Abstract
Knowledge extracted from educational data can be used by the educators to obtain insights about how the quality of teaching and learning must be improved, how the factors affect the performance of the students and how qualified students can be trained for the industry requirements. This research focuses on classifying a knowledge based system using a set of rules. The main purpose of the study is to analyze the most influencing attributes of the students for their module performance in tertiary education in Sri Lanka. The study has gathered data about students in a reputed degree awarding institute in Sri Lanka and used three different data mining algorithms to predict the influential factors and they have been evaluated for interestingness using objective oriented utility based method. Subsequently, age of the students, their family background with regard to parents’ occupations, average monthly income of the family, their English language fluency level and knowledge of Mathematics were identified as the interesting factors. The findings of this study will positively affect the future decisions made regarding the progress of the students’ performance, quality of the education process and the future of the education provider.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kasthuriarachchi, K.T.S., Liyanage, S.R., Bhatt, C.M.: A data mining approach to identify the factors affecting the academic success of tertiary students in Sri Lanka. In: Caballé, S., Conesa, J. (eds.) Software Data Engineering for Network eLearning Environments. Lecture Notes on Data Engineering and Communications Technologies, vol. 11. Springer, Cham (2018)
Amershi, S., Conati, C.: Combining unsupervised and supervised classification to build user models for exploratory learning environments. J. Educ. Data Min. 18–71 (2009)
Antunes, C.: Acquiring background knowledge for intelligent tutoring systems. In: Proceedings of International Conference on Educational Data Mining, Montreal, QC, Canada, pp. 18–27 (2008)
Chen, C., Chen, M., Li, Y.: Mining key formative assessment rules based on learner profiles for web-based learning systems. In: Proceedings of IEEE International Conference on Advanced Learning Technology, Niigata, Japan, pp. 1–5 (2007)
Herrera, O.L.: Investigation of the role of pre- and post-admission variables in undergraduate institutional persistence, using a Markov student flow model. PhD Dissertation, North Carolina State University, USA (2006)
Jun, J.: Understanding dropout of adult learners in e-learning. PhD Dissertation, The University of Georgia, USA (2005)
Kay, J., Maisonneuve, N., Yacef, K., Zaiane, O.R.: Mining patterns of events in students’ teamwork data. In: Proceedings of Workshop Educational Data Mining, Taiwan, pp. 1–8 (2006)
Kember, D.: Open Learning Courses for Adults: A Model of Student Progress. Education Technology, Englewood Cliffs (1995)
Nandeshwar, A., Chaudhari, S.: Enrollment prediction models using data mining (2009). http://nandeshwar.info/wp-content/uploads/2008/11/DMWVU_Project.pdf. Last accesses 12 May 2018
Romesburg, H.C.: Cluster Analysis for Researchers. Robert E. Krieger Publishing Co., Malabar, FL (1990)
Rus, V., Lintean, M., Azevedo, R.: Automatic detection of student mental models during prior knowledge activation in Meta Tutor. In: Proceedings of International Conference on Educational Data Mining, Cordoba, Spain, pp. 161–170 (2009)
Shen, R., Han, P., Yang, F., Yang, Q., Huang, J.: Data mining and case-based reasoning for distance learning. J. Distance Educ. Technol. 46–58 (2003)
Tian, F., Wang, S., Zheng, C., Zheng, Q.: Research on e-learning personality group based on fuzzy clustering analysis. In: Proceedings of International Conference on Computer Supported Cooperative Work Design, Xian, China, pp. 1035–1040 (2008)
Tsai, C.J., Tseng, S.S, Lin, C.Y.: A two-phase fuzzy mining and learning algorithm for adaptive learning environment. In: Proceedings of International Conference on Computer Science, San Francisco, pp. 429–438 (2001)
Shen, Y.D., Zhang, Z., Yang, Q.: Objective-oriented utility based association mining. In: Proceeding of the 2002 IEEE International Conference on Data Mining, Maebashi City, Japan, pp. 426–433 (December 2002)
Christoper, J.J., Nehemiah, K.H., Arputharaj, K.: Knowledge-based systems and interestingness measures: analysis with clinical datasets. CIT J. Comput. Inf. Technol. 24(1), 65–78 (2016)
Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), Article 9 (September 2006)
Acknowledgements
The authors would like to thank Sri Lanka Institute of Information Technology in Sri Lanka for the support given by providing a very valuable data set for the analysis.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kasthuriarachchi, K.T.S., Liyanage, S.R. (2019). Predicting Students’ Academic Performance Using Utility Based Educational Data Mining. In: Hung, J., Yen, N., Hui, L. (eds) Frontier Computing. FC 2018. Lecture Notes in Electrical Engineering, vol 542. Springer, Singapore. https://doi.org/10.1007/978-981-13-3648-5_4
Download citation
DOI: https://doi.org/10.1007/978-981-13-3648-5_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3647-8
Online ISBN: 978-981-13-3648-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)