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<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">We have developed a meta-system that generates program animation systems. The generated animation systems visually display changes in program actions and help students (novice programmers) understand them. The animation systems also accumulate historical records of the students’ operations as they execute a program step by step while trying to understand it. By analyzing accumulated records, the meta-system pinpoints common areas of dif- ficulty and their causes for the lecturer. To develop this meta-system, we first analyzed the relation between difficult to understand parts and records of which control operations students applied when using the program animation system. For this analysis, we developed a function enabling the program animation system to record each student’s history of operations. Next, we devised a technique to predict which parts of a program would be difficult for students to understand. Finally, we developed a generator of program animation systems based on this technique. Consequently, this meta-</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"> <span class="text">system enables lecturers to monitor the classroom learning of students in real-time and provide support to groups of students experiencing common difficulties.</span></span></p>

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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.