ABSTRACT
Educational Data Mining (EDM) and Learning Analytics (LA) research have emerged as interesting areas of research, which are unfolding useful knowledge from educational databases for many purposes such as predicting students' success. The ability to predict a student's performance can be beneficial for actions in modern educational systems. Existing methods have used features which are mostly related to academic performance, family income and family assets; while features belonging to family expenditures and students' personal information are usually ignored. In this paper, an effort is made to investigate aforementioned feature sets by collecting the scholarship holding students' data from different universities of Pakistan. Learning analytics, discriminative and generative classification models are applied to predict whether a student will be able to complete his degree or not. Experimental results show that proposed method significantly outperforms existing methods due to exploitation of family expenditures and students' personal information feature sets. Outcomes of this EDM/LA research can serve as policy improvement method in higher education.
- N. R. Aljohani and H. C. Davis, "Learning analytics in mobile and ubiquitous learning environments," in 11th World Conference on Mobile and Contextual Learning, 2012.Google Scholar
- N. R. Aljohani, H. C. Davis, and S. W. Loke, "A comparison between mobile and ubiquitous learning from the perspective of human-computer interaction," International Journal of Mobile Learning and Organization, vol. 6, no. 3/4, pp. 218--231, 2012. Google ScholarDigital Library
- R. Asif, A. Merceron, and M. K. Pathan, "Investigating performance of students: a longitudinal study," in Fifth International Conference on Learning Analytics And Knowledge (LAK '15), New York, USA, 2015, pp. 108--112. Google ScholarDigital Library
- M. A. Chatti, A. L. Dyckhoff, U. Schroeder, and H Thüs, "A reference model for learning analytics," International Journal of Technology Enhanced Learning (IJTEL), vol. 4, no. 5/6, pp. 318--331, 2012. Google ScholarDigital Library
- N. Fournier, R. Kop, and H. Sitlia, "The value of learning analytics to networked learning on a personal learning environment," in 1st International Conference on Learning Analytics and Knowledge, 2011, pp. 104--109. Google ScholarDigital Library
- S. Kotsiantis, C. Pierrakeas, and P. Pintelas, "Predicting students' performance in distance learning using machine learning techniques," Applied Artificial Intelligence, vol. 18, no. 5, pp. 411--426, 2004. Google ScholarCross Ref
- E. Lotsari, V. Verykios, C. Panagiotakopoulos, and D. Kalles, "A Learning Analytics Methodology for Student Profiling," in Artificial Intelligence: Methods and Applications, 2014, pp. 300--312.Google ScholarCross Ref
- Y. Ma, B. Liu, C. K. Wong, P. S. Yu, and S. M. Lee, "Targeting the right students using data mining," in 6th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD '00), New York, USA, 2000, pp. 457--464. Google ScholarDigital Library
- B. Minaei-Bidgoli, D. A. Kashy, G. Kortemeyer, and W. F. Punch, "Predicting student performance: an application of data mining methods with an educational Web-based system," in 33rd Annual Frontiers in Education (FIE 2003), Westminster, CO, 2003.Google Scholar
- T. Mishra, D. Kumar, and Sangeeta Gupta, "Students' Employability Prediction Model through Data Mining," International Journal of Applied Engineering Research, vol. 11, no. 4, pp. 2275--2282, 2016.Google Scholar
- E. Osmanbegović and M. Suljić., "Data mining approach for predicting student performance," Economic Review, vol. 10, no. 1, pp. 3--12, 2012.Google Scholar
- O. K. Oyedotun, S. N. Tackie, and Ebenezer O. Olaniyi, "Data Mining of Students' Performance: Turkish Students as a Case Study," International Journal of Intelligent Systems and Applications, vol. 7, no. 9, pp. 20--27, 2015. Google ScholarCross Ref
- Z. A. Pardos, N. T. Heffernan, B. Anderson, C. L. Heffernan, and W. P. Schools, "Using fine-grained skill models to fit student performance with Bayesian networks," in Handbook of educational data mining., 2010, pp. 417--426. Google ScholarCross Ref
