In this paper, we consider curriculum mining as an application of process mining in the domain of education. The basic objective of the curriculum mining is to construct a registration pattern model by using logs of registration data. However, subject registration patterns of students are very unstructured and complicated, called a spaghetti model, because it has a lot of different cases and high diversity of behaviors. In general, it is typically difficult to develop and analyze registration patterns. In the literature, there was an effort to handle this issue by using clustering based on the features of students and behaviors. However, it is not easy to obtain them in general since they are private and qualitative. Therefore, in this paper, we propose a new framework of curriculum mining applying K-means clustering based on subject attributes to solve the problems caused by unstructured process model obtained. Specifically, we divide subject’s attribute data into two parts : categorical and numerical data. Categorical attribute has subject name, class classification, and research field, while numerical attribute has ABEEK goal and semester information. In case of categorical attribute, we suggest a method to quantify them by using binarization. The number of clusters used for K-means clustering, we applied Elbow method using R-squared value representing the variance ratio that can be explained by the number of clusters. The performance of the suggested method was verified by using a log of student registration data from an ‘A university’ in terms of the simplicity and fitness, which are the typical performance measure of obtained process model in process mining.

1. 서 론
 2. 관련 연구 동향
  2.2 비구조화 프로세스(Unstructured Process)
 2.1 교육적 프로세스(커리큘럼) 마이닝
 3. 군집화 기반 프로세스 마이닝
  3.1 프레임 워크 제안
  3.2 데이터 전처리
  3.3 교과목 속성 기반 군집화
  3.4 수강패턴 모델 도출
 4. 수강패턴 분석
  4.1 군집별 특성 분석
  4.2 수강패턴 모델 분석
  4.3 수강 행동패턴 분석
 5. 결 론
 Acknowledgement

• 주우민(아주대학교 산업공학과) | Woo-Min Joo
• 최진영(아주대학교 산업공학과) | Jin Young Choi Corresponding Author