Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Evaluation Challenges <s> This paper describes the context-aware mobile tourist application COMPASS that adapts its services to the user’s needs based on both the user’s interests and his current context. In order to provide context-aware recommendations, a recommender system has been integrated with a context-aware application platform. We describe how this integration has been accomplished and how users feel about such an adaptive tourist application. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Evaluation Challenges <s> The paper describes a model for recommendations in learning scenarios which has been designed from empirical findings following usability and accessibility criteria. This model supports course designers in describing recommendations and presents additional information to the user to explain why the recommendation has been provided. A prototype of a recommender system based on this model has been integrated in an open source standard-based learning management system. The main goal of the recommender is to improve the learning efficiency. Examples of recommendations defined with this model are provided. Moreover, a users' experience is reported. <s> BIB002
In order to prove the successful application of contextual recommender systems in TEL, the evaluation of this technology needs to be further strengthened. Particularly interesting would be having more comprehensive evaluation studies that assess the impact of individual context elements on the recommendation process. Such evaluation has for instance been explored by Santos and Boticario BIB002 . For example, some context dimensions may have a negative influence on the recommendation process as outlined in BIB001 . In order to facilitate evaluation, a more structured and coordinated approach to identify the effects of the context dimensions and their combinations would benefit from generalizing the data according to the framework presented in this paper. Following this approach, existing research efforts can be aligned to enable comparative evaluation studies. The approach would also enable to identify possible gaps and to address them respectively. In addition, we propose the creation of controlled experiments where the influence of different dimensions can be measured. These experiments could follow the same line as the TREC challenges organized in the Information Retrieval Community  and have already been conducted by the context-aware recommendation community. Examples include recommendation of suitable movies depending on time (e.g., the Christmas week and the week leading to the Oscars ceremony) and for a given mood  . For the creation of such experiments in the TEL field, data sets need to be collected from the use of TEL systems. Based on such data sets, correlations between different contextual variables may be discovered that could identify relevant contextual variables for TEL recommendation. Particularly relevant would be an exploratory investigation on the correlation between contextual variables and actions (i.e., ratings, downloads, reads, etc.) of users. Such an explorative analysis could identify potential differences in behavior based on contextual variables-such as day of the week, learning environment and available peers-and used to define meaningful recommendation challenges for the TEL field. Research opportunities to support this process are described in the next section.