Designing User-Centered Interactive Tools for Monitoring Software Learning Patterns

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Feature-rich software applications, such as statistical analysis tools, image and video editors, and 3D modelling tools can contain hundreds of commands and features embedded in elaborate menu and navigation structures. Software users exhibit different learning patterns when interacting with such applications. For example, some users try to follow specific instructions in software tutorials, videos, or discussion forums and switch back-and-forth between the application and the web several times. Others do not rely on external resources and prefer learning by trial and error to test the effect of different software actions. Unfortunately, many of these learning patterns can be time-consuming and ineffective, especially for novice users who are not able to easily assess whether what they are doing is right or wrong. In the proposed research, we will use principles from human-computer interaction (HCI) to invent novel tools and techniques that allow users to self-monitor, analyze, and reflect on their learning patterns when using a feature-rich application. The main tenet of self-monitoring is that if people can become more aware of what they are doing, they will be able to make positive changes in their behaviours. Our long-term objective is to design tools and algorithms that can automatically assess how well a user is learning a new feature-rich application, detect potential inefficiencies, and make targeted recommendations for improving their interaction. Beyond improving user interaction with feature-rich software, our overarching goal is to empower all users to be more productive, self-directed learners. We will lay the groundwork for achieving our long-term objectives by focusing on the following short-term objectives: (1) user-centered design and evaluation of interactive self-monitoring techniques that allow users to visualize and reflect on their learning as they use a new feature-rich application (2) design of collaborative privacy-sensitive tools for users to share their learning patterns with individual experts or the larger user community for feedback and further reflection (3) techniques for aggregating users' learning patterns to help identify application-specific learnability and usability issues at scale In pursuing this research, a key challenge for us will be accounting for individual differences in software use and learning and ensuring that our tools and techniques are not only fast and accurate, but also useful across different user populations. Furthermore, by tackling the challenge of aggregating users' learning patterns in a meaningful way to derive large-scale insights, our research will open up new avenues for data-driven software learnability research. The benefit of this research will be in ultimately enhancing the learning experience of millions of software users and serving as a foundation for building user-centered learning interventions across a wide range of domains.