TY - GEN
T1 - Sequential Pattern Mining System for Analysis of Programming Learning History
AU - Nakamura, Shoichi
AU - Nozaki, Kaname
AU - Nakayama, Hiroki
AU - Morimoto, Yasuhiko
AU - Miyadera, Youzou
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - programming learning; sequential pattern mining; learning history; educational data mining; education support
UR - http://www.scopus.com/inward/record.url?scp=84964493014&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964493014&partnerID=8YFLogxK
U2 - 10.1109/DSDIS.2015.120
DO - 10.1109/DSDIS.2015.120
M3 - Conference contribution
AN - SCOPUS:84964493014
T3 - 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
SP - 69
EP - 74
BT - 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
A2 - Yang, Laurence T.
A2 - Chen, Jinjun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 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
Y2 - 11 December 2015 through 13 December 2015
ER -