Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Physical Conditions <s> Abstract Given the growing use of mobile devices, there is now increasing interest in the potential for supporting the mobile learner. However, there remains much research to be undertaken, to find effective ways of facilitating learning with mobile devices. This paper considers how to support the mobile language learner using a handheld computer. It introduces TenseITS, a language learning environment that adapts the interaction to the individual learner’s understanding, as represented in a learner model constructed during the interaction. It also adapts according to contextual features of the learner’s location that may affect their ability to study – the likelihood that they will be interrupted by others; their general ability to concentrate at that location; and the amount of time they have available for study. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Physical Conditions <s> A learning context describes the current situation of a learner related to a learning activity, and continually changes in a mobile learning environment. As a result, context-awareness in mobile learning has become an essential part when designing more adaptive mobile learning systems. In this paper, we introduce our novel Context- aware and adaptive learning schedule framework which makes use of a learning schedule to support the students' daily routines, adapts the activities to the student's learning styles and then selects the appropriate activity for the learner based on their current learning context. <s> BIB002
Physical context describes the environmental conditions where the system or user is situated, and commonly includes measures for heat, light, and sound  . Whereas physical context has been researched extensively in home automation research, its use by TEL applications is still limited. In learning scenarios, lighting and noise are sometimes considered as important criteria  . Physical conditions are captured explicitly by the user or implicitly from the environment. Current prototypes only implement noise level indicators. TenseITS BIB001 relies on manual input to capture such information. CALS BIB002 uses a microphone to automate such acquisition.