Researchers often attribute social media’s appeal to its ability to elicit flow experiences of deep absorption and effortless engagement. Yet prolonged use has also been linked to distraction, fatigue, and lower mood. This paradox remains poorly understood, in part because prior studies rely on habitual or one-shot reports that ask participants to directly attribute flow to social media. To address this gap, we conducted a five-day field study with 40 participants, combining objective smartphone app tracking with daily reconstructions of flow-inducing activities. Across 673 reported flow occurrences, participants rarely associated flow with social media (2%). Instead, heavier social media use predicted fewer daily flow occurrences. We further examine this relationship through the effects of social media use on fatigue, mood, and motivation. Altogether, our findings suggest that flow and social media may not align as closely as assumed - and might even compete - underscoring the need for further research.

In digital knowledge work, flow promises not just productivity; it offers a pathway to well-being. Yet despite decades of flow research in HCI, we know little about how to design digital interventions that support it. In this work, we foreground lived interventions — everyday practices workers already use to foster flow — to uncover overlooked opportunities and chart new directions for digital intervention design. Specifically, we report findings from two studies: (1) a reflexive thematic analysis of open-ended survey responses (n = 160), surfacing 38 lived interventions across four categories: environment, organization, task shaping, and personal readiness; and (2) a quantitative online survey (n = 121) that validates this repertoire, identifies which interventions are broadly endorsed versus polarizing, and elicits visions of technological support. We contribute empirical insights into how digital workers cultivate flow, situate these lived interventions within existing literature, and derive design opportunities for future digital flow interventions.

The paradigm of web search is currently shifting from reactive information retrieval to Agentic AI, where proactive systems autonomously synthesise information to assist users. However, for these agents to be effective, they must understand which user parameters drive behaviour to resolve the personalisation cold-start problem. While cognitive architecture is a recognised factor, empirical evidence linking specific traits to web search interaction remains unclear. This paper investigates the Verbal-Imagery (V-I) cognitive style dimension and its influence on proactive search behaviour, mental workload (MWL), and overall search user interface (SUI) alignment. Through a controlled user study (N = 20), web search behaviours were evaluated using interaction logs and think-aloud protocols, while MWL and usability were assessed via NASA-TLX and the System Usability Scale (SUS). Our findings reveal that verbalisers and imagers adopt statistically distinct navigational preferences: ver-balisers prefer sporadic, reactive interactions, while imagers rely on structured, proactive synthesis such as the Knowledge Panel. It is worth noting that qualitative data identifies a "trust gap" in AI-generated overviews based on cognitive modality preferences. These results demonstrate that the V-I dimension is a critical parameter for user modelling in agentic systems. We conclude by proposing requirements for user-aware agentic information retrieval, providing a framework for agents to dynamically adapt their representation strategies to minimise cognitive friction and enhance trust in proactive computing environments. This work is licensed under a Creative Commons "Attribution 4.0 International" license.

Cognitive style, a user's habitual approach to information processing, offers a promising approach to personalising information searching (IS) systems, yet the underlying style-related search behaviours remain poorly understood. This study investigates how the Wholist–Analytic and Verbal–Imagery dimensions of cognitive style influence search behaviour and prefrontal cortex (PFC) activation during the Post-focus stage of exploratory search. Forty participants completed comparison search tasks while we recorded behavioural metrics, subjective workload ratings, and functional near-infrared spectroscopy (fNIRS) data. Our results demonstrate that cognitive style significantly predicted search engine results page (SERP) interaction patterns: analytics and imagers adopted structured navigation with detailed reading, whereas wholists and verbalisers preferred sporadic navigation with rapid scanning. Critically, fNIRS revealed distinct PFC activation patterns, specifically in the Ventrolateral PFC (VLPFC) and Dorsolateral PFC (DLPFC), corresponding to these behavioural differences. By mapping neuro-cognitive profiles to IR behaviours, this work provides empirical grounding for designing ''style-aware'' adaptive IR interfaces that tailor information density and navigational support to individual cognitive profiles.

