. (2008). Using data mining to predict secondary school student performance.

[18]

Demsar J, Curk T, Erjavec A, Gorup C, Hocevar T, Milutinovic M, Mozina M, Polajnar M, Toplak M, Staric A, Stajdohar M, Umek L, Zagar L, Zbontar J, Zitnik M, Zupan B (2013) Orange: Data Mining Toolbox in Python, Journal of Machine Learning Research 14(Aug): 2349−2353

[19]

Zhang, S., Li, X., Zong, M., Zhu, X., & Cheng, D. (2017). Learning k for knn classification. ACM Transactions on Intelligent Systems and Technology (TIST), 8(3), 1-19.

[20]

Cunningham, P., & Delany, S. J. (2021). K-nearest neighbour classifiers-a tutorial. ACM Computing Surveys (CSUR), 54(6), 1-25.

[21]

Ben-Hur, A., & Weston, J. (2010). A user's guide to support vector machines. In Data mining techniques for the life sciences (pp. 223-239). Humana Press.

[22]

Chang, C. C., & Lin, C. J. (2011). LIBSVM: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2(3), 1-27.

Digital Library

[23]

Nalepa, J., & Kawulok, M. (2019). Selecting training sets for support vector machines: a review. Artificial Intelligence Review, 52(2), 857-900.

Digital Library

[24]

Resende, P. A. A., & Drummond, A. C. (2018). A survey of random forest based methods for intrusion detection systems. ACM Computing Surveys (CSUR), 51(3), 1-36.

[25]

Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.

[26]

Ashfaque, J. M., & Iqbal, A. (2019). Introduction to support vector machines and kernel methods. publication at https://www. researchgate. net/publication/332370436.

Cited By

Ale FIkpaya IDaniyan IOpateye GOnwodi GAgboola O(2024)Application of Machine Learning Models for Predicting Students' Performance in Mathematics: A K-Fold Approach2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)10.1109/SEB4SDG60871.2024.10630249(1-9)Online publication date: 2-Apr-2024
https://doi.org/10.1109/SEB4SDG60871.2024.10630249
Kadu VMishra PDandhare SPatni JChandel PPathak SBahadure N(2024)Student’s Performance Prediction Using Machine Learning Algorithms- A Comparative Study2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.010.1109/OTCON60325.2024.10687450(1-6)Online publication date: 5-Jun-2024
https://doi.org/10.1109/OTCON60325.2024.10687450
Leelaluk STang CMinematsu TTaniguchi YOkubo FYamashita TShimada A(2024)Attention-Based Artificial Neural Network for Student Performance Prediction Based on Learning ActivitiesIEEE Access10.1109/ACCESS.2024.342955412(100659-100675)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3429554
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Index Terms

Predicting Students' Performance Using Machine Learning Algorithms
1. Computing methodologies
  1. Machine learning
    1. Cross-validation

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ICACS '22: Proceedings of the 6th International Conference on Algorithms, Computing and Systems

September 2022

132 pages

ISBN:9781450397407

DOI:10.1145/3564982

Copyright © 2022 ACM.

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Published: 30 January 2023

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ICACS 2022

ICACS 2022: 2022 The 6th International Conference on Algorithms, Computing and Systems

September 16 - 18, 2022

Larissa, Greece

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Cited By

Ale FIkpaya IDaniyan IOpateye GOnwodi GAgboola O(2024)Application of Machine Learning Models for Predicting Students' Performance in Mathematics: A K-Fold Approach2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)10.1109/SEB4SDG60871.2024.10630249(1-9)Online publication date: 2-Apr-2024
https://doi.org/10.1109/SEB4SDG60871.2024.10630249
Kadu VMishra PDandhare SPatni JChandel PPathak SBahadure N(2024)Student’s Performance Prediction Using Machine Learning Algorithms- A Comparative Study2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.010.1109/OTCON60325.2024.10687450(1-6)Online publication date: 5-Jun-2024
https://doi.org/10.1109/OTCON60325.2024.10687450
Leelaluk STang CMinematsu TTaniguchi YOkubo FYamashita TShimada A(2024)Attention-Based Artificial Neural Network for Student Performance Prediction Based on Learning ActivitiesIEEE Access10.1109/ACCESS.2024.342955412(100659-100675)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3429554
Zhang HTian M(2024)Unpacking the multi-dimensional nature of teacher competencies: a systematic reviewScandinavian Journal of Educational Research10.1080/00313831.2024.2369867(1-22)Online publication date: 3-Jul-2024
https://doi.org/10.1080/00313831.2024.2369867
Miranda EAryuni MRahmawati MHiererra SDian Sano A(2024)Machine learning's model-agnostic interpretability on the prediction of students' academic performance in video-conference-assisted online learning during the covid-19 pandemicComputers and Education: Artificial Intelligence10.1016/j.caeai.2024.100312(100312)Online publication date: Oct-2024
https://doi.org/10.1016/j.caeai.2024.100312
Gelorme Silveira MDe Souza LDe Oliveira Brandão LFranco Brandão A(2023)Learning Analytics to Support Education for All: Learning from the Past2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343491(1-8)Online publication date: 18-Oct-2023
https://doi.org/10.1109/FIE58773.2023.10343491

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