Our research in this paper lies at the intersection of Generative AI (GenAI) and search-as-learning (SAL). GenAI technologies (e.g., ChatGPT) have revolutionized how people search for and interact with information. However, we do not yet fully understand how people use GenAI systems to learn about complex topics. SAL research has studied how different tools can support learning with traditional document retrieval systems. Our research closely relates to SAL work that has investigated the effects of goal-setting on learning during search. We explore the influence of goal-setting on learning during information-seeking sessions with a GenAI system. We report on a between-subjects crowdsourced study (N = 120) in which participants were asked to learn about a complex topic using a GenAI system. The study had four conditions that varied along two factors (a 2 × 2 design). The first factor involved displaying related web results in addition to the GenAI output. The second factor involved giving participants access to the Subgoal Manager (SM), a tool designed to help people develop subgoals and take notes. We investigated the effects of both factors on: (RQ1) perceptions; (RQ2) behaviors; (RQ3) learning and retention; (RQ4) the types of requests issued to the system; and (RQ5) participants’ motivations for engaging (or not engaging) with the related web results. Results found that participants with access to the SM had higher post-task learning outcomes, did less copy/pasting into their notes, perceived the task as more difficult, and requested more examples and support for differentiating concepts from the GenAI system.

We present the SRL Perceptions Questionnaire (SPQ), developed to measure perceptions of self-regulated learning (SRL) after information seeking and learning sessions. In a crowd-sourced study (N = 127), participants completed the SPQ after searching to learn about a complex topic. The SPQ asked participants to report their perceptions of particular SRL constructs (e.g., planning, monitoring, strategy use, adapting). A principal component analysis supported a five-factor structure with high reliability (α > =.87). Perceived SRL did not correlate with normalized learning gains, yet pre-task and post-task perceptions showed correlations with several SPQ dimensions. We offer both the SPQ as an instrument for measuring SRL (processes critical to supporting human learning) after information seeking and insights into how perceptions of SRL constructs align with objective learning outcomes, pre-task perceptions, and post-task perceptions while learning during search.

This paper presents a user study (N=22) where participants used an interface combining Web Search and a Generative AI-Chat feature to solve health-related information tasks.We study how people behaved with the interface, why they behaved in certain ways, and what the outcomes of these behaviours were.A think-aloud protocol captured their thought processes during searches.Our findings suggest that GenAI is neither a search panacea nor a major regression compared to standard Web Search interfaces.Qualitative and quantitative analyses identified 78 tactics across five categories and provided insight into how and why different interface features were used.We find evidence that pre-task confidence and trust both influenced which interface feature was used.In both systems, but particularly when using the chat feature, trust was often misplaced in favour of ease-of-use and seemingly perfect answers, leading to increased confidence post-search despite having incorrect results.We discuss what our findings mean in the context of our defined research questions and outline several open questions for future research.

Diversity has been an important concept in both interactive information retrieval (IIR) and recommendation systems (RS), especially in the context of entertainment activities such as music, video, or games.However, diversity itself is a highly abstract concept, and providing diversity to users brings the risk of unsatisfactory results.In this short paper, we investigate diversity in the context of music recommendation.We conducted an online survey with 149 participants to investigate how they perceive music diversity.Our results revealed their explicit ideas about different aspects of diversity, as well as their underlying perceptions shaping their diversity preferences.We propose viewing music diversity using an "above and below-surface" structure, and our findings can help design other entertainment systems such as video or games.

Generative AI (GenAI) technologies such as ChatGPT are changing the ways people interact with information.To illustrate, popular search engines (e.g., Google) have started integrating responses from GenAI tools with the traditional search results.In this paper, we explore the integration of GenAI technology with traditional search in the context of a learning-oriented task.We report on a between-subjects study (𝑁 = 40) in which participants completed a complex, learning-oriented search task.Participants were assigned to one of two conditions.In the SearchOnly condition, participants used a traditional web search system to gather information.In the Search+Chat condition, participants used an experimental system that combined a traditional web search component and an interactive GenAI-based chat component (Chat AI).The study investigated seven research questions.RQ1-RQ3 focused on differences between groups: (RQ1) post-task perceptions, (RQ2) search behaviors, and (RQ3) learning outcomes.To measure learning, participants completed a multiple-choice test before the search task, immediately after, and one week later (to measure retention).RQ4-RQ7 delved deeper into participants' behaviors and experiences in the Search+Chat condition: (RQ4) motivations for (and gains from) engaging with the Chat AI; (RQ5) the phases during which participants engaged with the Chat AI; (RQ6) the types of queries issued to each component; and (RQ7) perceptions about the information returned by each component.

Extensive research exists on user interactions with search engine result pages (SERPs) in desktop and mobile environments. However, relatively little work has focused on understanding how virtual reality (VR) users interact with SERPs in 3D immersive virtual environments (IVEs). Unlike 2D displays with established paradigms (e.g., ranked lists), 3D IVEs lack standardized methods for presenting search results. This work explores how different information arrangements in 3D virtual space impact users' search behaviors and preferences.

An oft-repeated ideal of personal information management (PIM) is to have “the right information, at the right time, in the right place…” for the current need. But the technologies and innovations that bring us ever closer to this ideal carry costs as well as benefits. In this ninth in a series of PIM workshops, we give closer, critical consideration to the “right time, right place” ideal of PIM. Can we manage the potential downsides involved in achieving this ideal, while preserving its obvious benefits? Or should we revise our ideal of PIM?