Flow, a state of deep task engagement, is associated with optimal experience and well-being, making its detection a prolific HCI research focus. While physiological sensors show promise for flow detection, most studies are lab-based. Furthermore, brain sensing during natural work remains unexplored due to the intrusive nature of traditional EEG setups. This study addresses this gap by using wearable, around-the-ear EEG sensors to observe flow during natural knowledge work, measuring EEG throughout an entire day. In a semi-controlled field experiment, participants engaged in academic writing or programming, with their natural flow experiences compared to those from a classic lab paradigm. Our results show that natural work tasks elicit more intense flow than artificial tasks, albeit with smaller experience contrasts. EEG results show a well-known quadratic relationship between theta power and flow across tasks, and a novel quadratic relationship between beta asymmetry and flow during complex, real-world tasks.

Playing games has been shown to be an effective method of postwork recovery.Previous research has shown that gameplay with high cognitive involvement is effective for recovery.This finding conflicts with models of mental workload (MWL), which suggest that people feel best when cycling between high and low MWL.To unpack the relationship between recovery and mental workload, we designed a lab experiment where 40 participants experienced different combinations of high and low MWL while undertaking both work tasks and recovery gameplay, and we collected both selfreport and physiological (fNIRS) data.Results showed that high and low MWL games created different impacts on recovery, depending on the MWL of the prior work task.While fNIRS measurements of MWL varied as expected during work tasks, experience of MWL when playing games was not evident in the prefrontal cortex.We conclude by discussing the relationship between mental workload and theories of recovery.

Cognitive style has been shown to influence users’ interaction with search interfaces. However, as a fundamental dimension of cognitive styles, the relationship between the Verbal-Imagery (VI) cognitive style dimension and search behaviour has not been studied thoroughly, and it is not clear whether VI cognitive style can be used to inform search user interface design. We present a study (N=29), investigating how search behaviour and mental workload (MWL) changes relate to VI cognitive styles by examining participants’ search behaviour across three increasingly complex tasks. MWL was subjectively rated by participants, and blood oxygenation changes in the prefrontal cortex were measured using functional near-infrared spectroscopy (fNIRS).

With the growing concern for the health of ageing populations, much research continues to look at the impact of cognitive training, particularly in relation to cognitive decline. We sought to use novel techniques, including augmented reality and portable neurotechnology, to evaluate the impact of a dynamically adjusting cognitive training programme, in comparison to a statically challenging alternative. Before and after an 8-week training period, and at a 5-week follow-up, we used portable functional Near Infrared Spectroscopy to examine mental workload in a mixed battery of cognitive and transfer tasks. A recently developed tablet-based task was used to identify changes in cognitive misbinding. Augmented Reality was used to create a supermarket shopping experience, as a more ecologically valid and realistic transfer task relating to an everyday task relating to independence that quickly becomes difficult with cognitive decline. The analyses showed a decreased mental workload within the dorsolateral prefrontal cortex and that participants considerably increased their performance in the trained task. Some results were maintained at the 5-week follow-up assessment. In terms of transfer, we observed reliable group differences immediately after training completion, which were mainly driven by distinct conditions. Some behavioural memory gains were maintained during the follow-up. The use of novel technologies brought new insights into the effects produced by the dynamic computerised cognitive training programme, which has potential future applications in cognitive decline screening and prevention.

We can now buy consumer brain-computer interface devices to help us meditate and focus, but what are we aiming to achieve? Mental workload (MWL) is an established concept, and as a form of personal data could be useful for making positive life changes. However, MWL is typically only studied for isolated tasks to avoid overload and underload. We investigated lived experiences of MWL, aiming to understand how tracking such data could implicate our everyday lives. 19 participants, that had previously experienced tracking their mental workload, took part in interviews and an Interpretive Phenomenological Analysis identified four superordinate themes. Results point towards mixed and changing perceptions of MWL and the importance of fluctuating between MWL levels in daily life in terms of performances, perceptions, and wellbeing. These findings are captured in an apparent Cycle, which outside factors can disrupt, and we discuss these cycles in terms of personal informatics and work performance.

