TRAINING PREFERENCES OF ADULT LEARNERS IN ONLINE LEARNING ENVIRONMENTS: DISTANCE LEARNING GATE

Abstract

Adults are independent learners and are expected to direct their own learning. Nowadays, there are many MOOC platforms preferred by adult learners. Many states have adapted to these developments and established an online learning platform in order to carry out the training and development activities of civil servants. The Presidency of the Republic of Türkiye Human Resources Office has established the Distance Learning Gate (DLG) platform in order to carry out educational activities. In this study, learning preferences of adult learners were determined in order to improve the relevant online learning environment and to structure appropriate trainings for public personnel. For this purpose, the training preferences of adult learners were determined according to their title groups and education levels. The participants of the research consisted of 4864 civil servants who received training from the DLG platform. According to the results, it was determined that adult learners preferred personal development training the most and professional development training in the last place. It is seen that the servants in the service title group that watched the most training in all categories according to the title groups. It is seen that the groups that received the least education are the senior manager and manager title group. On the other hand, it has been determined that the number of adult learners' training and the education categories differ according to the education level. According to the findings, it is seen that those who watched the most education are adults at the compulsory primary education level, while those who watched the least education are those who are at the graduate level. This research includes the first phase of a series of research and a design-based research. As a first step, the training preferences of adult learners were determined. In the next stage, a recommendation system will be integrated into the DLG platform based on the determined profiles.