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Towards a Model for Implicit Evaluation in the Wild on a Large Scale

Evaluation is an essential activity in HCI. Once a product is released, it may be of interest to continue its evaluation in an implicit and in the wild fashion on a large scale. In addition to data logs which can offer a view of the usage of the system, attitudinal data (e.g. satisfaction, emotional reaction) also contribute to understand the user's complete interaction. Compiling these data is a challenge when performing it implicitly and in the wild. This work proposes an approach to evaluate interactive systems with a large number of users (in the large), in real conditions (in the wild) and implicitly - without the users being aware that they are participating in the evaluation of the system.