Visualization literacy, the ability to interpret and comprehend visual designs, is recognized as an essential skill by the visualization community. We identify and investigate barriers to comprehending parallel coordinates plots (PCPs), one of the advanced graphical representations for the display of multivariate and high-dimensional data. We develop a parallel coordinates literacy test with diverse images generated using popular PCP software tools. The test improves PCP literacy and evaluates the user’s literacy skills. We introduce an interactive educational tool that assists the teaching and learning of parallel coordinates by offering a more active learning experience. Using this pedagogical tool, we aim to advance novice users’ parallel coordinates literacy skills. Based on the hypothesis that an interactive tool that links traditional Cartesian Coordinates with PCPs interactively will enhance PCP literacy further than static slides, we compare the learning experience using traditional slides with our novel software tool and investigate the efficiency of the educational software with an online, crowdsourced user-study. User-study results show that our pedagogical tool positively impacts a user’s PCP comprehension.

Conversational search systems, such as Google Assistant and Microsoft Cortana, provide a new search paradigm where users are allowed, via natural language dialogues, to communicate with search systems. Evaluating such systems is very challenging since search results are presented in the format of natural language sentences. Given the unlimited number of possible responses, collecting relevance assessments for all the possible responses is infeasible. In this paper, we propose POSSCORE, a simple yet effective automatic evaluation method for conversational search. The proposed embedding-based metric takes the influence of part of speech (POS) of the terms in the response into account. To the best knowledge, our work is the first to systematically demonstrate the importance of incorporating syntactic information, such as POS labels, for conversational search evaluation. Experimental results demonstrate that our metrics can correlate with human preference, achieving significant improvements over state-of-the-art baseline metrics.

As computer interfaces spread across and even into our bodies, so we find ourselves engaged in ever more deeply connected bodily interactions. From the mouse and keyboard, to wearables and gestural...

As Digital Manufacturing transforms traditionally physical work into more system-monitoring tasks, new methods are required for understanding people’s mental workload and prolonged capacity for focused attention. Many physiological measures have shown promise for detecting changes in cognitive state, and recent advances in sensor technology offer minimally-invasive ways to monitor our cognitive activity. Previous research in functional near-infrared spectroscopy, for example, has observed changes in cerebral hemodynamic response during periods of high demand within tasks. This work investigated the relationships among task demand, fatigue, and attention degradation in a sustained attention task, and their effect on heart rate, breathing rate, nose temperature and hemodynamic response in the prefrontal cortex and middle temporal gyrus. Analysis revealed a small but significant effect of fatigue on heart rate relative to baseline, breathing rate and hemodynamic response. Task demand had a small but significant effect on breathing rate and nose temperature, both relative to baseline, but no difference between levels of demand was observed in heart rate or hemodynamic response. Our results provide insight into what physiological data can tell us about cognitive state, ability to focus, and the impact of fatigue over time.

The motivation behind using physiological measures to estimate cognitive activity is typically to build technology that can help people to understand themselves and their work, or indeed for systems to do so and adapt. While functional Near Infrared Spectroscopy (fNIRS) has been shown to reliably reflect manipulations of mental workload in different work tasks, we still need to establish whether fNIRS can differentiate variety within common office-like tasks in order to broaden our understanding of the factors involved in tracking them in real working conditions. 20 healthy participants (8 females, 12 males), whose work included office-like tasks, took part in a user study that investigated a) the sensitivity of fNIRS for measuring mental workload variations in representations of everyday reading and writing tasks, and b) how representations of natural interruptions are reflected in the data. Results supported fNIRS measuring PFC activation in differentiating between workload levels for reading tasks but not writing tasks in terms of increased oxygenated haemoglobin (O2Hb) and decreased deoxygenated haemoglobin (HHb), for harder conditions compared to easier conditions. There was considerable support for fNIRS in detecting changes in workload levels due to interruptions. Variations in workload levels during the interruptions could be understood in relation to spare capacity models. These findings may guide future work into sustained monitoring of cognitive activity in real-world settings.

