Sequential Pattern Mining System for Analysis of Programming Learning History

Shoichi Nakamura, Kaname Nozaki, Hiroki Nakayama, Yasuhiko Morimoto, Youzou Miyadera

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

3 Citations (Scopus)

Abstract

The overall goal of this research is to establish the methodology for analyzing learning history data of programming exercise in accordance with learning processes. To achieve this goal, we developed a theoretical method of sequential pattern mining specialized for learning histories in programming exercise. On the basis of this method, we designed a system for analyzing the programming learning history data. This system consists of functions that are responsible for collection of learning histories, generation of sequence from the collected learning histories, extraction of noteworthy patterns from a set of sequences, and acquisition of findings from the extracted patterns. This paper mainly describes the functions of the system and their implementation along with an overview of the sequential pattern mining method.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Data Science and Data Intensive Systems; 8th IEEE International Conference Cyber, Physical and Social Computing; 11th IEEE International Conference on Green Computing and Communications and 8th IEEE International Conference on Internet of Things, DSDIS/CPSCom/GreenCom/iThings 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages69-74
Number of pages6
ISBN (Electronic)9781509002146
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event2015 IEEE International Conference on Data Science and Data Intensive Systems; 8th IEEE International Conference Cyber, Physical and Social Computing; 11th IEEE International Conference on Green Computing and Communications and 8th IEEE International Conference on Internet of Things, DSDIS/CPSCom/GreenCom/iThings 2015 - Sydney, Australia
Duration: 2015 Dec 112015 Dec 13

Other

Other2015 IEEE International Conference on Data Science and Data Intensive Systems; 8th IEEE International Conference Cyber, Physical and Social Computing; 11th IEEE International Conference on Green Computing and Communications and 8th IEEE International Conference on Internet of Things, DSDIS/CPSCom/GreenCom/iThings 2015
CountryAustralia
CitySydney
Period15/12/1115/12/13

Keywords

  • programming learning; sequential pattern mining; learning history; educational data mining; education support

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Sequential Pattern Mining System for Analysis of Programming Learning History'. Together they form a unique fingerprint.

  • Cite this

    Nakamura, S., Nozaki, K., Nakayama, H., Morimoto, Y., & Miyadera, Y. (2015). Sequential Pattern Mining System for Analysis of Programming Learning History. In Proceedings - 2015 IEEE International Conference on Data Science and Data Intensive Systems; 8th IEEE International Conference Cyber, Physical and Social Computing; 11th IEEE International Conference on Green Computing and Communications and 8th IEEE International Conference on Internet of Things, DSDIS/CPSCom/GreenCom/iThings 2015 (pp. 69-74). [7396483] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSDIS.2015.120