The aim of this study is to explore connections among the reasons that lead users to stop a search session, the types of information they found during the session, and the cognitive activities involved in interacting with such information – all in the context of complex tasks involving multiple search sessions. Prior research has primarily examined search-stopping behavior in single search sessions, focusing on instances when users identify needed information or are satisfied with their findings. However, recent insights from the Search as Learning (SAL) community highlight the relationships between user search behavior, the nature of the information they encountered, and various cognitive activities during the information-searching process. We conducted a diary study with 25 participants engaged in real-life tasks that spanned multiple search sessions over time. Our analysis found six predominant reasons users elected to stop a search session during the cross-session search process. We also examined the types of information that participants found and the cognitive activities that they engaged in during search sessions. We found statistically significant associations between the information types found and the session stopping reasons. We did not find significant associations between the cognitive activities and stopping reasons, but did observe that almost half the sessions reached the evaluate level of cognitive activity. Our findings provide insights about search behaviors and help inform the design of tools to support users working on multi-session search tasks.

People often search for information to acquire procedural knowledge–“how to” knowledge about step-by-step procedures, methods, algorithms, techniques, heuristics, and skills. A procedural search task might involve implementing a solution to a problem, evaluating different approaches to a problem, and brainstorming on the types of problems that can be solved with a specific resource. We report on a study ( N =36) that aimed to better understand how people search for procedural knowledge. Much research has investigated how search task characteristics impact people’s perceptions and behaviors. Along these lines, we manipulated procedural search tasks along two orthogonal dimensions: product and goal. The product dimension relates to the main outcome of the task and the goal dimension relates to task’s success criteria. We manipulated tasks across three product categories and two goal categories. The study investigated four research questions. First, we examined the effects of the product and goal on participants’ (RQ1) pre-task perceptions, (RQ2) post-task perceptions, and (RQ3) search behaviors. Second, regardless of the task product and goal, by analyzing participants’ think-aloud comments and screen activities we closely examined how people search for procedural knowledge. Specifically, we report on (RQ4) important relevance criteria, types of information sought, and challenges.

People often search online for procedural (i.e., “how-to”) knowledge. A procedural search task might involve a do-it-yourself project, cooking a dish, fixing a problem, or learning a new skill. Prior research has studied procedural search tasks from different perspectives: estimating the frequency of procedural searches online, understanding how people acquire procedural knowledge in specific contexts, and developing tools to support procedural search. Less research has aimed at deeply understanding procedural search tasks “in the wild”. To bridge this gap, we conducted a survey (N = 128) on Amazon Mechanical Turk. Participants were asked to recall a recent procedural task for which they searched online. Participants were asked open-ended questions about the task itself and their unique situation (e.g., constraints and needs). Additionally, participants provided webpages they found useful in their searches and described the characteristics of the page that made it useful. Finally, they provided useful pieces of information from each selected page and explained what they gained from the information. Using an inductive coding approach, we analyzed participants’ responses to gain insights about: (1) procedural task characteristics, (2) goals, (3) constraints, (4) contextual factors, (5) relevance criteria, and (6) gains obtained from useful information. Based on our results, we discuss important implications for future research and system design.

User interactions with search engine result pages (SERPs) are well researched in desktop and mobile computing environments. However, relatively little work has focused on how users interact with SERPs presented in 3D immersive virtual environments (IVEs) using virtual reality head-mounted displays (VR HMDs). While 2D displays have well-understood methods to present search results (e.g., ranked lists of 10 blue links), 3D IVEs do not yet have established paradigms for presenting search results.

Music listening is heavily influenced by contexts, and contextual factors can shape users’ interaction with music information retrieval (MIR) systems. To better design context-sensitive user experiences in MIR systems, in this paper, we present a review of prior studies on how contexts are associated with user behavior in MIR systems. Contextual factors considered include interaction design, age, personality, time of day, activity, motivation, nationality, etc. Based on the review, we introduce a framework to consider these contextual factors in a consistent and organized way. The framework is adapted from Ingwersen and Järvelin’s 2006 nested contexts framework, and has four layers: 1) MIR/system contexts that focus on MIR systems themselves, including both hardware and software; 2) situational contexts that describe varied and transient daily situations where users interact with MIR systems; 3) personal contexts that focus on the more stable personal characteristics; and 4) social and cultural contexts that describe the characteristics of users’ environments. We also present an example to illustrate how to systematically analyze user contexts by using the framework. Finally, we discuss several areas for possible future studies.

Abstract Exploratory searches involve significant cognitively demanding aiming at learning and investigation. However, users gain little support from search engines for their cognitive and metacognitive activities (e.g., discovery, synthesis, planning, transformation, monitoring, and reflection) during exploratory searches. To better support the exploratory search process, we designed a new search assistance tool called OrgBox. OrgBox allows users to drag‐and‐drop information they find during searches into “boxes” and “items” that can be created, labeled, and rearranged on a canvas. We conducted a controlled, within‐subjects user study with 24 participants to evaluate the OrgBox versus a baseline tool called the OrgDoc that supported rich‐text features. Our findings show that participants perceived the OrgBox tool to provide more support for grouping and reorganizing information, tracking thought processes, planning and monitoring search and task processes, and gaining a visual overview of the collected information. The usability test reveals users' preferences for simplicity, familiarity, and flexibility of the design of OrgBox, along with technical problems such as delay of response and restrictions of use. Our results have implications for the design of search‐assisting systems that encourage cognitive and metacognitive activities during exploratory search processes.