Conversational search systems, such as Google assistant and Microsoft Cortana, enable users to interact with search systems in multiple rounds through natural language dialogues. Evaluating such systems is very challenging, given that any natural language responses could be generated, and users commonly interact for multiple semantically coherent rounds to accomplish a search task. Although prior studies proposed many evaluation metrics, the extent of how those measures effectively capture user preference remain to be investigated. In this article, we systematically meta-evaluate a variety of conversational search metrics. We specifically study three perspectives on those metrics: (1) reliability : the ability to detect “actual” performance differences as opposed to those observed by chance; (2) fidelity : the ability to agree with ultimate user preference; and (3) intuitiveness : the ability to capture any property deemed important: adequacy, informativeness, and fluency in the context of conversational search. By conducting experiments on two test collections, we find that the performance of different metrics vary significantly across different scenarios, whereas consistent with prior studies, existing metrics only achieve weak correlation with ultimate user preference and satisfaction. METEOR is, comparatively speaking, the best existing single-turn metric considering all three perspectives. We also demonstrate that adapted session-based evaluation metrics can be used to measure multi-turn conversational search, achieving moderate concordance with user satisfaction. To our knowledge, our work establishes the most comprehensive meta-evaluation for conversational search to date.

Introducing interactivity to films has proven a longstanding and difficult challenge due to their narrative-driven, linear and theatre-based nature. Previous research has suggested that Brain-Computer Interfaces (BCI) may be a promising approach but also revealed a tension between being immersed in the film and thinking about control. We report a performance-led and in-the-wild study of a BCI film called The MOMENT covering its design rationale and how it was experienced by the public as controllers, non-controllers and repeat viewers. Our findings suggest that BCI movies should be designed to be credibly controllable, generate personal versions, be watchable as linear films, encourage repeat viewing and fit the medium of cinema. They also reveal how viewers appreciated the sense of editing their own personal cuts, suggesting a new stance on introducing interactivity into lean-back media in which filmmakers release editorial control to users to make their own versions.

Systematic reviews are a comprehensive and parameterised form of literature review, found in most disciplines, that involve exhaustive analyses and rigorous interpretation of prior literature. Performing systematic reviews, however, can involve repetitive and laborious work in order to reach reliable standards. Strict guidelines and availability of published reviews make the task amenable to computerised assistance and automation using text mining, information extraction, and machine learning techniques. However, it is unclear which aspects of this Work Task are best suited for such support. This paper describes a three-month ethnographic study and CognitiveWork Analysis of the systematic reviews performed by a medical research group. Our findings show that the IR aspects of systematic reviews involve many tasks at two separate levels: 1) taxonomic organisation of documents and sub-document elements in relation to topic queries and domain-specific resources, and 2) extraction methods for structured summaries from the classified resources. This provides the basis for future work designing search tools with localised optimization and subtask automation to support specific phases of the process.

With greater access to computational resources, people use search to address many everyday challenges in their lives, including solving technology problems. Although there are now many useful ‘how-to’ resources online (especially videos on YouTube), it can still be difficult to identify, understand, and resolve certain kinds of technical problem. While research tasks have been studied for many years and we know the tactics people use, we know far less about searchers’ tactics for how-to technical tasks that involve actually being able to apply found information to resolve a problem. Crucial to our study was developing and studying a highly realistic, how-to technical task, for which there was no single guidance resource: making a phone safe for a child. After providing 39 participants with an actual phone to fix, and a search engine to perform the task, we analysed their search tactics using retrospective cued think aloud interviews. Our primary contribution is a set of 77 tactics used, in three categories, along with detail of how common they were. We conclude that people had a lot of tactics in their repertoire. Although it was not hard for participants to find relevant information, what was hard was for participants to find information they could use; indeed only 23% of participants successfully completed the entire task. Domain knowledge affected the choice of tactics used (although not necessarily towards better task success). We discuss these influences and make design recommendations for how future search systems can support those in resolving how-to technical tasks.

