Career choice is a life-changing decision for young adults. Online career guidance systems facilitate information access and provide recommendations for career paths. However, they bear the risk of imposed decisions, as they often neglect the non-cognitive processes of career decision-making. Mobile apps are promising for supporting career choices, since smartphones are a constant companion of adolescents. Following a user-centered design process, we developed “FindMyself”, a mobile app helping users discover their personal strengths and interests. FindMyself includes multiple sessions, prompting users with short challenges and reflection tasks building a personal profile. Our approach leaves time for self-reflection and discussion with peers. In a field study (N = 46), we found that FindMyself was perceived as easy, flexible, and fun to use, and was able to support self-reflection. For future iterations, some users requested specific career recommendations. We discuss these results and derive implications for further research on career decision support tools.

Entering text passwords on mobile devices is a significant challenge. Current systems either display passwords in plain text: making them visible to bystanders, or replace characters with asterisks shortly after they are typed: making editing them harder. This work presents a novel approach to mask text passwords by distorting them using graphical filters. Distorted passwords are difficult to observe by attackers because they cannot mentally reverse the distortions. Yet passwords remain readable by their owners because humans can recognize visually distorted versions of content they saw before. We present results of an online questionnaire and a user study where we compared Color-halftone, Crystallize, Blurring, and Mosaic filters to Plain text and Asterisks when 1) entering, 2) editing, and 3) shoulder surfing one-word passwords, random character passwords, and passphrases. Rigorous analysis shows that Color-halftone and Crystallize filters significantly improve editing speed, editing accuracy and observation resistance compared to current approaches.