While current systems are effective in helping searchers resolve simple information needs (e.g., fact-finding), they provide less support for searchers working on complex information-seeking tasks. Complex search tasks involve a wide range of (meta)cognitive activities, including goal-setting, organizing information, drawing inferences, monitoring progress, and revising mental models and search strategies. We report on a lab study ( N = 32) that investigated the influences of a knowledge representation tool called the OrgBox, developed to support searchers with complex tasks. The OrgBox tool was integrated into a custom-built search system and allowed study participants to drag-and-drop textual passages into the tool, organize passages into logical groupings called “boxes”, and make notes on passages and boxes. The OrgBox was compared to a baseline tool (called the Bookmark) that allowed participants to save textual passages, but not organize them nor make notes. Knowledge representation tools such as the OrgBox may provide special benefits for users with different cognitive profiles. In this article, we explore two cognitive abilities: (1) working memory (WM) capacity and (2) switching (SW) ability. Participants in the study were asked to gather information on a complex subject and produce an outline for a hypothetical research article. We investigate the influences of the tool (OrgBox vs. Bookmark) and the participant’s working memory capacity and switching ability on three types of outcomes: (RQ1) search behaviors, (RQ2) post-task perceptions, and (RQ3) the quality of outlines produces by participants.

Prior studies have explored the information-seeking practices of specific professional communities, including lawyers, physicians, engineers, recruiters, and government workers. In this research, we investigate the information-seeking practices of intelligence analysts (IAs) employed by a U.S. government agency. Specifically, we focus on the needs, practices, and challenges related to IAs searching for procedural knowledge using an internal system called the Tradecraft Hub (TC Hub). The TC Hub is a searchable repository of procedural knowledge documents written by agency employees. Procedural knowledge (as opposed to factual and conceptual knowledge) includes knowledge about step-by-step procedures, techniques, methods, tools, technologies, and skills, and is inherently task-oriented. We report on a survey study involving 22 IAs who routinely use the TC Hub. Our survey was designed to address four research questions. In RQ1, we investigate the types of work-related objectives that motivate IAs to search the TC Hub. In RQ2, we investigate the types of information IAs seek when they search the TC Hub. In RQ3, we investigate important relevance criteria used by IAs when judging the usefulness of information. Finally, in RQ4, we investigate the challenges faced by IAs when searching the TC Hub. Based on our findings, we discuss implications for improving and extending searchable knowledge base systems such as the TC Hub that exist in many organizations.

Users often search for information online across multiple sessions for a specific task. Sometimes cross-session searches may be unanticipated, however, in many cases users know beforehand that their search will spread across multiple sessions. Little is known about why people expect to search across multiple sessions. Based on a qualitative analysis of the interview data collected from a diary study, we identified two types of reasons that motivate people to conduct cross-session searches: (1) task-oriented motivations, (2) cognition-oriented motivations. We also identified a group of reasons that cause challenges for cross-session search. Our findings have implications for the future design of search systems to support cross-session search from different aspects.

As an important skill in many domains, creativity is understood to be a process that includes stages like finding goals, looking up information, exploring, creating ideas, combining ideas, selecting ideas, and executing plans [50]. However, little prior information retrieval research has considered how people use search tools and information resources to support their creative stages.

Modern search systems are largely designed and optimized for simple navigational or fact-finding tasks, with little support for complex tasks involving comprehension and learning. In response, the search-as-learning research community has undertaken a wide range of research questions focused on understanding how various types of learning outcomes are affected by searcher characteristics, the search task, and the search system. Typically, these views embed learning within a search system. In this paper we take a different view, embedding search within a framework for an end-to-end learning system designed to support learning in a formal educational context. Our central goal is to motivate research questions aligned to advance progress on techniques for active support of comprehension and formal learning. Thus we intentionally set aside goals for informal and surface learning. We argue that to be effective, such a search-centric learning system must model four key components: individual students (searcher factors), the educational domain (topic factors), academic assignments (task factors), and progress toward learning goals (the objective function of the end-to-end system). In modeling these components, our hypothetical system makes inferences about students’ learning histories, knowledge states, comprehension, and the utilities of different types of information resources. We present examples of possible techniques and data sources for each model. We also introduce the novel concept of leveraging school assignments as rich task context. Our intention is not to propose a functional system, but to frame search-as-learning in the context of comprehension and to inspire research questions arising from an end-to-end view of this important research domain.

Current search systems are effective in helping users complete simple search tasks (e.g., fact-finding). However, they provide less support for users completing complex search tasks. Complex search tasks involve a diverse set of cognitive and metacognitive activities, such as goal-setting, organizing information, drawing inferences, monitoring progress, and updating mental models. We report on a lab study (N=32) that investigated the uses and influences of a novel knowledge representation tool called the "OrgBox'', developed to support searchers with complex tasks. The OrgBox was integrated into a custom-built search system and allowed participants to save information by drag-and-dropping textual passages into the tool, organize passages into "boxes'', and make notes on passages and boxes. The OrgBox tool was compared to a baseline tool (the "Bookmark'') that allowed participants to save passages, but not organize them nor make notes. We investigate four research questions. In RQ1, we investigate the effects of the knowledge representation tool on participants' post-task perceptions. In RQ2-RQ4, we investigate: (RQ2) how participants used different features of each tool; (RQ3) the perceived benefits and challenges of each tool; and (RQ4) the influences of each tool on the approaches taken by participants to complete the task. To address RQ2-RQ4, we conducted a qualitative analysis of participants' responses during an exit interview. We discuss implications from our results for designing tools to support users with complex search tasks.