The field of neuroscience has fruitfully contributed to a wide variety of other fields, for example, economics, marketing and information systems, where the broad adoption and influence of neurophysiological (NP) research tools led to the creation of several new subfields, including neuroeconomics.

Neighbourhoods have been described as ‘the building blocks of public services society’. Their subjective nature, however, and the resulting difficulties in collecting data, means that in many countries there are no officially defined neighbourhoods either in terms of names or boundaries. This has implications not only for policy but also business and social decisions as a whole. With the absence of neighbourhood boundaries many studies resort to using standard administrative units as proxies. Such administrative geographies, however, often have a poor fit with those perceived by residents. Our approach detects these important social boundaries by automatically mining the Web en masse for passively declared neighbourhood data within postal addresses. Focusing on the United Kingdom (UK), this research demonstrates the feasibility of automated extraction of urban neighbourhood names and their subsequent mapping as vague entities. Importantly, and unlike previous work, our process does not require any neighbourhood names to be established a priori.

Feedback is valuable for allowing us to improve on tasks. While retrospective feedback can help us improve for next time, feedback ‘in action’ can allow us to improve the outcome of on-going tasks. In this article, we use data from functional Near InfraRed Spectroscopy to provide participants with feedback about their mental workload levels during high-workload tasks. We evaluate the impact of this feedback on task performance and perceived task performance, in comparison to industry standard mid-task self-assessments, and explore participants’ perceptions of this feedback. In line with previous work, we confirm that deploying self-reporting methods affect both perceived and actual performance. Conversely, we conclude that our objective concurrent feedback correlated more closely with task demand, supported reflection in action, and did not negatively affect performance. Future work, however, should focus on the design of this feedback and the potential behaviour changes that will result.

Theoretical abstractions, of many different aspects of search, have played a crucial role in driving research into human information seeking and retrieval forward. From models of the Information Seeking Process, to how we perceive search systems, these models help us to 1) conceptually formalise and separate aspects of the model's focus, 2) communicate more clearly about these aspects, 3) create hypotheses for subsequent research, and 4) produce implications for future systems. Implicit in these four aspects is that models and theories should have a focus and a purpose. After clarifying the relationships between models, theories, and meta-theories, this perspectives paper introduces the Tetris Model of Resolving Information Needs within the Information Seeking Process, the purpose of which is to better represent the behaviours around the Human Computer Interaction with Information Retrieval, which are often confounded within stage-based models of the Information Seeking Process. In particular, the possible sequence of actions performed by a searcher are typically linearly aligned from left-to-right, and thus imply a temporal progression. The differing focus of the Tetris model is to better capture the temporal experience of searching, by removing the implied progression of left-to-right. The aim of this perspectives paper, therefore, is to introduce this new Tetris Model, such that it can be used to formalise people's interactive experiences in a new way, so that we can more clearly communicate about them, create hypotheses from the model, and consider novel design implications based upon it.

Amongst the many tasks in our lives, we encounter web forms on a regular basis, whether they are mundane like registering for a website, or complex and important like tax returns. There are many aspects of Usability, but one concern for user interfaces is to reduce mental workload and error rates. Whilst most assessment of mental workload is subjective and retrospective reporting by users, we examine the potential of functional Near Infrared Spectroscopy (fNIRS) as a tool for objectively and concurrently measuring mental workload during usability testing. We use this technology to evaluate the design of three different form layouts for a car insurance claim process, and show that a form divided into subforms increases mental workload, contrary to our expectations. We conclude that fNIRS is highly suitable for objectively examining mental workload during usability testing, and will therefore be able to provide more detailed insight than summative retrospective assessments. Further, for the fNIRS community, we show that the technology can easily move beyond typical psychology tasks, and be used for more natural study tasks.

