| dc.description.abstract | With the arising of technology and the extensive utilization of Internet, online open education resources are also emerging. Massive Open Online Course (MOOC) is the latest remote education indicator recently. The features of MOOC are open access and its scalability. In early 2014, the Ministry of Education of Taiwan developed the MOOCs program. By the open source provided by Coursera, Udacity, and Open edX, various colleges and universities developed their own open online courses, aiming at creating a learning environment for students free from the restriction on the time, space and location. In addition, based on the learning data from the extensive participation of the students, it is intended to provide more assistance to the students according to the learning situation.
This study is directed to the students′ participation of the online course functions provided by the large scale MOOC courses, including reading online e-book content, watching online course videos, and the online course tests. Through the records (logs) generated by the students′ continuous operation, after the data is collected, processed, and defined, the behavior is simplified into an encoding. For example, the information of a series learning activities by a user includes: user name, Session, operation status, time of occurrence, etc.
Through a series of analysis methods, is it possible to make the learning activities sequential and then to find a common learning sub-sequences between the students according to different sequential patter mining methods (such as, Sequential Patter Mining, etc.)? After mining the students learning sub-sequences, is it possible to find the correlation between the learning performance and the typical learning pattern based on the learning sub-sequences? What are the main components of the learning pattern found by Principal Component Analysis (PCA) of Exploratory Factor Analysis (EFA)? Afterward, what are the learning performance differences by observing the student groups under different performances using the Multiple Factor Analysis (MFA)?
According to the result of the present research, the adopted Sequence Pattern Detections method shows that the students′ common learning behavior has a positive/negative effects on learning performances. According to the types of learning sequences of the students on the online courses, four major learning strategies are defined.
For future study, further research may be conducted on how the results of the present solution may aid to provide suggestions to the students by the auxiliary learning system of the online course system in order to improve the learning performance when it is observed that the learning pattern of the students tends to a more improper learning strategy during the online course learning.
Keywords: MOOCs (Massive Open Online Courses), activity sequence, Principal Component Analysis, Exploratory Factor Analysis, Multiple Factor Analysis
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