Predicting learning result of learner in e-learning course with feature selection using SVM

Yuki Kitanaka, Kazuhiro Takeuchi, Sachio Hirokawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, data mining targeting educational data has been widely performed. With the spread of the e-learning system, activities of various learners have been recorded. By analyzing this record, research is being conducted to evaluate the achievement level of the learner and to find hidden problems. In this paper, we compare the existing method and the method by SVM using feature selection for the method of classifying the final result from the learner's activity record. This confirms the effectiveness of the method using feature selection. Next, we confirmed that the click stream which is the activity data in the elearning system is more effective than the learner's profile in classification of grades. In the classification of learners with good grades, the connection records and the number of clicks in the latter period of the learning period are important factors, further the difference in the important features by the grade evaluation was shown.

Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Education Technology and Computers, ICETC 2017
PublisherAssociation for Computing Machinery
Pages122-125
Number of pages4
ISBN (Electronic)9781450354356
DOIs
Publication statusPublished - Dec 20 2017
Event9th International Conference on Education Technology and Computers, ICETC 2017 - Barcelona, Spain
Duration: Dec 20 2017Dec 22 2017

Publication series

NameACM International Conference Proceeding Series

Other

Other9th International Conference on Education Technology and Computers, ICETC 2017
CountrySpain
CityBarcelona
Period12/20/1712/22/17

Fingerprint

Feature extraction
Data mining
Learning systems

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this

Kitanaka, Y., Takeuchi, K., & Hirokawa, S. (2017). Predicting learning result of learner in e-learning course with feature selection using SVM. In Proceedings of the 9th International Conference on Education Technology and Computers, ICETC 2017 (pp. 122-125). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3175536.3175567

Predicting learning result of learner in e-learning course with feature selection using SVM. / Kitanaka, Yuki; Takeuchi, Kazuhiro; Hirokawa, Sachio.

Proceedings of the 9th International Conference on Education Technology and Computers, ICETC 2017. Association for Computing Machinery, 2017. p. 122-125 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kitanaka, Y, Takeuchi, K & Hirokawa, S 2017, Predicting learning result of learner in e-learning course with feature selection using SVM. in Proceedings of the 9th International Conference on Education Technology and Computers, ICETC 2017. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 122-125, 9th International Conference on Education Technology and Computers, ICETC 2017, Barcelona, Spain, 12/20/17. https://doi.org/10.1145/3175536.3175567
Kitanaka Y, Takeuchi K, Hirokawa S. Predicting learning result of learner in e-learning course with feature selection using SVM. In Proceedings of the 9th International Conference on Education Technology and Computers, ICETC 2017. Association for Computing Machinery. 2017. p. 122-125. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3175536.3175567
Kitanaka, Yuki ; Takeuchi, Kazuhiro ; Hirokawa, Sachio. / Predicting learning result of learner in e-learning course with feature selection using SVM. Proceedings of the 9th International Conference on Education Technology and Computers, ICETC 2017. Association for Computing Machinery, 2017. pp. 122-125 (ACM International Conference Proceeding Series).
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