This paper explores the design space of bio-responsive entertainment, in this case using a film that responds to the brain and blink data of users. A film was created with four parallel channels of footage, where blinking and levels of attention and meditation, as recorded by a commercially available EEG device, affected which footage participants saw. As a performance-led piece of research in the wild, this experience, named #Scanners, was presented at a week long national exhibition in the UK. We examined the experiences of 35 viewers, and found that these forms of partially-involuntary control created engaging and enjoyable, but sometimes distracting, experiences. We translate our findings into a two-dimensional design space between the extent of voluntary control that a physiological measure can provide against the level of conscious awareness that the user has of that control. This highlights that novel design opportunities exist when deviating from these two-dimensions - when giving up conscious control and when abstracting the affect of control. Reflection on of how viewers negotiated this space during an experience reveals novel design tactics.

Models of information seeking, including Kuhlthau's Information Search Process model, describe fundamentally different macro-level stages. Current search systems usually do not provide support for these stages, but provide a static set of features predominantly focused on supporting micro-level search interactions. This paper investigates the utility of search user interface (SUI) features at different macro-level stages of complex tasks. A user study was designed, using simulated work tasks, to explicitly place users within different stages of a complex task: pre-focus, focus, and post-focus. Active use, passive use and perceived usefulness of features were analysed in order to derive when search features are most useful. Our results identify significant differences in the utility of SUI features between each stage. Specifically, we have observed that informational features are naturally useful in every stage, while input and control features decline in usefulness after the pre-focus stage, and personalisable features become more useful after the pre-focus stage. From these findings, we conclude that features less commonly found in web search interfaces can provide value for users, without cluttering simple searches, when provided at the right times.

In this paper we present #Scanners, a digital arts installation that aims to bridge the gap between digital arts and neuroscience. #Scanners is an experience in which an individual wears a wireless brain scanners whilst being presented media which is dynamically affected by the individuals physiology. A prototype system has been successfully trialled on roughly 100 users over the past 18 months and has received unanimously positive feedback. We state the minimal additional requirements for demonstrating a Higher Fidelity prototype system and argue the value of including #Scanners at C&C2015.

There has been a massive growth in the number of people who film and upload amateur footage of events to services such as Facebook and Youtube, or even stream live to services such as LiveStream. We present an exploratory study that investigates the potential of these spectators in creating footage en masse; in this case, during a live trial at a local marathon. We deployed a prototype app, RunSpotRun, as a technology probe to see what kinds of footage spectators would produce. We present an analysis of this footage in terms of its coverage, quality, and contents, and also discuss the implications for a) spectators enjoying the race, and b) extracting the stories of individual runners throughout the race. We conclude with a discussion of the challenges that remain for deploying such technology at a larger scale.

Recent efforts have shown that functional near-infrared spectroscopy (fNIRS) has potential value for brain sensing in HCI user studies. Research has shown that, although large head movement significantly affects fNIRS data, typical keyboard use, mouse movement, and non-task-related verbalisations do not affect measurements during Verbal tasks. This work aims to examine the Reliability of fNIRS, by 1) confirming these prior findings, and 2) significantly extending our understanding of how artefacts affect recordings during Spatial tasks, since much of user interfaces and interaction is inherently spatial. Our results show that artefacts have a significantly different impact during Verbal and Spatial tasks. We contribute clearer insights into using fNIRS as a tool within HCI user studies.

