Traditional goal setting simply assumes a binary outcome for goal evaluation. This binary judgment does not consider a user’s effort, which may demotivate the user. This work explores the possibility of mitigating this negative impact with a slight modification on the goal evaluation criterion, by introducing a ‘margin’ that is widely used for quality control in the manufacturing fields. A margin represents a range near the goal where the user’s outcome will be regarded as ‘good enough’ even if the user fails to reach it. We explore users’ perceptions and behaviors through a large-scale survey study and a small-scale field experiment using a coaching system to promote physical activity. Our results provide positive evidence on the margin, such as lowering the burden of goal achievement and increasing motivation to make attempts. We discuss practical design implications on margin-enabled goal setting and evaluation for behavioral change support systems.

When designing comfort and usability in products, designers need to evaluate aspects ranging from anthropometrics to use scenarios. Therefore, virtual and poseable mannequins are employed as a reference in early-stage tools and for evaluation in the later stages. However, tools to intuitively interact with virtual humans are lacking. In this paper, we introduce SmartManikin, a mannequin with agency that responds to high-level commands and to real-time design changes. We first captured human poses with respect to desk configurations, identified key features of the pose and trained regression functions to estimate the optimal features at a given desk setup. The SmartManikin’s pose is generated by the predicted features as well as by using forward and inverse kinematics. We present our design, implementation, and an evaluation with expert designers. The results revealed that SmartManikin enhances the design experience by providing feedback concerning comfort and health in real time.

Digital educational resources could enable the use of randomized experiments to answer pedagogical questions that instructors care about, taking academic research out of the laboratory and into the classroom. We take an instructor-centered approach to designing tools for experimentation that lower the barriers for instructors to conduct experiments. We explore this approach through DynamicProblem, a proof-of-concept system for experimentation on components of digital problems, which provides interfaces for authoring of experiments on explanations, hints, feedback messages, and learning tips. To rapidly turn data from experiments into practical improvements, the system uses an interpretable machine learning algorithm to analyze students’ ratings of which conditions are helpful, and present conditions to future students in proportion to the evidence they are higher rated. We evaluated the system by collaboratively deploying experiments in the courses of three mathematics instructors. They reported benefits in reflecting on their pedagogy, and having a new method for improving online problems for future students.