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Abstract
Higher education is becoming a big business, with huge investments in IT technology supporting Learning
Management System (LMS). LMS is a software application which is used by student in learning process. The
purpose of this paper is to analyze the use of LMS based on activities and results of student learning and seeks
to address the interface between individual learning that use LMS data to support decision-making and course
design. A model of data warehouse is built to evaluate by means of a case study the usefulness validity of
analyses performed. Data mining is considered the non-trivial extraction of implicit, previously unknown, and
potentially useful information from data. This paper analyzes students’ activities records using LMS then
building a data warehouse, and data mining. The paper finds that after mining data, students can be classified
into different groups according to the activities using LMS. The data mining results shows that students can be
categorized into four clusters; each cluster has its own characteristics. Integration of data warehouse, data
mining and LMS produce a framework that uses transactional data to be transformed into knowledge that can
be used to maximize the teaching-learning process of lecturer and students.
Management System (LMS). LMS is a software application which is used by student in learning process. The
purpose of this paper is to analyze the use of LMS based on activities and results of student learning and seeks
to address the interface between individual learning that use LMS data to support decision-making and course
design. A model of data warehouse is built to evaluate by means of a case study the usefulness validity of
analyses performed. Data mining is considered the non-trivial extraction of implicit, previously unknown, and
potentially useful information from data. This paper analyzes students’ activities records using LMS then
building a data warehouse, and data mining. The paper finds that after mining data, students can be classified
into different groups according to the activities using LMS. The data mining results shows that students can be
categorized into four clusters; each cluster has its own characteristics. Integration of data warehouse, data
mining and LMS produce a framework that uses transactional data to be transformed into knowledge that can
be used to maximize the teaching-learning process of lecturer and students.