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Finding Key Integer Values in Many Features for Learners' Academic Performance Prediction

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Published:20 December 2017Publication History

ABSTRACT

In recent years, along with the proliferation of the learning management system (LMS), a large amount of data regarding the interaction between the system and the learners has been accumulated. Correspondingly, various data mining methods have been applied to these data. In order to employ a suitable computational model that is at the core of the data mining method and is not automatically acquired by the mining method itself, it is important to make or find various reasonable hypotheses for target variables. In this paper, we propose a method for analyzing closely the degree to which the explanatory variables represented in integer value contributes to predicting categorical objective variables, such as a learner's academic performance. Specifically, we describe that a decision tree combining support vector machines (SVM) achieves accuracy consistent with existing research, and it contributes further extraction of particular explanatory values from the integer features. Before making a model with SVM, our proposal method expands original features represented by integer value to corresponding binary features. With this expansion of original features, we can identify the key values that closely relate to a learner's academic performance from behavioral features gathered from LMS. Identifying such key values in specific features plays an important role in developing a hypothesis that explains the objective variables, using them as explanatory variables. We believe that closer analysis of these key explanatory values will find latent knowledge that can improve learners' academic abilities

References

  1. Amrieh, E. A., Hamtini, T., and Aljarah, I. 2015. Preprocessing and analyzing educational data set using X-API for improving student's performance. IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), 1--5.Google ScholarGoogle Scholar
  2. Amrieh, E.A., Hamtini, T., and Aljarah, I. 2016. Mining Educational Data to Predict Student's academic Performance using Ensemble Methods. International Journal of Database Theory and Application. 9, 119--136.Google ScholarGoogle ScholarCross RefCross Ref
  3. Cortes, C. and Vapnik, V. 1995. Support Vector Networks. Machine Learning. 20, 273--297. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Joachims, T. 2006. Training Linear SVMs in Linear Time. In Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), 217--226. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Joachims, T. and Yu, C. N. J. 2009. Sparse Kernel SVMs via Cutting-Plane Training. Machine Learning, 76 (2-3), 179--193. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Kakasevski, G., Mihajlov, M., Arsenovski, S., and Chungurski, S. 2008. Evaluating usability in learning management system Moodle. The 30th IEEE International Conference on Information Technology Interfaces (ITI 2008). 613--618.Google ScholarGoogle Scholar
  7. Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z., and Wolff, A. 2015. OU Analyse: Analysing At-Risk Students at The Open University. Learning Analytics Review, LAK15-1.Google ScholarGoogle Scholar
  8. Sakai, T. and Hirokawa, S. 2012. Feature words that classify problem sentence in scientific article Authors of Document. In Proceedings of iiWAS2012. 360--367. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Shahiri, A. M. and Husain, W. 2015. A Review on Predicting Student's Performance Using Data Mining Techniques. Procedia Computer Science. 72, 414--422.Google ScholarGoogle ScholarCross RefCross Ref
  10. Taira, H. and Haruno, M. 1999. Feature Selection in SVM Text Classification. In Proceedings of AAI99. 480--486. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Romero, C. and Ventura, S. 2007. Educational data mining: A survey from 1995 to 2005. Expert systems with applications. 33(1), 135--146. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Romero, C., Ventura S., and García, E. 2008. Data mining in course management systems: Moodle case study and tutorial, Computers & Education. 51(1), 368--384. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Romero, C. and Ventura, S. 2010. Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 40(6), 601--618. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Wang, R. et al. 2014. StudentLife: Assessing Mental Health, Academic Performance and Behavioral Trends of College Students using Smartphones. In Proceedings of the ACM Conference on Ubiquitous Computing, 3--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Zhou, Z. H. 2012. Ensemble methods: foundations and algorithms, CRC Press. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      cover image ACM Other conferences
      ICETC '17: Proceedings of the 9th International Conference on Education Technology and Computers
      December 2017
      270 pages
      ISBN:9781450354356
      DOI:10.1145/3175536

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      Publication History

      • Published: 20 December 2017

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