Open Educational Resources (OER) repositories provide teachers with a wide range of learning resources (LRs), enabling them to design various learning sequences. However, search & select in large OER repositories can be a daunting task for teachers. Incorporating peer recommendations, as is common in online marketplaces, is becoming a popular solution that seeks to exploit the wisdom of the crowd for this task. However, teachers are often reluctant to take a contributory role and provide social recommendations. In addition, little is known about the actual value of social recommendations as a search aid. In this research, we implemented a “light-weight” socially-based recommender system (RS) within a large OER repository that includes social network features. We examined two aspects of the socially-based recommendation mechanisms. First, their utility as search aids that assist teachers in searching and selecting suitable LRs, and second, their impact on teachers' incentives to share recommendations that can assist fellow teachers. To study these two aspects, we examined two science teacher communities using this repository. The results demonstrated the incentivising power of social rewards, and the value of social recommendations as means for search & select. However, we also observed a heterogeneous effect of social features on teachers' behaviour. To explore the factors that may explain these differences, we employed a mixed-method approach, combining qualitative, quantitative, and Social Network Analysis methods. Triangulation of the findings underline the relation between the strength of the social ties within the teachers’ community and the effectiveness of socially-based features.

开放教育资源(OER) 存储库为教师提供广泛的学习资源 (LR)，使他们能够设计各种学习序列。然而，在大型开放教育资源库中搜索和选择对于教师来说可能是一项艰巨的任务。正如在线市场中常见的那样，结合同行推荐正在成为一种流行的解决方案，旨在利用群众的智慧来完成这项任务。然而，教师往往不愿意发挥贡献作用并提供社会建议。此外，人们对社交推荐作为搜索辅助工具的实际价值知之甚少。在这项研究中，我们在一个包含社交网络功能的大型 OER 存储库中实现了一个“轻量级”基于社交的推荐系统 (RS)。我们研究了基于社交的推荐机制的两个方面。首先，它们作为搜索辅助工具的实用性，可以帮助教师搜索和选择合适的 LR；其次，它们对教师分享可以帮助其他教师的建议的积极性产生影响。为了研究这两方面，我们使用这个存储库检查了两个科学教师社区。结果证明了社交奖励的激励作用，以及社交推荐作为搜索和选择手段的价值。然而，我们也观察到社会特征对教师行为的异质影响。为了探索可能解释这些差异的因素，我们采用了混合方法，结合了定性、定量和社交网络分析方法。研究结果的三角测量强调了教师社区内社会联系的强度与基于社会的特征的有效性之间的关系。