Prior work in IR has found that searchers under time constraints may adapt their search processes and perceive their task or their performance differently. In many of these prior studies, the task descriptions implicitly or explicitly conveyed an expectation of the amount of information needed to satisfy the task requirements in terms of number of pages (e.g., find N webpages on topic X) or the time to spend on the task (e.g., search until time is up) rather than allowing the participant to determine how much information was needed. In this lab-based study, we investigated the effects of time constraints on information search and decision-making. Participants completed a series of decision-making tasks with half of the participants receiving a 5-minute time constraint and half given no time guidance. They were asked to make good, specific recommendations for a friend, and they had considerable latitude in deciding how much information they needed. Results showed that participants in the time constraint condition made their decisions faster but there were few significant differences in measures of search behaviors between the time constraint conditions (RQ1). Qualitative analysis indicated that participants adapted their decision task by varying their recommendations in their specificity, justification strength, and contents in both time conditions (RQ2). Finally, we found evidence that the impact of the time constraint on time- and task-related perceptions was moderated by the extent to which participants adapted their decision task (RQ3).

Previous studies found that cross-session search tasks are often complex due to the uncertainty of task features, users' perception of the tasks, and the challenges (e.g., time limitation, interruptions) that users encounter during the process. Using Anderson & Krathwohl's taxonomy of learning, we analyzed the complexity of real-life cross-session search tasks reported by 110 Amazon Mechanical Turk workers. We found that cross-session search tasks often require different types of information (i.e., factual and conceptual information, procedure information, opinions, and information for helping metacognition) and are frequently associated with higher-level cognitive activities, including: applying analyzing, evaluating, and creating. Our findings add knowledge about cross-session search, and have insights for future research related to system design and studying cross-session search.

Current search systems provide effective support to users engaged in fact-finding and look-up oriented tasks. However, they provide relatively little support for users engaged in exploratory search tasks that involve cognitive and metacognitive activities such as learning, synthesis, planning, and reflection. We conducted a within-subject user study (N=24) that investigated the effects of a novel knowledge organization tool called the OrgBox, designed to assist users with organizing and synthesizing information, and metacognitive activities. The OrgBox included features to allow users to drag-drop information they found through search into "boxes" that could be created, labelled, and re-arranged. Study participants completed two exploratory search tasks, one with the OrgBox, and one with the OrgDoc, a baseline tool that included features of a rich-text editor (e.g., formatting, bullets) for taking notes.

We describe a search-assistance tool called the OrgBox, created to support users' organizational, cognitive, and metacognitive activities while performing exploratory search tasks. The OrgBox tool allows users to create and label "boxes" to organize information found during a search. The OrgBox was integrated with a custom built search system that allowed users to save, organize, and synthesize information using drag-and-drop actions. The OrgBox tool also encourages users to engage in metacognitive activities during their search process (e.g., planning their next steps, monitoring their progress, evaluating the information found so far). In this paper, we describe the features and implementation of the OrgBox tool. We also summarize result of two user studies conducted using the OrgBox that show its cognitive and metacognitive benefits to users.

An important question in interactive information retrieval (IIR) is: How can we support searchers with specific types of search tasks? We describe an auxiliary support tool referred to as the "Matrix''. The Matrix tool was designed to support searchers with comparative search tasks, which require comparing items along different dimensions. The Matrix was designed as a grid of rows and columns representing the items and dimensions related to a comparative task. The Matrix was integrated with a custom-built search interface, which allowed users to search for information and drag-and-drop relevant passages directly into cells in the Matrix. We investigate the following general question: Given a partially completed Matrix, can a system automatically populate empty cells in the Matrix with relevant passages? To this end, we conducted two crowdsourced studies in which participants were assigned comparative tasks and asked to use our system (integrated search interface + Matrix) to populate every cell in the Matrix. After gathering this data, we evaluated machine-learned models for ranking passages in response to an empty Matrix cell and partially completed Matrix. We address two research questions: (RQ1) What are useful types of features for this predictive task? and (RQ2) How does performance vary based on the level of Matrix completion? We view our research as a step towards designing support tools that: (1) help users organize information while searching and (2) can autocomplete search tasks by exploiting the task structure and a searcher's partial solution.

We report on a survey about people's cross-session search activities in their everyday work and life. We recruited a broad range of participants (N=110) using the Amazon Mechanical Turk service. The survey asked people to describe a recent task in which they searched across multiple sessions, to recall the reasons they started and stopped the most recent search session for the task, and to explain methods they used for reacquainting themselves with the task at the beginning of the most recent session. Across a broad range of tasks reported by our participants, our results show that a majority of the cross-session work tasks involved high levels of cognitive complexity, consultation with additional human information sources (in addition to search engines), and often involved multiple devices (e.g., computer and smartphone). Our analysis of reasons why people stopped and restarted search sessions extends and validates the reasons outlined in Lin and Belkin's model of Multiple Information Seeking Episodes and previous results from MacKay and Watters' study of multi-session search. We also identified methods that searchers use to re-acquaint themselves when restarting multi-session searches, and reasons why they may choose not to use any re-acquainting method. Our results update prior work and provide insights about how search systems can better support cross-session work tasks.

