Multimedia learning is today a part of everyday life. Learning from digital sources on the internet is probably more common than printed material. The goal of this project is to determine if measuring user interaction in a interactive manual can be of use to evaluate the effectiveness of the manual. Since feedback of multimedia learning materials is costly to achieve in face-to-face interaction, automatic feedback data might be useful for evaluating and improving the quality of multimedia learning materials. In this project an interactive manual was developed for a real-world report generating application. The manual was then tested on 21 test users. Using the k-nearest neighbour machine learning algorithm the results shows that time taken on each step and the number of views on each step did not provide for good evaluation of the manual. Number of faults done by the user was good at predicting if the user would abort the manual and in combination with the number of acceptable interactions the usability data did provide for a better classification then ZeroR classification. The conclusions can be questioned by the small dataset used in this project.