- P. J. Piety, D. T. Hickey, and M. J. Bishop, "Educational data sciences: Framing emergent practices for analytics of learning, organizations, and systems," in 4th International Conference on Learning Analytics and Knowledge, 2014, p. 193. Google ScholarDigital Library
- M. Ramaswami and R. Bhaskaran., "A CHAID based performance prediction model in educational data mining," International Journal of Computer Science, vol. 7, no. 1, pp. 10--18, 2010.Google Scholar
- C. Romero and S. Ventura, "Educational data mining: A survey from 1995 to 2005," Expert systems with applications, vol. 33, no. 1, pp. 135--146, 2007. Google ScholarDigital Library
- J. L. Santos, K. Verbert, S. Govaerts, and E. Duval, "Addressing learner issues with StepUp!: An evaluation," in International Conference on Learning Analytics and Knowledge, 2013, pp. 14--22. Google ScholarDigital Library
- B. Shalem, Y. Bachrach, J. Guiver, and C. M. Bishop, "Students, teachers, exams and MOOCs: Predicting and optimizing attainment in web-based education using a probabilistic graphical model," in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2014, pp. 82--97. Google ScholarDigital Library
- A. Sharabiani, F. Karim, A. Sharabiani, M. Atanasov, and H. Darabi, "An enhanced bayesian network model for prediction of students' academic performance in engineering programs," in IEEE Global Engineering Education Conference (EDUCON), 2014, pp. 832--837. Google ScholarCross Ref
- G., Siemens and P Long, "Penetrating the fog: Analytics in learning and education," EDUCAUSE Review, vol. 46, no. 5, 2011.Google Scholar
- S. Slater, S. Joksimović, V. Kovanovic, R. S. Baker, and D. Gasevic, "Tools for Educational Data Mining: A Review," Journal of Educational and Behavioral Statistics, 2016.Google Scholar
- G. S. Sree and C. Rupa., "Data Mining: Performance Improvement In Education Sector Using Classification And Clustering Algorithm," International Journal of Innovative Research and Development, vol. 2, no. 7, pp. 101--106, 2013.Google Scholar
- P. Strecht, L. Cruz, C. Soares, J. Mendes-Moreira, and R. Abreu, "A Comparative Study of Classification and Regression Algorithms for Modelling Students' Academic Performance," in International Educational Data Mining Society, 2015, pp. 392--395.Google Scholar
- M. M. A. Tair and A. M. El-Halees, "Mining educational data to improve students' performance: a case study," International Journal of Information, vol. 2, no. 2, pp. 140--146, 2012.Google Scholar
- S. K. Yadav, B. Bharadwaj, and S. Pal, "Data mining applications: A comparative study for predicting student's performance," International Journal of Innovative Technology and Creative Engineering, vol. 1, no. 12, pp. 13--19, 2011.Google Scholar
- B. J. Zimmerman and A. Kitsantas., "Comparing student's self-discipline and self-regulation measures and their prediction of academic achievement," Contemporary Educational Psychology, vol. 39, no. 2, pp. 145--155, 2014. Google ScholarCross Ref
Index Terms
- Predicting Student Performance using Advanced Learning Analytics
Recommendations
Using Learning Analytics to Promote Student Engagement and Achievement in Blended Learning: An Empirical Study
ICEBT '18: Proceedings of the 2018 2nd International Conference on E-Education, E-Business and E-TechnologyThe emergence of blended learning has huge impact on traditional learning. Blended learning has its own unique characteristics combining the advantages of traditional learning and online learning. However, some problems of blended learning have also ...
Student performance prediction model for early-identification of at-risk students in traditional classroom settings
MEDES '18: Proceedings of the 10th International Conference on Management of Digital EcoSystemsStudent performance prediction is one of the educational data mining tasks that has received great deal of attentions. It enables the educators to improve the quality and effectiveness of classroom management and to help students achieve better academic ...
The Black-Box Syndrome: Embracing Randomness in Machine Learning Models
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral ConsortiumAbstractAcknowledging the ‘curse’ of dimensionality, the educational sector has reasonably turned to automated (in some cases autonomous) solutions, in the process of extracting and communicating patterns in data, to promote innovative teaching and ...
Comments