A recent study found that search engines play an important role in supporting creative tasks that people encounter in everyday life. However, little research has been conducted to understand how people use search engines and other information resources to support creative endeavors in their day-to-day work and life. To gain a deeper understanding, we conducted a diary study by asking eleven participants to record diary entries about their own design-related creative projects over a two-week period. In this paper, we report the findings of our qualitative analysis of participants' diary entries and post-diary in-depth interviews. Specifically, we identified six types of information that were essential to design-related creative projects: procedural information, domain information, finished examples, tips/opinions/recommendations, information about specific topics, and inspiring/motivating information. For each type of information, we discuss participants' intents to use the information in their projects. We also describe challenges reported by our participants when they used search engines to address their information needs. Finally, we discuss implications of our findings for future research and the future design of search systems.

In this paper, we present results from an exploratory study to investigate users' behaviors and preferences for three different styles of search results presentation in a virtual reality (VR) head-mounted display (HMD). Prior work in 2D displays has suggested possible benefits of presenting information in ways that exploit users' spatial cognition abilities. We designed a VR system that displays search results in three different spatial arrangements: a list of 8 results, a 4x5 grid, and a 2x10 arc. These spatial display conditions were designed to differ in terms of the number of results displayed per page (8 vs 20) and the amount of head movement required to scan the results (list < grid < arc). Thirty-six participants completed 6 search trials in each display condition (18 total). For each trial, the participant was presented with a display of search results and asked to find a given target result or to indicate that the target was not present. We collected data about users' behaviors with and perceptions about the three display conditions using interaction data, questionnaires, and interviews. We explore the effects of display condition and target presence on behavioral measures (e.g., completion time, head movement, paging events, accuracy) and on users' perceptions (e.g., workload, ease of use, comfort, confidence, difficulty, and lostness). Our results suggest that there was no difference in accuracy among the display conditions, but that users completed tasks more quickly using the arc. However, users also expressed lower preferences for the arc, instead preferring the list and grid displays. Our findings extend prior research on visual search into to the area of 3-dimensional result displays for interactive information retrieval in VR HMD environments.

Abstract It is now common for individuals to have multiple computing devices, such as laptops, smart phones, and tablets. This multidevice environment increases the popularity of cross‐device search activities. Cross‐device search can be seen as a special case of cross‐session search. Previous studies regarded re‐finding behaviors in cross‐session search as task resumption. Based on this, this article proposes considering 2 phases of cross‐device search: task preparation and task resumption and to explore their features by modeling. A within‐subject user experiment was designed to collect data. Four groups of features were captured from specific behaviors of querying, clicking, gazing, and cognition. This article tested 3 machine‐learning methods and found that the C5.0 decision tree performed best. Five features were included in the task preparation behavior model, and 3 in the task resumption behavior model. The difference and relationship between task preparation and task resumption were investigated by comparing their behavioral features. It is concluded that information need remains blurred in task preparation and becomes clear in task resumption. The changing states of information need suggest an exploratory process in cross‐device search. We also identify some implications for search engine designers.

In interactive information retrieval, an important research question is: How do task characteristics influence users’ needs and behaviors? We report on a laboratory study ( N =32) that investigated the effects of task complexity on the types of information used by participants while searching. Participants completed tasks of four complexity levels and had access to four different types of information provided through a search-assistance tool referred to as the InfoBoxes (IB). The IB tool presented the following types of task-related information ( info-types ) on different tabs: (1) facts, (2) concepts, (3) opinions, and (4) insights. Facts (and opinions) were defined as objective (and subjective) statements relevant to the task. Concepts were defined as important ideas, principles, or entities related to the task. Insights were defined as tips or advice about the task. The study investigated six research questions that considered the effects of task complexity on: (RQ1) participants’ pre-/post-task perceptions about useful info-types; (RQ2) use of different info-types during the task; (RQ3) motivations for engaging with the IB; (RQ4) gains from using it; (RQ5) the search stage participants were in while engaging with the IB; and (RQ6) motivations for sometimes avoiding the IB. Our results suggest that task complexity influenced all six types of outcomes. We discuss implications of our results for designing search assistance tools and systems that favor certain types of content based on task characteristics.

Popular messaging platforms such as Slack have given rise to thousands of applications (or bots) that users can engage with individually or as a group. In this paper, we study the use of searchbots (i.e., bots that perform specific types of searches) during collaborative information-seeking tasks mediated through Slack. We report on a user study in which 27 pairs of participants were exposed to three searchbot conditions (a within-subjects design). In the first condition, participants completed the task by searching independently and coordinating through Slack (no searchbot). In the second condition, participants could only search inside of Slack using the searchbot. In the third condition, participants could both search inside of Slack using the searchbot and outside of Slack using their own independent search interfaces. We investigate four research questions focusing on the influence of the searchbot condition on outcomes associated with: (RQ1) participants' levels of workload, (RQ2) collaborative awareness, (RQ3) experiences interacting with the searchbot, and (RQ4) search behaviors. Our results suggest opportunities and challenges in designing searchbots to support collaborative search. On one hand, access to the searchbot resulted in more collaborative awareness, ease of coordination, and fewer duplicated searches. On the other hand, forcing participants to share the querying environment resulted in fewer overall queries, fewer query refinements by individuals, and greater levels of effort. We discuss the implications of our findings for designing effective searchbots to support collaborative search.

We report on a study that evaluated the effects of working memory and task complexity on participants' perceptions, behaviors, and outcomes. Twenty-four participants performed two search tasks of varying complexity and completed a psychometric test to measure working memory ability. Our results found several important trends. First, task complexity had an effect on participants' perceptions about temporal demand and satisfaction with the time spent on the task. Second, participants with higher working memory exerted more search effort (e.g., issued more queries). Third, participants with higher working memory had better outcomes, particularly during more complex tasks. Finally, while participants with lower working memory exerted less effort (engaged in satisficing behaviors) and had weaker outcomes, working memory did not affect participants' post-task perceptions about workload and satisfaction. We discuss implications of our results for developing search tools to support users with varying levels of working memory.

