Sequential pattern mining method for analysis of programming learning history based on the learning process

This research aims to realize a novel method for learning history analysis based on the learning processes in programming exercise classes. This paper proposes the sequential pattern mining method specialized for analysis of learning histories of programing learning. This paper initially describes a data processing method which investigates learning transitions as sequences based on the analyses of learners´ source codes and compile errors generated in their exercises. Next, this paper describes an analysis support tool. This tool assists collection of learning histories, generation of sequence based on analysis of the histories, extraction of the noteworthy patterns based on SPADE algorithm and acquisition of findings from the extracted patterns. This tool enables to effectively analyze the relationships between learning processes in programming exercises and learning situations. Such analysis can contribute to practical grasping of learning situations in accordance with learning process and acquisition of advanced findings based on it.