An Automated Approach to Assessing an Application Tutorial’s Difficulty
Online step-by-step text and video tutorials play an integral role in learning feature-rich software applications. However, when searching, users can find it difficult to assess whether a tutorial is designed for their level of software expertise. Novice users can struggle when a tutorial is out of their reach, whereas more advanced users can end up wasting time with overly simple, first-principles instruction. To assist users in selecting tutorials, we investigate the feasibility of using machine-learning techniques to automatically assess a tutorial’s difficulty. Using Photoshop as our primary testbed, we develop a set of distinguishable tutorial features, and use these features to train a classifier that can label a tutorial as either Beginner or Advanced with 85% accuracy. To illustrate a potential application, we developed a tutorial browsing interface called TutVis. Our initial user evaluation provides insight into TutVis’s ability to support users in a range of tutorial selection scenarios.
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S. A. Sabab, A. Khan, P. K. Chilana, J. McGrenere and A. Bunt, "An Automated Approach to Assessing an Application Tutorial’s Difficulty," 2020 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), Dunedin, New Zealand, 2020, pp. 1-10, doi: 10.1109/VL/HCC50065.2020.9127271.