Exploring social recommenders for teacher networks to address challenges of starting teachers

Abstract

The lack of a proper induction and organizational socialization is seen as one of the main reasons for beginning employees to leave the profession after a few years. It seems especially problematic for professions such as teaching. Teachers work in contexts that do not allow frequent and intense observations and interaction with others who could provide meaningful information and act as exemplars of good practice. Recently graduated teachers experience their first job as extremely challenging and they typically lack social support during their induction period. Teachers look for mentors and colleagues they can trust and discuss their problems with rather than content expert. Comprehensive induction programmes are characterised by accessing to good mentors, having relationships to and with peers, and professional development. Learning Networks, as online social networks designed to support professional development can fill this gap by offering an informal social support structure for professional development. They can improve the quality of the induction of starting teachers by providing means to share, exchange and acquire knowledge and experiences with other teachers. Not only can teachers find resources, moreover they get access to like-minded people with expertise and experience in the same domain. However, providing opportunities to meet like-minded people does not automatically result in the required social interactions for knowledge sharing. Therefore, we propose in this paper a social recommender that assists young teachers to find most suitable peers to address their problems. The social recommender is inspired by recommender systems from the e-commerce world that recommend most suitable content to a user. In this paper, we first analyse the problems that young teachers face during their induction phase. Second, we present online Learning Networks as a promising solution for the induction phase of starting teachers. Third, we evaluate promising recommendation approaches for the intended social recommender within the Learning Network, and we present some initial ideas on how to improve them to take into account the learner characteristics and also to meet the conditions of a Learning Network. Finally, we present a model and draw further conclusions for design and implementation of the Learning Network and the social recommender for starting teachers.