Tootfinder

A case study examining graduate student sensemaking using the epistemic game framework for \v{Laplace}s equation in upper-level electrostatics
Jaya Shivangani Kashyap, Chandralekha Singh
https://arxiv.org/abs/2509.12487

A case study examining graduate student sensemaking using the epistemic game framework for \v{Laplace}s equation in upper-level electrostatics
This case study used individual interviews to investigate graduate student sense-making in upper-level electrostatics in the context of problems that can be efficiently solved for the electric potential using \v{Laplace}s equation. Although there are many technical mathematical issues involved in solving \v{Laplace}s equation, the focus of this research is not on those issues. Instead, the focus is on structural issues such as whether students recognize when solving \v{Laplace}s equation would …

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
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- HINTs: Sensemaking on large collections of documents with Hypergraph visualization and INTelligen...
Sam Yu-Te Lee, Kwan-Liu Ma

From Noise to Knowledge: Interactive Summaries for Developer Alerts
Burak Yeti\c{s}tiren, Hong Jin Kang, Miryung Kim
https://arxiv.org/abs/2508.07169 https://

From Noise to Knowledge: Interactive Summaries for Developer Alerts
Programmers using bug-finding tools often review their reported warnings one by one. Based on the insight that identifying recurring themes and relationships can enhance the cognitive process of sensemaking, we propose CLARITY, which supports interpreting tool-generated warnings through interactive inquiry. CLARITY derives summary rules for custom grouping of related warnings with active feedback. As users mark warnings as interesting or uninteresting, CLARITY's rule inference algorithm surface…

LLMs in the SOC: An Empirical Study of Human-AI Collaboration in Security Operations Centres
Ronal Singh, Shahroz Tariq, Fatemeh Jalalvand, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris, Martin Lochner
https://arxiv.org/abs/2508.18947

LLMs in the SOC: An Empirical Study of Human-AI Collaboration in Security Operations Centres
The integration of Large Language Models (LLMs) into Security Operations Centres (SOCs) presents a transformative, yet still evolving, opportunity to reduce analyst workload through human-AI collaboration. However, their real-world application in SOCs remains underexplored. To address this gap, we present a longitudinal study of 3,090 analyst queries from 45 SOC analysts over 10 months. Our analysis reveals that analysts use LLMs as on-demand aids for sensemaking and context-building, rather th…

Human-AI Schema Discovery and Application for Creative Problem Solving
Sitong Wang
https://arxiv.org/abs/2508.05045 https://arxiv.org/pdf/2508.05045…

Human-AI Schema Discovery and Application for Creative Problem Solving
Humans often rely on underlying structural patterns-schemas-to create, whether by writing stories, designing software, or composing music. Schemas help organize ideas and guide exploration, but they are often difficult to discover and apply, especially in complex or unfamiliar domains. My Ph.D. research develops a framework for human-AI schema discovery and application to support creative problem solving. I design systems that support users in sensemaking over examples to abstract schemas, and …

LitForager: Exploring Multimodal Literature Foraging Strategies in Immersive Sensemaking
Haoyang Yang, Elliott H. Faa, Weijian Liu, Shunan Guo, Duen Horng Chau, Yalong Yang
https://arxiv.org/abs/2508.15043

LitForager: Exploring Multimodal Literature Foraging Strategies in Immersive Sensemaking
Exploring and comprehending relevant academic literature is a vital yet challenging task for researchers, especially given the rapid expansion in research publications. This task fundamentally involves sensemaking - interpreting complex, scattered information sources to build understanding. While emerging immersive analytics tools have shown cognitive benefits like enhanced spatial memory and reduced mental load, they predominantly focus on information synthesis (e.g., organizing known document…

Facilitating Individuals' Sensemaking about Sedentary Behavior via Contextualized Data
Kefan Xu, Rosa I. Arriaga
https://arxiv.org/abs/2509.19420 https://

Facilitating Individuals' Sensemaking about Sedentary Behavior via Contextualized Data
The sedentary lifestyle increases individuals' risks of developing chronic diseases. To support individuals to be more physically active, we propose a mobile system, MotionShift, that presents users with step count data alongside contextual information (e.g., location, weather, calendar events, etc.) and self-reported records. By implementing and deploying this system, we aim to understand how contextual information impacts individuals' sense-making on sensor-captured data and how individuals l…

Agentic Reasoning and Refinement through Semantic Interaction
Xuxin Tang, Rehema Abulikemu, Eric Krokos, Kirsten Whitley, Xuan Wang, Chris North
https://arxiv.org/abs/2510.02157

Agentic Reasoning and Refinement through Semantic Interaction
Sensemaking report writing often requires multiple refinements in the iterative process. While Large Language Models (LLMs) have shown promise in generating initial reports based on human visual workspace representations, they struggle to precisely incorporate sequential semantic interactions during the refinement process. We introduce VIS-ReAct, a framework that reasons about newly-added semantic interactions in visual workspaces to steer the LLM for report refinement. VIS-ReAct is a two-age…

Evaluation of a Provenance Management Tool for Immersive Virtual Fieldwork
Armin Bernstetter, Tom Kwasnitschka, Isabella Peters
https://arxiv.org/abs/2507.18622 https://

Evaluation of a Provenance Management Tool for Immersive Virtual Fieldwork
Ensuring reproducibility of research is an integral part of good scientific practice. One way to support this is through provenance: information about research workflows from data gathering to researchers' sensemaking processes leading to published results. This is highly important in disciplines such as geosciences, where researchers use software for interactive and immersive visualizations of geospatial data, doing virtual measurements in simulated fieldwork on 3D models. We evaluated a prove…