RESEARCH

Teleportation - changing the point of view in 3D space by specifying a position - is one of the most common locomotion solutions in VR. However, it currently lacks a mechanism to adjust the height in 3D space, and it is difficult for users to predict the exact final view after the teleportation. Users are relocated to a place without knowing what the final view will look like. As a result, they often need to perform remedial interactions to achieve their ideal position, which can be time-consuming and effort-intensive. In this paper, we present Fly the Moon to Me (Locomoontion), a novel technique that enables users to bring their destination to themselves through object manipulation. Users first create a copy of the object they want to approach as a preview by selecting it, then bring it to an ideal position and direction using existing object manipulation techniques, and then snap the original object to the preview together with the rest of the world. A controlled experiment with 18 participants via a teleportation task reveals that Locomoontion is more effective than the traditional Point&Teleport technique with grabbing the world as a remedy to adjust the final positioning.

This paper adopted an iterative design process to gain insights into programmers’ strategies when using LLMs for programming. We proposed CoLadder, a novel system that supports programmers by facilitating hierarchical task decomposition, direct code segment manipulation, and result evaluation during prompt authoring. A user study with 12 experienced programmers showed that CoLadder is effective in helping programmers externalize their problem-solving intentions flexibly, improving their ability to evaluate and modify code across various abstraction levels, from their task’s goal to final code implementation.

Voice messages, by nature, prevent users from gauging the emotional tone without fully diving into the audio content. This hinders the shared emotional experience at the pre-retrieval stage. Research scarcely explored “Emotional Teasers”—pre-retrieval cues offering a glimpse into an awaiting message’s emotional tone without disclosing its content. We introduce EmoWear, a smartwatch voice messaging system enabling users to apply 30 animation teasers on message bubbles to reflect emotions. EmoWear eases senders’ choice by prioritizing emotions based on semantic and acoustic processing. EmoWear was evaluated in comparison with a mirroring system using color-coded message bubbles as emotional cues (N=24). Results showed EmoWear significantly enhanced emotional communication experience in both receiving and sending messages. The animated teasers were considered intuitive and valued for diverse expressions. Desirable interaction qualities and practical implications are distilled for future design. We thereby contribute both a novel system and empirical knowledge concerning emotional teasers for voice messaging.

Grandparent-grandchild bonds are crucial for both parties. Many immigrant families are geographically dispersed, and the grandparents and grandchildren need to rely on remote communication to maintain their relationships. In addition to geographical separation, grandparents and grandchildren in such families also face language and culture barriers during remote communication. The associated challenges and needs remain understudied as existing research primarily focuses on non-immigrant families or co-located immigrant families. To address this gap, we conducted interviews with six Chinese immigrant families in Canada. Our findings highlight unique challenges faced by immigrant families during remote communication, such as amplified language and cultural barriers due to geographic separation, and provide insights into how technology can better support remote communication. This work offers empirical knowledge about the communication needs of distributed immigrant families and provides directions for future research and design to support grandparent-grandchild remote communication in these families.

Data exploration is an important aspect of the workflow of mixed-methods researchers, who conduct both qualitative and quantitative analysis. However, there currently exists few tools that adequately support both types of analysis simultaneously, forcing researchers to context-switch between different tools and increasing their mental burden when integrating the results. To address this gap, we propose a unified environment that facilitates mixed-methods analysis in a computational notebook-based settings. We conduct a scenario study with three HCI mixed-methods researchers to gather feedback on our design concept and to understand our users’ needs and requirements.