Experience Sampling Method is a modern approach of studying human behavior. This research methodology is used in psycho-metrics to ask participant to provide feedback regarding the state of their health, opinion, etc. The collected data is then probed, analyzed and used for creating related statistical predictions. Despite the remarkable data that is produced with ESM, receiving data from the participants is not a straightforward task. It could either be via physical presence of the participant in certain labs or through special hand-held devices that are sometimes difficult to carry, maintain and use. A more advanced version of ESM is Mobile Experience Sampling Method . As mobile devices have become the de facto standard in human communication as well as ubiquitous multi-purpose portable devices, taking advantage of their features, especially their location-awareness functionality can help improve the experience sampling method. Therefore the focus of this thesis is on improving the method byimplementing a versatile mobile experience sampling toolkit that is locationaware and easy to use.