THE EFFECTIVENESS OF THE NETWORK MODEL IN BLENDED LEARNING OF UNIVERSITAS TERBUKA'S LMS

Abstract

This research aims to evaluate the effectiveness of the network model in implementing blended learning in the Learning Management System (LMS) at the Open University. Blended learning, which combines face-to-face learning and online learning, has become an increasingly popular approach to distance education. The network model implemented in the LMS is expected to improve the quality of interaction and collaboration between students and lecturers, which is often a challenge in distance learning. 

The research method used is qualitative with a survey approach. This approach was chosen to gain an in-depth understanding of the experiences and perceptions of LMS users. Data was collected through an open questionnaire distributed to students and lecturers who actively use LMS in their learning process. This questionnaire is designed to collect information about various aspects of LMS use, including ease of access, interaction, collaboration, and impact on learning outcomes. 

The research results show that the network model implemented in the Open University LMS can increase interaction and collaboration between students and lecturers. Students report that they feel more connected to their lecturers and peers, despite being in various locations. In addition, this model also helps in overcoming geographic and time barriers that are often faced in distance learning. Students can access learning materials and communicate with lecturers anytime and anywhere, which increases flexibility and comfort in learning.

These findings suggest that the network model in blended learning can be an effective solution for improving the quality of education at the Open University. This model not only increases interaction and collaboration, but also provides the flexibility needed in distance learning. Recommendations for future research include further development of more adaptive network models and comparative studies with other institutions. Further research could also explore how these network models can be integrated with innovative technologies, such as artificial intelligence and learning analytics, to further enhance the student learning experience.