Pirate stealth or inattentional blindness?:the effects of target relevance and sustained attention on security monitoring for experienced and naïve operators

Closed Circuit Television (CCTV) operators are responsible for maintaining security in various applied settings. However, research has largely ignored human factors that may contribute to CCTV operator error. One important source of error is inattentional blindness--the failure to detect unexpected but clearly visible stimuli when attending to a scene. We compared inattentional blindness rates for experienced (84 infantry personnel) and naïve (87 civilians) operators in a CCTV monitoring task. The task-relevance of the unexpected stimulus and the length of the monitoring period were manipulated between participants. Inattentional blindness rates were measured using typical post-event questionnaires, and participants' real-time descriptions of the monitored event. Based on the post-event measure, 66% of the participants failed to detect salient, ongoing stimuli appearing in the spatial field of their attentional focus. The unexpected task-irrelevant stimulus was significantly more likely to go undetected (79%) than the unexpected task-relevant stimulus (55%). Prior task experience did not inoculate operators against inattentional blindness effects. Participants' real-time descriptions revealed similar patterns, ruling out inattentional amnesia accounts

From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences

We describe the state-of-the-art in performance modeling and prediction for Information Retrieval (IR), Natural Language Processing (NLP) and Recommender Systems (RecSys) along with its shortcomings and strengths. We present a framework for further research, identifying five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of prediction models describing the relationship between assumptions, features and resulting performanc

Reducing Risk in Digital Self-Control Tools: Design Patterns and Prototype

Many users take advantage of digital self-control tools to self-regulate their device usage through interventions such as timers and lockout mechanisms. One of the major challenges faced by these tools is the user reacting against their self-imposed constraints and abandoning the tool. Although lower-risk interventions would reduce the likelihood of abandonment, previous research on digital self-control tools has left this area of study relatively unexplored. In response, this paper contributes two foundational principles relating risk and effectiveness; four widely applicable novel design patterns for reducing risk of abandonment of digital self-control tools (continuously variable interventions, anti-aging design, obligatory bundling of interventions, and intermediary control systems); and a prototype digital self-control tool that implements these four low-risk design patterns

Novel Datasets, User Interfaces and Learner Models to Improve Learner Engagement Prediction on Educational Videos

With the emergence of Open Education Resources (OERs), educational content creation has rapidly scaled up, making a large collection of new materials made available. Among these, we find educational videos, the most popular modality for transferring knowledge in the technology-enhanced learning paradigm. Rapid creation of learning resources opens up opportunities in facilitating sustainable education, as the potential to personalise and recommend specific materials that align with individual users’ interests, goals, knowledge level, language and stylistic preferences increases. However, the quality and topical coverage of these materials could vary significantly, posing significant challenges in managing this large collection, including the risk of negative user experience and engagement with these materials. The scarcity of support resources such as public datasets is another challenge that slows down the development of tools in this research area. This thesis develops a set of novel tools that improve the recommendation of educational videos. Two novel datasets and an e-learning platform with a novel user interface are developed to support the offline and online testing of recommendation models for educational videos. Furthermore, a set of learner models that accounts for the learner interests, knowledge, novelty and popularity of content is developed through this thesis. The different models are integrated together to propose a novel learner model that accounts for the different factors simultaneously. The user studies conducted on the novel user interface show that the new interface encourages users to explore the topical content more rigorously before making relevance judgements about educational videos. Offline experiments on the newly constructed datasets show that the newly proposed learner models outperform their relevant baselines significantly

Effects of Transparency and Haze on Trust and Performance During a Full Motion Video Analysis Task

Automation is pervasive across all task domains, but its adoption poses unique challenges within the intelligence, surveillance, and reconnaissance (ISR) domain. When users are unable to establish optimal levels of trust in the automation, task accuracy, speed, and automation usage suffer (Chung & Wark, 2016). Degraded visual environments (DVEs) are a particular problem in ISR; however, their specific effects on trust and task performance are still open to investigation (Narayanaswami, Gandhe, & Mehra, 2010). Research suggests that transparency of automation is necessary for users to accurately calibrate trust levels (Lyons et al., 2017). Chen et al. (2014) proposed three levels of transparency, with varying amounts of information provided to the user at each level. Transparency may reduce the negative effects of DVEs on trust and performance, but the optimal level of transparency has not been established (Nicolau & McKnight, 2006). The current study investigated the effects of varying levels of transparency and image haze on task performance and user trust in automation. A new model predicting trust from attention was also proposed. A secondary aim was to investigate the usefulness of task shedding and accuracy as measures of trust. A group of 48 undergraduates attempted to identify explosive emplacement activity within a series of full motion video (FMV) clips, aided by an automated analyst. The experimental setup was intended to replicate Level 5 automation (Sheridan & Verplank, 1978). Reliability of the automated analyst was primed to participants as 78% historical accuracy. For each clip, participants could shed their decision to an automated analyst. Higher transparency of automation predicted significantly higher accuracy, whereas hazy visual stimuli predicted significantly lower accuracy and 2.24 times greater likelihood of task shedding. Trust significantly predicted accuracy, but not task shedding. Participants were fastest in the medium transparency condition. The proposed model of attention was not supported; however, participants’ scanning behavior differed significantly between hazy and zero haze conditions. The study was limited by task complexity due to efforts to replicate real-world conditions, leading to confusion on the part of some participants. Results suggested that transparency of automation is critical, and should include purpose, process, performance, reason, algorithm, and environment information. Additional research is needed to explain task shedding behavior and to investigate the relationship between degrade visual environments, transparency of automation, and trust in automation

From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences

Non peer reviewe

Using contextual information to understand searching and browsing behavior

There is great imbalance in the richness of information on the web and the succinctness and poverty of search requests of web users, making their queries only a partial description of the underlying complex information needs. Finding ways to better leverage contextual information and make search context-aware holds the promise to dramatically improve the search experience of users. We conducted a series of studies to discover, model and utilize contextual information in order to understand and improve users' searching and browsing behavior on the web. Our results capture important aspects of context under the realistic conditions of different online search services, aiming to ensure that our scientific insights and solutions transfer to the operational settings of real world applications