2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS) (2015)
Dec. 11, 2015 to Dec. 13, 2015
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.
History, Programming profession, Data mining, Algorithm design and analysis, Syntactics, Servers
S. Nakamura, K. Nozaki, H. Nakayama, Y. Morimoto and Y. Miyadera, "Sequential Pattern Mining System for Analysis of Programming Learning History," 2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS), Sydney, Australia, 2015, pp. 69-74.