Contextual and Sentimental Teachers’ Peer Recommendations

The current recommender systems’ approaches favor item’s recommendation over peer’s recommendation. Such virtual information systems do not allow face-to-face communication for collaborative knowledge exchange between peers. However, the on-going worldwide situation affects the sentimental state and especially for a teacher who struggle to adapt his course’s contents according to current variables. These challenges can be mitigated through personalized peer recommendations for teachers. A more-experienced teacher can provide the required support for a less-experienced one in the form of knowledge sharing. This paper proposes a matching approach to provide peer recommendations for teachers in consonance with their context and sentimental state. The peer recommendation aids the teacher to collaborate with other teachers in the form of experience sharing and knowledge sharing. Furthermore, the paper highlights the similarity measurement criteria for contextual and sentimental matching algorithm in addition to predefined rules. At the end, the paper discusses the effectiveness of the algorithm application with a real-life scenario.