In users. Moreover, due to the complexity and multi-scenario

In skill acquisition, learning by doing is the belief to show how users’ perform an action to achieve a specific goal when the Human-Computer Interaction is performed. how a beginner becomes the advance and how the structure of other domains is associated to different behavior within a System. How a user learns from environment while performing specific task within a system, the aim of this research is to identify user group based on skill acquisition level using datamining.

 

With the wide use of smartphones, and the continuous improvement of mobile Internet technologies, people get used to using mobile applications (APPs) but users’ usage habits are still unclear. To optimize user interface, support users with different goals and different levels of skills, and to provide better user experiences, it is useful to identify user group representations of the goals and behaviors of a hypothesized group of users. Moreover, due to the complexity and multi-scenario nature of users’ operation sequences, it is a challenging task to effectively and accurately measure the similarity of user’s operation sequences.

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In Human Computer Interaction (HCI) field, data mining techniques can be used by the researchers for the recognizing and predicting users’ behavior and skills acquisition level while completing a certain action within a system. A data mining framework is proposed to identify users’ skills acquisition level through hotel booking mobile application. For this purpose, we collected users’ activities through hotel booking application and asked them to fill the survey questionnaire to make comparison of results. K-means clustering technique is used to measure users’ activities and to create clusters with Advance, intermediate and beginner user groups.

 

Users’ dataset for this research work is acquired from Hochschule Heilbronn, Germany and different age of users were surveyed. Forty- One events have been collected and three events being selected to identify user groups based on users’ skills acquisition level. During this research, users’ internet speed, physical disability, his/her broken mobile screen and some other parameters are considered to carefully identify users’ skill acquisition level before validating results.

 

In the end results are validated by comparing users’ surveys and accuracy is measured using data mining k-means clusters. Results prove that proposed research framework is 100% accurate and can be used for future development.