Visual blends is a design technique that combines elements from multiple images into harmonious compositions and has been increasingly explored as a means to support early-stage ideation in engineering design. However, existing blending workflows rely heavily on manual image selection and composition, making the process difficult, time-consuming, and skill-intensive for designers. In this work, we present a proof-of-concept brain-guided visual blends technique that integrates an EEG-to-image model to simplify the image acquisition process and a local image editing model to enable automated and controllable image composition. Our EEG-to-image model employs a two-stage training strategy, combining pretraining on large-scale unlabelled EEG data with fine-tuning in an EEG-conditioned diffusion model, achieving state-of-the-art performance in reconstructing visual stimuli. To support visual blending tasks, we incorporate a local editing model (Paint-by-Example) that generates coherent blends using user-provided masks, reference images, and backgrounds. A user study with 15 participants demonstrated that the model effectively supported the creation of visual blends that aligned with users' design vision, even without artistic skills. The results suggest that brain-guided blending can serve as a early-stage ideation interface in engineering design, helping designers iterate on mental concepts before formal modelling and evaluation.

Data Humanism has gained prominence in personal visualization and Personal Informatics, advocating for a subjective and slow approach to engage with personal data. Collaborative sensemaking has great potential for aiding the understanding of personal data, yet little is known about addressing requirements of structure and coordination when integrating Data Humanism into collaborative visualization. In this paper, we propose design principles for creating both subjective and effective collaborative visualizations, while coordinating the slow sensemaking process and promoting data awareness and communication. We operationalize these principles into a personal visualization toolkit, which we evaluate with an observational study involving 16 university students (8 pairs) analyzing each other's screen-time data. Our findings reveal that implementing the proposed design principles: (1) facilitated data comparison from shared subjective perspectives, (2) helped coordinate sensemaking while allowing time for understanding personal data, and (3) helped the contextualization of data patterns, in turn aiding self-reflection.

Motivation: In today’s increasingly digital world, making sense of personal data has become a valuable approach for individuals to understand their behavior and reflect on lived experiences. Across fields such as personal informatics, data visualization, and design, supporting reflection through sensemaking of personal data is increasingly recognized as a critical for promoting behavior change and cultivating deeper self-understanding. Within the literature on collaborative sensemaking, pair collaboration has emerged as a particularly promising strategy for supporting reflective practices. It offers unique advantages— such as the integration of diverse perspectives, focused dyadic comparison, and rich interaction dynamics—that are especially effective for surfacing tacit knowledge and generating deeper insights. Despite this promise, most existing research in personal informatics and visualization has focused on individual engagement with personal data, with limited exploration of collaborative or group-level interactions. In particular, the distinctive characteristics of pair collaboration remain underexplored in the context of personal data. This reveals a significant gap in understanding how to effectively support pair sensemaking of personal data in order to foster reflection.
Objective: To address this gap, this dissertation introduces and develops the pair sensemaking of personal data approach—a novel method for fostering reflection through pair engagement with personal data. This approach emphasizes the integration of both one’s own and others’ data, dyadic comparison within subjective data representations, and reciprocal, reflective dialogues between pairs to co-construct self-knowledge and deepen personal reflection. To realize this objective, the thesis adopts a progressive, mixed-methods research strategy, unfolding across three empirical studies. Each study incrementally explores and refines how pair sensemaking of personal data can be effectively designed to enhance reflective engagement and foster meaningful insights into lived experiences.

This paper explores pair collaboration as a novel approach for making sense of personal data. Pair collaboration - characterized by dyadic comparison and structured roles for questioning and reasoning - has proven effective for co-constructing knowledge. However, current collaborative visualization tools primarily focus on group comparisons, overlooking the challenges of accommodating pair collaboration in the context of personal data. To address this gap, we propose a set of design rationales supporting subjective data analysis through dyadic comparison and mixed-focus collaboration styles for co-constructing personal narratives. We operationalize these principles in a tangible visualization toolkit, PAIRcolator. Our user study demonstrates that pairwise collaboration facilitated by the toolkit: 1) reveals detailed data insights that are effective for recalling personal experiences, and 2) fosters a structured, reciprocal sensemaking process for interpreting and reconstructing personal experiences beyond data insights. Our results shed light on the design rationales for, and the processes of pair sensemaking of personal data, and their effects to foster deep levels of reflection.

The design of collaborative personal informatics (PI) has shifted its focus from using one’s own data to integrating others’ data to enhance self-understanding. In this trend, understanding the effectiveness of the two data sources in facilitating personal insights becomes essential, as a comprehensive understanding of self-understanding requires insights from both individual and interpersonal perspectives. While recent studies have suggested the potential role of others’ data as a reflective medium to generate personal insights, little is understood about its distinctive effectiveness in personal insights generated compared to one’s own data. To address this gap, we conducted a crowdsourced study involving two participant groups (N1=N2=60) in a data-informed reflection task: Data Providers (DP) reflecting on their own data; Non-Data Providers (NDP) reflecting on the data provided by DP. Analyzing the textual responses, we assess the reflection levels, self-disclosure levels, and characteristics of personal insights. Our findings uncover that others’ data possess a comparable effectiveness in facilitating reflection and self-disclosure of personal thoughts and feelings. Others’ data displays a strength in supporting value judgments, while one’s own data excels in enhancing behavioral awareness. This research sheds light on the design of collaborative PI, offering insights into how to leverage the benefits while mitigating the disadvantages of both data sources to enhance the self-understanding.