This Tutorial+Workshop will discuss opportunities and challenges involved in using neuro-physiological tools/techniques (such as fMRI, fNIRS, EEG, eye-tracking, GSR, HR, and facial expressions) and theories in information retrieval. The hybrid format will engage researchers and students at different levels of expertise, from those who are active in this area to those who are interested and want to learn more. The workshop will combine presentations, discussions and tutorial elements and consist of four segments (tutorial, completed research, work-in-progress, closing panel).

Serendipitously discovering new information can bring many benefits. Although we can design systems to highlight serendipitous information, serendipity cannot be easily orchestrated and is thus hard to study. In this paper, we deployed a working search engine that matched search results with Facebook 'Like' data, as a technology probe to examine naturally occurring serendipitous discoveries. Search logs and diary entries revealed the nature of these occasions in both leisure and work contexts. The findings support the use of the micro-serendipity model in search system design.

The Think Aloud Protocol (TAP) is a verbalisation technique widely employed in HCI user studies to give insight into user experience, yet little work has explored the impact that TAPs have on participants during user studies. This paper utilises a brain sensing technique, fNIRS, to observe the effect that TAPs have on participants. Functional Near-Infrared Spectroscopy (fNIRS) is a brain sensing technology that offers the potential to provide continuous, detailed insight into brain activity, enabling an objective view of cognitive processes during complex tasks. Participants were asked to perform a mathematical task under 4 conditions: nonsense verbalisations, passive concurrent think aloud protocol, invasive concurrent think aloud protocol, and a baseline of silence. Subjective ratings and performance measures were collected during the study. Our results provide a novel view into the effect that different forms of verbalisation have on workload during tasks. Further, the results provide a means for estimating the effect of spoken artefacts when measuring workload, which is another step towards our goal of proactively involving fNIRS analysis in ecologically valid user studies.

Neighbourhoods have been described by the UK Secretary of State for Communities and Local Government as the "building blocks of public service society". Despite this, difficulties in data collection combined with the concept's subjective nature have left most countries lacking official neighbourhood definitions. This issue has implications not only for policy, but for the field of computational social science as a whole (with many studies being forced to use administrative units as proxies despite the fact that these bear little connection to resident perceptions of social boundaries). In this paper we illustrate that the mass linguistic datasets now available on the internet need only be combined with relatively simple linguistic computational models to produce definitions that are not only probabilistic and dynamic, but do not require a priori knowledge of neighbourhood names.

A proposal summary for the EuroHCIR workshop at SIGIR2013.

While it is easy to identify whether someone has found a piece of information during a search task, it is much harder to measure how much someone has learned during the search process. Searchers who are learning often exhibit exploratory behaviors, and so current research is often focused on improving support for exploratory search. Consequently, we need effective measures of learning to demonstrate better support for exploratory search. Some approaches, such as quizzes, measure recall when learning from a fixed source of information. This research, however, focuses on techniques for measuring open‐ended learning, which often involve analyzing handwritten summaries produced by participants after a task. There are two common techniques for analyzing such summaries: (a) counting facts and statements and (b) judging topic coverage. Both of these techniques, however, can be easily confounded by simple variables such as summary length. This article presents a new technique that measures depth of learning within written summaries based on Bloom's taxonomy (B.S. Bloom & M.D. Engelhart, 1956). This technique was generated using grounded theory and is designed to be less susceptible to such confounding variables. Together, these three categories of measure were compared by applying them to a large collection of written summaries produced in a task‐based study, and our results provide insights into each of their strengths and weaknesses. Both fact‐to‐statement ratio and our own measure of depth of learning were effective while being less affected by confounding variables. Recommendations and clear areas of future work are provided to help continued research into supporting sensemaking and learning.

