SoftVideo: Improving the Learning Experience of Software Tutorial Videos with Collective Interaction Data

Abstract

Many people rely on tutorial videos when learning to perform tasks using complex software. Watching the video for instructions and applying them to target software requires frequent going back-and-forth between the two, which incurs cognitive overhead. Furthermore, users need to constantly compare the two to see if they are following correctly, as they are prone to missing out on subtle differences. We propose SoftVideo, a prototype system that helps users plan ahead before watching each step in tutorial videos and provides feedback and help to users on their progress. SoftVideo is powered by collective interaction data, as experiences of previous learners with the same goal can provide insights into how they learned from the tutorial. By identifying the difficulty and relatedness of each step from the interaction logs, SoftVideo provides information on each step such as its estimated difficulty, lets users know if they completed or missed a step, and suggests tips such as relevant steps when it detects users struggling. To enable such a data-driven system, we collected and analyzed video interaction logs and the associated Photoshop usage logs for two tutorial videos from 120 users. We then defined six metrics that portray the difficulty of each step, including the time taken to complete a step and the number of pauses in a step, which were also used to detect users' struggling moments by comparing their progress to the collected data. To investigate the feasibility and usefulness of SoftVideo, we ran a user study with 30 participants where they performed a Photoshop task by following along a tutorial video with SoftVideo. Results show that participants could proactively and effectively plan their pauses and playback speed, and adjust their concentration level. They were also able to identify and recover from errors with the help SoftVideo provides.