Creativity is an essential part of people's daily life and work across a range of everyday tasks. However, little prior work has explored how people use search engines and information resources as part of their creative processes, and how systems might better support users working on creative tasks. In this paper, we conducted an online survey with 175 participants to explore how people use search engines and online resources (e.g., images, videos, and social media) to support their creative tasks. Our participants reported information seeking to support a broad range of everyday creativity including tasks in arts, writing, crafts, and technical projects. Our findings show that participants' tasks included multiple stages of creative processes (e.g., creating ideas, combining ideas, executing plans) and that participants reported using search engines along with other tools (such as images and videos) to facilitate their creative process. By using Bayesian random effects regression models, we found that different stages of the creative process influence participants' use of tools. For example, for tasks that involved creating ideas, participants were more likely to use images and social sites, and when needing to put ideas into practice they were more likely to use videos. We also found differences in users' satisfaction with using the tools for different creative stages. Based on our findings, we provide recommendations for supporting users' information seeking needs during creative tasks.

A search trail is an interactive visualization of how a previous searcher approached a related task. Using search trails to assist users requires understanding aspects of the task, user, and trails. In this paper, we examine two questions. First, what are task characteristics that influence a user's ability to gain benefits from others' trails? Second, what is the impact of a "mismatch" between a current user's task and previous user's task which originated the trail? We report on a study that investigated the influence of two factors on participants' perceptions and behaviors while using search trails to complete tasks. Our first factor, task scope, focused on the scope of the task assigned to the participant (broad to narrow). Our manipulation of this factor involved varying the number of constraints associated with tasks. Our second factor, trail scope, focused on the scope of the task that originated the search trails given to participants. We investigated how task scope and trail scope affected participants' (RQ1) pre-task perceptions, (RQ2) post-task perceptions, and (RQ3) search behaviors. We discuss implications of our results for systems that use search trails to provide assistance.

Recently, there has been interest in integrating information retrieval systems more closely with users' knowledge development processes, especially to support exploratory search. In this work, we investigated how people organize and structure information they discover during exploratory searches. In a lab study, we asked 24 participants to take hand-written notes they could use in the future while they were completing an exploratory search. We then asked participants to organize their findings to share with someone else who wants to explore the topic. Finally, we conducted post-session interviews to gain insights into the types of information saved and how participants organized the information they found. In our qualitative analysis of the notes and interviews, we found that the notes included background information about the topic, key concepts, specific details, useful information sources, and information to help with the broader work task. Notes were primarily structured in lists, and reflected a combination of linear note-taking strategies and grouping by information source or topical themes. Participants changed the content and structure of the shared notes to make them easier to understand and to provide a more thorough overview of the topic. Our findings have implications for the design of search tools to help current searchers organize, structure, and synthesize information and to help future searchers engaged in similar information seeking tasks.

Popular messaging platforms such as Slack have given rise to hundreds of chatbots that users can engage with individually or as a group. We present a Wizard of Oz study on the use of searchbots (i.e., chatbots that perform specific types of searches) during collaborative information-seeking tasks. Specifically, we study searchbots that intervene dynamically and compare between two intervention types: (1) the searchbot presents questions to users to gather the information it needs to produce results, and (2) the searchbot monitors the conversation among the collaborators, infers the necessary information, and then displays search results with no additional input from the users. We investigate three research questions: (RQ1) What is the effect of a searchbot (and its intervention type) on participants» collaborative experience' (RQ2) What is the effect of a searchbot»s intervention type on participants» perceptions about the searchbot and level of engagement with the searchbot' and (RQ3) What are participants» impressions of a dynamic searchbot? Our results suggest that dynamic searchbots can enhance users» collaborative experience and that the intervention type does not greatly affect users» perceptions and level of engagement. Participants» impressions of the searchbot suggest unique opportunities and challenges for future work.

An important area of IR research involves understanding how task characteristics influence search behaviors and outcomes. Task complexity is one characteristic that has received considerable attention. One view of task complexity is through the lens of a priori determinability -- the level of uncertainty about task outcomes and processes experienced by the searcher. In this work, we manipulated the determinability of comparative tasks. Our task manipulation involved modifying the scope of the task by specifying exact items and/or exact (objective or subjective) dimensions to consider as part of the task. This paper reports on a within-subject study (N=144) where we investigated how our task manipulation influenced participants' perceptions, levels of engagement, search effort, and choice of search strategies. Our results suggest a complex relationship between task scope, determinability, and different outcome measures. Our most open-ended tasks were perceived to have low determinability (high uncertainty), but were the least challenging for participants due to satisficing. Furthermore, narrowing the scope of tasks by specifying items had a different effect than by specifying dimensions. Specifying items increased the task determinability (lower uncertainty) and made the task easier, while specifying dimensions did not increase the task determinability and made the task more challenging. A qualitative analysis of participants' queries suggests that searching for dimensions is more challenging than for items. Finally, we observed subtle differences between objective and subjective dimensions. We discuss implications for the design of IIR studies and tools to support users.

