Conventional practices for recording student attendance in a classroom, such as roll-call and sign-in-sheet, are proven to be inefficient in terms of time and staff-workload. In addition, they are also prone to human error and bogus attendance, which introduce inaccuracy in the recorded data. A number of studies have been conducted to improve the way we record class attendance. However, some of the proposed solutions are costly and impractical. Most solutions also fail to address bogus attendance. This paper presents a low cost solution in recording student attendance. Attendance is recorded on class photographs, students' faces are automatically located using a face detection technique, and students then registered their attendance by simply identifying their face on the records. Mobile applications were developed for both students and lecturers as the primary interfaces to interact with the system.

Educational data mining(EDM) has been used widely to investigate data that come from a learning process, including blended learning. This study explores educational data from a Learning Course Management System (LMS) and academic data in two courses of Management Study Program, Faculty of Economics at Maranatha Christian University, which are Change Management (CM) in undergraduate program and Creative Leadership (CL) in master degree program as case studies. The main aim of this research is to provide feedback for the learning process through the LMS in order to improve students' achievement. EDM methods used are association rule mining and J48 classification. The results of association rule mining are two sets of interesting rules for the CM course and three sets of rules for CL course. Using J48 classification, two J48 pruned trees are obtained for each course. Based on those results, some suggestions are proposed to enhance the LMS and to encourage students' involvement in blended learning.