Abstract
Nowadays, recognizing and predicting students learning achievement introduces a significant challenge, especially in blended learning environments, where online (web-based electronic interaction) and offline (direct face-to-face interaction in classrooms) learning are combined. This paper presents a Machine Learning (ML) based classification approach for students learning achievement behavior in Higher Education. In the proposed approach, Random Forests (RF) and Support Vector Machines (SVM) classification algorithms are being applied for developing prediction models in order to discover the underlying relationship between students past course interactions with Learning Management Systems (LMS) and their tendency to pass/fail. In this paper, we considered daily students interaction events, based on time series, with a number of Moodle LMS modules as the leading characteristics to observe students learning performance. The dataset used for experiments is constructed based on anonymized real data samples traced from web-log files of students access behavior concerning different modules in a Moodle online LMS throughout two academic years. Experimental results showed that the proposed RF classification system has outperformed the typical SVMs classification algorithm.
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
References
Kim, J.H., Park, Y., Song, J., Jo, I.-H., Predicting students’ learning performance by using online behavior patterns in blended learning environments: comparison of two cases on linear and non-linear model. In: The International Conference on Educational Data Mining (EDM 2014), London, United Kingdom (2014)
Liebowitz, J., Frank, M.: Knowledge Management and E-learning. CRC Press (2010)
Romero, C., Ventura, S., Garca, E.: Data mining in course management systems: moodle case study and tutorial. Comput. Educ. 51, 368–384 (2008)
Romero, C., Ventura, S.: Data mining in education. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 3, 12–27 (2013)
Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 40(6), 601–618 (2010)
van Barneveld, A., Arnold, K.E., Campbell, J.P.: Analytics in higher education: establishing a common language. Educause Learn. Initiat. 1, 1–11 (2012)
Bienkowski, M., Feng, M., Means, B.: Enhancing teaching and learning through educational data mining and learning analytics: an issue brief. Office of Educational Technology, U.S. Department of Education (2012)
Wu, Q., Zhou, D.-X.: Analysis of support vector machine classification. J. Comput. Anal. Appl. 8, 99–119 (2006)
Zawbaa, H.M., El-Bendary, N., Hassanien, A.E., Abraham, A.: SVM-based soccer video summarization system. In: Proceedings of the Third IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC2011), Salamanca, Spain, pp. 7–11 (2011)
Zawbaa, H.M., El-Bendary, N., Hassanien, A.E., Kim, T.H.: Machine learning-based soccer video summarization system. In: Proceedings of the Multimedia, Computer Graphics and Broadcasting FGIT-MulGraB (2), Jeju Island, Korea, vol. 263, pp. 19–28. Springer (2011)
Suralkar, S.R., Karode, A.H., Pawade, P.W.: Texture image classification using support vector machine. Int. J. Comput. Appl. Technol. 3(1), 71–75 (2012)
Kulkarni, V.Y., Sinha, P.K.: Efficient learning of random forest classifier using disjoint partitioning approach. In: Proceedings of the World Congress on Engineering, vol. 2 (2013)
Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: IEEE 11th International Conference on Computer Vision (2007)
Nisbet, R., Elder, J., Miner, G.: Handbook of Statistical Analysis and Data Mining Applications. Academic Press (2009)
Acknowledgments
This work is funded by: the European Regional Development Fund (ERDF) and the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC); the Spanish Government and the European Regional Development Fund (ERDF) under project TACTICA; and the Spanish Ministry of Economy and Competitiveness under the National Science Program (TEC2014-54335-C4-3-R). This work is also partially funded by the European Commission under the Erasmus Mundus GreenIT project (3772227-1-2012-ES-ERA MUNDUS-EMA21). The authors also thank GRADIANT for its computing support and the University of Vigo for its e-learning service for their support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Nespereira, C.G., Elhariri, E., El-Bendary, N., Vilas, A.F., Redondo, R.P.D. (2016). Machine Learning Based Classification Approach for Predicting Students Performance in Blended Learning. In: Gaber, T., Hassanien, A., El-Bendary, N., Dey, N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-319-26690-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-26690-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26688-6
Online ISBN: 978-3-319-26690-9
eBook Packages: Computer ScienceComputer Science (R0)