Learning personalization has proven its effectiveness in enhancing learner performance. Therefore, modern digital learning platforms have been increasingly depending on recommendation systems to offer learners personalized suggestions of learning materials. Learners can utilize those recommendations to acquire certain skills for the labor market or for their formal education. Personalization can be based on several factors, such as personal preference, social connections or learning context. In an educational environment, the learning context plays an important role in generating sound recommendations, which not only fulfill the preferences of the learner, but also correspond to the pedagogical goals of the learning process. This is because a learning context describes the actual situation of the learner at the moment of requesting a learning recommendation. It provides information about the learner current state of knowledge, goal orientation, motivation, needs, available time, and other factors that reflect their status and may influence how learning recommendations are perceived and utilized. Context aware recommender systems have the potential to reflect the logic that a learning expert may follow in recommending materials to students with respect to their status and needs. In this paper, we review the state-of-the-art approaches for defining a user learning-context. We provide an overview of the definitions available, as well as the different factors that are considered when defining a context. Moreover, we further investigate the links between those factors and their pedagogical foundations in learning theories. We aim to provide a comprehensive understanding of contextualized learning from both pedagogical and technical points of view. By combining those two viewpoints, we aim to bridge a gap between both domains, in terms of contextualizing learning recommendations.

翻译：学习个性化已被证实能有效提升学习者的学习成效。因此，现代数字化学习平台日益依赖推荐系统，为学习者提供学习资源的个性化建议。学习者可利用这些推荐获取特定技能，以满足劳动力市场或正规教育的需求。个性化可基于多种因素实现，例如个人偏好、社交关系或学习语境。在教育环境中，学习语境在生成优质推荐方面发挥着重要作用，这些推荐不仅契合学习者的偏好，还需对应学习过程的教学目标。这是因为学习语境描述了学习者在请求学习推荐时的实际情境，提供了关于学习者当前知识状态、目标导向、动机、需求、可用时间及其他反映其状况并可能影响学习推荐感知与利用方式的因素信息。情境感知推荐系统具备模拟教学专家根据学生状况与需求推荐学习材料逻辑的潜力。本文综述了定义用户学习语境的最新方法，梳理了现有定义体系及定义语境时考虑的不同因素，并进一步探究了这些因素与学习理论中教学基础之间的关联。我们旨在从教学与技术双重视角出发，提供对情境化学习的全面理解。通过融合这两种视角，力求弥合这两个领域在学习推荐情境化方面的鸿沟。