A Social Collaboration Model Between Guidance and Counseling Teacher and Parent to Guide Students During Distance Learning - Andina Amalia; Zulkipli Lessy; Miftahur Rohman (AL-ISHLAH: Jurnal Pendidikan, 2023)
It is apparent that during the Covid-19 pandemic, students are opting to learn from home. For guidance and counseling teachers, this situation is a challenge to guide children in online-based learning. For parents who are not used to access technology, it becomes a problem. The purpose of this study is to explore a collaboration model between guidance and counseling teachers and parents for attaining learning objectives and overcoming impediments. This research is a descriptive analysis, which explores and analyzes the collaboration between parents and counseling teachers in overcoming difficulties of distance learning and in supporting best efforts that parents should make to guide their children during online learning. The data collection technique is library research, with the primary sources being two journal articles from 2020-2021 about the roles of parents and guidance and counseling teachers in online learning. These sources were examined by a content analysis method. Distance learning requires collaboration with various parties engaged in different educational activities. Parents are involved in the process of mutual communication in the guidance and counseling program and development of students. They also assist in collecting data and information and they support the success of guidance and counseling services via extracurricular monitoring. These various roles are seen to make significant contributions to the effectiveness of the counseling program implementation in schools. In particular, the parental involvement in schooling methods and experiences can provide positive support and influence for the development of student growth. This study has limitation since it provides only few data which cover two journal articles. For future research, it is recommended to probe more than two journal articles and with a large data set.