Implementation of Player Behavior-Based Adaptive Interfaces in E-Learning Programming
The pandemic has caused a significant impact on the educational sector’s implementation. The teaching and learning process that was previously carried out face-to-face now must be conducted online. Online learning utilizing e-learning is very beneficial for learners because it can be accessed online anytime, anywhere. Not only is it expected to be a medium to share material files that serve as learning supports, but e-learning is also required to replace the teachers’ or lecturers’ role in the classroom. In the teaching and learning process, it is essential to understand the students’ conditions and behaviors. This knowledge of students’ conditions and circumstances during the learning process can be used as resources for improving the quality and students’ learning process. Most of the existing e-learning has not been equipped with features to detect the students’ state while using the system. In this research, an adaptive interface was applied as a reflection of e-learning that could adapt to the characteristics of user behavior. The performance testing results showed that the time required for the log data with 950 active users was 23,35 ms. Meanwhile, based on the functionality test, the system succeeded in displaying the interface according to the cluster member.
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