We report on a crowdsourced study that investigated how two factors influence the way people formulate information requests. Our first factor, medium , considers whether the request is produced using text or voice. Our second factor, target , considers whether the request is intended for a search engine or a human intermediary (i.e., someone who will search on the user’s behalf). In particular, we study how these two factors influence the way people formulate requests in situations where the information need has a specific type of extra-topical dimension (i.e., a type of constraint that is independent from the information need’s topic). We focus on six extra-topical dimensions: (1) domain knowledge, (2) viewpoint, (3) experiential, (4) venue location, (5) source location, and (6) temporal. The extra-topical dimension was manipulated by giving participants carefully constructed search tasks. We analyzed a large number of information requests produced by study participants, and address three research questions. We study the effects of our two factors (medium and target) on (RQ1) participants’ perceptions about their own information requests, (RQ2) the different characteristics of their information requests (e.g., natural language structure, retrieval performance), and (RQ3) participants’ strategies for requesting information when the search task has a specific type of extra-topical dimension. Our results found that both factors influenced participants’ perceptions about their own information requests, the characteristics of participants’ requests, and the strategies adopted by participants to request information matching the extra-topical dimension. Our results have implications for future research on methods that can harness (rather than ignore) extra-topical query terms to retrieve relevant information.

In this paper, we analyze the impact of task time limits as described by 24 participants in an experimental study investigating the use of a search assistance tool, the Search Guide (SG), during four search tasks with varying levels of cognitive complexity. Participants were given a 12 minute task time limit and a warning notification when 3 minutes remained. In post-experiment interviews, participants reported two different impacts of the time limit on use of the SG: ten described using the SG as a way to save time given the time limit while five reported less SG use due to uncertainty whether it would contain useful information. Participants also described skimming and reading pages more shallowly, selecting easier to read search results, and bookmarking pages more freely because of the limited time they had to complete the task.

Aggregated search is the task of combining results from multiple independent search systems in a single Search Engine Results Page (SERP). Aggregated search coherence refers to the extent to which different sources on the SERP focus on similar senses of an ambiguous or underspecified query. In previous studies, we found that the query senses in a set of vertical results can influence user engagement with the web results (the so-called “spillover” effect). In this work, we investigate five research questions (RQ1--RQ5) that extend our prior work. First, we investigate the extent to which results from different sources focus on different senses of an ambiguous query (RQ1). Second, we investigate how the vertical-to-web spillover effect varies across different verticals (RQ2). Then, we examine whether the level of spillover depends on the vertical position (RQ3) and on whether the vertical results are displayed with a border and different-colored background to distinguish them from the web results (RQ4). Finally, we propose a new method for displaying results from a particular vertical that are more consistent with the query senses in the web results (RQ5). We evaluate this new method based on how it influences users to make more correct decisions with respect to the web results—to engage with the web results when at least one of them is relevant and to avoid engaging with the web results otherwise. Our results show the following trends. In terms of RQ1, our analysis suggests that the top results from the web search engine are more diversified than the top results from our four different verticals considered (images, news, shopping, and video). In terms of RQ2, we found a stronger spillover effect for the images vertical than the news, shopping, and video verticals. In terms of RQ3, we found a stronger level of spillover when the vertical was positioned at the top of the SERP versus to the right side of the web results. In terms of RQ4, we found an interesting additive effect between the vertical’s position and displaying the vertical results enclosed in a border and with a different-colored background—the image vertical had no spillover when presented to the right side of the web results and with a border and background. Finally, in terms of RQ5, we found that our proposed vertical results selection approach can influence users to make more correct predictions about their level of engagement with the web results.

In this paper, we study how users interact with a search assistance tool while completing tasks of varying complexity. We designed a novel tool referred to as the search guide (SG) that displays the search trails (queries issued, results clicked, pages bookmarked) from three previous users who completed the task. We report on a laboratory study with 48 participants that investigates different factors that may influence user interaction with the SG and the effects of the SG on different outcome measures. Participants were asked to find and bookmark pages for four tasks of varying complexity and the SG was made available to half the participants. We collected log data and conducted retrospective stimulated recall interviews to learn about participants' use of the SG. Our results suggest the following trends. First, interaction with the SG was greater for more complex tasks. Second, the a priori determinability of the task (i.e., whether the task was perceived to be well-defined) helped predict whether participants gained a bookmark from the SG. Third, participants who interacted with the SG, but did not gain a bookmark, felt less system support than those who gained a bookmark and those who did not interact. Finally, a qualitative analysis of our interviews suggests differences in motivation and benefits from SG use for different levels of task complexity. Our findings extend prior research on search assistance tools and provide insights for the design of systems to help users with complex search tasks.

Aggregated search is the task of blending results from different search services, or verticals, into a set of web search results. Aggregated search coherence is the extent to which results from different sources focus on similar senses of an ambiguous or underspecified query. Prior work investigated the "spill-over" effect between a set of blended vertical results and the web results. These studies found that users are more likely to interact with the web results when the vertical results are more consistent with the user's intended query-sense. We extend this prior work by investigating three new research questions: (1) Does the spill-over effect generalize across different verticals? (2) Does the vertical rank moderate the level of spill-over? and (3) Does the presence of a border around the vertical results moderate the level of spill-over? We investigate four different verticals (images, news, shopping, and video) and measure spill-over using interaction measures associated with varying levels of engagement with the web results (bookmarks, clicks, scrolls, and mouseovers). Results from a large-scale crowdsourced study suggest that: (1) The spill-over effect generalizes across verticals, but is stronger for some verticals than others, (2) Vertical rank has a stronger moderating effect for verticals with a mid-level of spill-over, and (3) Including a border around the vertical results has a subtle moderating effect for those verticals with a low level of spill-over.

