Abstract
Predicting students’ performance has become a major need in most educational institutions. This is necessary to support at-risk students, ensure their retention, provide top-notch learning opportunities, and improve the university’s efficiency and competitiveness. Even so, this may be difficult to collect records for medium-sized institutions., particularly those that concentrate on graduate and postgraduate programs and have a limited number of applicant records available for examination. Therefore, the prime objective of this research is to demonstrate the viability of constructing and training a predictive model with a credible accuracy rate using a modest dataset size. This study also investigates the possibilities of employing, visualization and clustering techniques to identify the key factors in the set of data used to build classification models. The most accurate model was determined by evaluating the best indications through various machine-learning methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Manjarres, A.V., Sandoval, L.G.M., Suárez, M.S.: Data mining techniques applied in educational environments: literature review. Digital Educ. Rev. (33), 235–266 (2018)
Martin, F., Chen, Y., Moore, R.L., Westine, C.D.: Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Educ. Technol. Res. Dev. 68(4), 1903–1929 (2020)
Comendador, B.E.V., Rabago, L.W., Tanguilig, B.T.: An educational model based on Knowledge Discovery in Databases (KDD) to predict learner’s behavior using classification techniques. In: 2016 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), pp. 1–6. IEEE(2016)
Araque, F., Roldán, C., Salguero, A.: Factors influencing university drop out rates. Comput. Educ. 53(3), 563–574 (2009)
Mueen, A., Zafar, B., Manzoor, U.: Modeling and predicting students’ academic performance using data mining techniques. Int. J. Modern Educ. Comput. Sci. 8(11), 36 (2016)
Asif, R., Merceron, A., Ali, S.A., Haider, N.G.: Analyzing undergraduate students’ performance using educational data mining. Comput. Educ. 113, 177–194 (2017)
Naseer, M., Zhang, W., Zhu, W.: Prediction of coding intricacy in a software engineering team through machine learning to ensure cooperative learning and sustainable education. Sustainability 12(21), 8986 (2020)
Villegas-Ch, W., Román-Cañizares, M., Palacios-Pacheco, X.: Improvement of an online education model with the integration of machine learning and data analysis in an LMS. Appl. Sci. 10(15), 5371 (2020)
Conijn, R., Snijders, C., Kleingeld, A., Matzat, U.: Predicting student performance from LMS data: a comparison of 17 blended courses using Moodle LMS. IEEE Trans. Learn. Technol. 10(1), 17–29 (2016)
Riestra-González, M., del Puerto Paule-Ruíz, M., Ortin, F.: Massive LMS log data analysis for the early prediction of course-agnostic student performance. Comput. Educ. 163, 104108 (2021)
Gallego-Romero, J.M., Alario-Hoyos, C., Estévez-Ayres, I., Delgado Kloos, C.: Analyzing learners’ engagement and behavior in MOOCs on programming with the Codeboard IDE. Educ. Technol. Res. Dev. 68(5), 2505–2528 (2020)
Demmans Epp, C., Phirangee, K., Hewitt, J., Perfetti, C.A.: Learning management system and course influences on student actions and learning experiences. Educ. Technol. Res. Dev. 68(6), 3263–3297 (2020)
Julia, K., Marco, K.: Educational scalability in MOOCs: analysing instructional designs to find best practices. Comput. Educ. 161, 104054 (2021)
Zohair, A., Mahmoud, L.: Prediction of Student’s performance by modelling small dataset size. Int. J. Educ. Technol. High. Educ. 16(1), 1–18 (2019)
Ingrassia, S., Morlini, I.: Neural network modeling for small datasets. Technometrics 47(3), 297–311 (2005)
Sharma, A., Paliwal, K.K.: Linear discriminant analysis for the small sample size problem: an overview. Int. J. Mach. Learn. Cybern. 6(3), 443–454 (2015)
Pasini, A.: Artificial neural networks for small dataset analysis. J. Thoracic Dis. 7(5), 953 (2015)
Naicker, N., Adeliyi, T., Wing, J.: Linear support vector machines for prediction of student performance in school-based education. Math. Prob. Eng. 2020 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hafdi, Z.S., El Kafhali, S. (2023). Student Performance Prediction in Learning Management System Using Small Dataset. In: Hassanien, A.E., et al. The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023. AICV 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 164. Springer, Cham. https://doi.org/10.1007/978-3-031-27762-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-27762-7_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27761-0
Online ISBN: 978-3-031-27762-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)