The aim of this workshop is to provide a focal point for research and technology dedicated to persuasion and influence on mobile platforms. We aspire to establish a scientific network and community dedicated to emerging technologies for persuasion using mobile devices. This workshop would be a unique opportunity for interaction designers and researchers in this area to share their latest research and technologies on 'nudge' methods with the scientific communities. Patterns of consumption such as drinking and smoking are shaped by the taken-for-granted practices of everyday life. However, these practices are not fixed and 'immensely malleable'. Consequently, it is important to understand how the habits of everyday life change and evolve. Our decisions are inevitably influenced by how the choices are presented. Therefore, it is legitimate to deliberately 'nudge' people's behaviour in order to improve their lives. Mobile devices can play a significant role in shaping normal practices in three distinct ways: (1) they facilitate the capture of information at the right time and place; (2) they provide non-invasive and cost effective methods for communicating personalised data that compare individual performance with relevant social group performance; and (3) social network sites running on the device facilitate communication of personalised data that relate to the participant's self-defined community. Among the issues the workshop will take on are:(a) What opportunities do mobile interventions provide? (b) How far the intervention should go? (c) Is persuasion ethical? and (d) How can we extend the scale of intervention in a society using mobile devices? Participants will contribute to the workshop with examples of nudge and persuasive technologies, and we will work together to create novel ideas, interactive applications on the phone, and discuss future opportunities.

Could people use tagging to manage day-to-day work in their personal computing environment? Could tagging be sufficiently generic and lightweight to support diverse ways of working and, perhaps, support new and efficient practices for managing applications and accessing documents? We investigate these issues by implementing the TAGtivity system that enables users to tag resources in the context of their ongoing work. We deployed TAGtivity and studied users' tagging practices in their actual work places over a three week period. Our analysis of interviews and logs reveals that affordances of the TAGtivity system supported users in a variety of information and activity management tasks. These include new practices for managing emerging activities and ephemeral information and accessing documents across application data silos.

Abstract When users have poorly defined or complex goals, search interfaces that offer only keyword‐searching facilities provide inadequate support to help them reach their information‐seeking objectives. The emergence of interfaces with more advanced capabilities, such as faceted browsing and result clustering, can go some way toward addressing such problems. The evaluation of these interfaces, however, is challenging because they generally offer diverse and versatile search environments that introduce overwhelming amounts of independent variables to user studies; choosing the interface object as the only independent variable in a study would reveal very little about why one design outperforms another. Nonetheless, if we could effectively compare these interfaces, then we would have a way to determine which was best for a given scenario and begin to learn why. In this article, we present a formative inspection framework for the evaluation of advanced search interfaces through the quantification of the strengths and weaknesses of the interfaces in supporting user tactics and varying user conditions. This framework combines established models of users and their needs and behaviors to achieve this. The framework is applied to evaluate three search interfaces and demonstrates the potential value of this approach to interactive information retrieval evaluation.

Directional faceted browsers, such as the popular column browser iTunes, let a person pick an instance from any column-facet to start their search for music. The expected effect is that any columns to the right are filtered. In keeping with this directional filtering from left to right, however, the unexpected effect is that the columns to the left of the click provide no information about the possible associations to the selected item. In iTunes, this means that any selection in the Album column on the right returns no information about either the Artists (immediate left) or Genres (leftmost) associated with the chosen album.

Temporal events, while often discrete, also have interesting relationships within and across times: larger events are often collections of smaller more discrete events (battles within wars; artists' works within a form); events at one point also have correlations with events at other points (a play written in one period is related to its performance over a period of time). Most temporal visualisations, however, only represent discrete data points or single data types along a single timeline: this event started here and ended there; this work was published at this time; this tag was popular for this period. In order to represent richer, faceted attributes of temporal events, we present Continuum. Continuum enables hierarchical relationships in temporal data to be represented and explored; it enables relationships between events across periods to be expressed, and in particular it enables user-determined control over the level of detail of any facet of interest so that the person using the system can determine a focus point, no matter the level of zoom over the temporal space. We present the factors motivating our approach, our evaluation and implementation of this new visualisation which makes it easy for anyone to apply this interface to rich, large-scale datasets with temporal data.