Aggregated search is the task of incorporating results from different search services, or verticals, into the web search results. Aggregated search coherence refers to the extent to which results from different sources focus on similar senses of a given query. Prior research investigated aggregated search coherence between images and web results. A user study showed that users are more likely to interact with the web results when the images are more consistent with the intended query-sense. We build upon this work and address three outstanding research questions about aggregated search coherence: (1) Does the same "spill-over" effect generalize to other verticals besides images? (2) Is the effect stronger when the vertical results include image thumbnails? and (3) What factors influence if and when a spill-over occurs from a user's perspective? We investigate these questions using a large-scale crowdsourcing study and a smaller-scale laboratory study. Results suggest that the spill-over effect occurs for some verticals (images, shopping, video), but not others (news), and that including thumbnails in the vertical results has little effect. Qualitative data from our laboratory study provides insights about participants' actions and thought-processes when faced with (in)coherent results.

While images are commonly used in search result presentation for vertical domains such as shopping and news, web search results surrogates remain primarily text-based. In this paper, we present results of two large-scale user studies to examine the effects of augmenting text-based surrogates with images extracted from the underlying webpage. We evaluate effectiveness and efficiency at both the individual surrogate level and at the results page level. Additionally, we investigate the influence of two factors: the goodness of the image in terms of representing the underlying page content, and the diversity of the results on a results page. Our results show that at the individual surrogate level, good images provide only a small benefit in judgment accuracy versus text-only surrogates, with a slight increase in judgment time. At the results page level, surrogates with good images had similar effectiveness and efficiency compared to the text-only condition. However, in situations where the results page items had diverse senses, surrogates with images had higher click precision versus text-only ones. Results of these studies show tradeoffs in the use of images in web search surrogates, and highlight particular situations where they can provide benefits.

In this article, we present findings from a survey of nearly 600 university employees' e‐mail use. The study provides a detailed comparison of use patterns between work and personal e‐mail accounts. Our results suggest that users engage in more “keeping” behaviors with work e‐mail than with personal e‐mail—respondents reported more frequent use of keeping actions and larger inbox sizes for their work accounts. However, we found correlations between individual respondents' e‐mail behaviors in the two contexts, indicating that personal preferences can play a role. We also report results pointing to e‐mail as an important boundary management artifact. We show evidence that the use of multiple e‐mail accounts may be a work‐personal boundary placement strategy, but also observe that a fair amount of boundary permeation occurs through e‐mail. To our knowledge, this study is one of the first to compare e‐mail use in both work and personal contexts across the same sample. Our findings extend prior research on personal information management regarding e‐mail use, and help inform the role of e‐mail in managing work‐personal boundaries. The results have implications for the design of e‐mail systems, organizational e‐mail policies, user training, and understanding the impacts of technology on daily life.

Aggregated search is the task of blending results from different specialized search services, or verticals, into the web search results. Aggregated search coherence refers to the degree to which results from different systems focus on similar senses of the query. While cross-component coherence has been cited as an important criterion for whole-page evaluation, its effect on search behavior has not been deeply investigated in prior research. In this work, we focus on the coherence between two aggregated search components: images and web results. In particular, we investigate whether the query-senses associated with the blended image results can influence user interaction with the web results. For example, if a user wants web results about "jaguar" the animal, are they more likely to examine the web results if the image results contain pictures of the animal instead of pictures of the car? Based on two large user studies, our results show that the image results can systematically affect user interaction with the web results. If the web results are largely consistent with the search task, then the effect of the image results is small. However, if the web results are only marginally consistent with the search task, such as when they are highly diversified across query-senses, the image results have a significant effect on user interaction with the web results. Our findings have implications on current research in whole-page evaluation, aggregated search, and diversity ranking.

Abstract This study examined how searchers interact with a web‐based, faceted library catalog when conducting exploratory searches. It applied multiple methods, including eye tracking and stimulated recall interviews, to investigate important aspects of faceted search interface use, specifically: (a) searcher gaze behavior—what components of the interface searchers look at; (b) how gaze behavior differs when training is and is not provided; (c) how gaze behavior changes as searchers become familiar with the interface; and (d) how gaze behavior differs depending on the stage of the search process. The results confirm previous findings that facets account for approximately 10–30% of interface use. They show that providing a 60‐second video demonstration increased searcher use of facets. However, searcher use of the facets did not evolve during the study session, which suggests that searchers may not, on their own, rapidly apply the faceted interfaces. The findings also suggest that searcher use of interface elements varied by the stage of their search during the session, with higher use of facets during decision‐making stages. These findings will be of interest to librarians and interface designers who wish to maximize the value of faceted searching for patrons, as well as to researchers who study search behavior.

introduction Share on Introduction to keeping, refinding and sharing personal information Authors: Deborah Barreau University of North Carolina at Chapel Hill University of North Carolina at Chapel HillView Profile , Robert Capra University of North Carolina at Chapel Hill University of North Carolina at Chapel HillView Profile , Susan Dumais Microsoft Corporation Microsoft CorporationView Profile , William Jones University of Washington University of WashingtonView Profile , Manuel Pérez-Quiñones Virginia Polytechnic Institute and State University Virginia Polytechnic Institute and State UniversityView Profile Authors Info & Claims ACM Transactions on Information SystemsVolume 26Issue 4Article No.: 18pp 1–3https://doi.org/10.1145/1402256.1402257Published:07 October 2008Publication History 3citation0DownloadsMetricsTotal Citations3Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation Alerts Save to binder temporarily unavailable - maintenance in progressSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access