Understanding Mobile Search Task Relevance and User Behaviour in Context

Improvements in mobile technologies have led to a dramatic change in how and when people access and use information, and is having a profound impact on how users address their daily information needs. Smart phones are rapidly becoming our main method of accessing information and are frequently used to perform `on-the-go' search tasks. As research into information retrieval continues to evolve, evaluating search behaviour in context is relatively new. Previous research has studied the effects of context through either self-reported diary studies or quantitative log analysis; however, neither approach is able to accurately capture context of use at the time of searching. In this study, we aim to gain a better understanding of task relevance and search behaviour via a task-based user study (n=31) employing a bespoke Android app. The app allowed us to accurately capture the user's context when completing tasks at different times of the day over the period of a week. Through analysis of the collected data, we gain a better understanding of how using smart phones on the go impacts search behaviour, search performance and task relevance and whether or not the actual context is an important factor.Comment: To appear in CHIIR 2019 in Glasgow, U

Construct-level predictive validity of educational attainment and intellectual aptitude tests in medical student selection: meta-regression of six UK longitudinal studies

Background: Measures used for medical student selection should predict future performance during training. A problem for any selection study is that predictor-outcome correlations are known only in those who have been selected, whereas selectors need to know how measures would predict in the entire pool of applicants. That problem of interpretation can be solved by calculating construct-level predictive validity, an estimate of true predictor-outcome correlation across the range of applicant abilities. Methods: Construct-level predictive validities were calculated in six cohort studies of medical student selection and training (student entry, 1972 to 2009) for a range of predictors, including A-levels, General Certificates of Secondary Education (GCSEs)/O-levels, and aptitude tests (AH5 and UK Clinical Aptitude Test (UKCAT)). Outcomes included undergraduate basic medical science and finals assessments, as well as postgraduate measures of Membership of the Royal Colleges of Physicians of the United Kingdom (MRCP(UK)) performance and entry in the Specialist Register. Construct-level predictive validity was calculated with the method of Hunter, Schmidt and Le (2006), adapted to correct for right-censorship of examination results due to grade inflation. Results: Meta-regression analyzed 57 separate predictor-outcome correlations (POCs) and construct-level predictive validities (CLPVs). Mean CLPVs are substantially higher (.450) than mean POCs (.171). Mean CLPVs for first-year examinations, were high for A-levels (.809; CI: .501 to .935), and lower for GCSEs/O-levels (.332; CI: .024 to .583) and UKCAT (mean = .245; CI: .207 to .276). A-levels had higher CLPVs for all undergraduate and postgraduate assessments than did GCSEs/O-levels and intellectual aptitude tests. CLPVs of educational attainment measures decline somewhat during training, but continue to predict postgraduate performance. Intellectual aptitude tests have lower CLPVs than A-levels or GCSEs/O-levels. Conclusions: Educational attainment has strong CLPVs for undergraduate and postgraduate performance, accounting for perhaps 65% of true variance in first year performance. Such CLPVs justify the use of educational attainment measure in selection, but also raise a key theoretical question concerning the remaining 35% of variance (and measurement error, range restriction and right-censorship have been taken into account). Just as in astrophysics, ‘dark matter’ and ‘dark energy’ are posited to balance various theoretical equations, so medical student selection must also have its ‘dark variance’, whose nature is not yet properly characterized, but explains a third of the variation in performance during training. Some variance probably relates to factors which are unpredictable at selection, such as illness or other life events, but some is probably also associated with factors such as personality, motivation or study skills

Nomadic input on mobile devices: the influence of touch input technique and walking speed on performance and offset modeling

In everyday life people use their mobile phones on-the-go with different walking speeds and with different touch input techniques. Unfortunately, much of the published research in mobile interaction does not quantify the influence of these variables. In this paper, we analyze the influence of walking speed, gait pattern and input techniques on commonly used performance parameters like error rate, accuracy and tapping speed, and we compare the results to the static condition. We examine the influence of these factors on the machine learned offset model used to correct user input and we make design recommendations. The results show that all performance parameters degraded when the subject started to move, for all input techniques. Index finger pointing techniques demonstrated overall better performance compared to thumb-pointing techniques. The influence of gait phase on tap event likelihood and accuracy was demonstrated for all input techniques and all walking speeds. Finally, it was shown that the offset model built on static data did not perform as well as models inferred from dynamic data, which indicates the speed-specific nature of the models. Also, models identified using specific input techniques did not perform well when tested in other conditions, demonstrating the limited validity of offset models to a particular input technique. The model was therefore calibrated using data recorded with the appropriate input technique, at 75% of preferred walking speed, which is the speed to which users spontaneously slow down when they use a mobile device and which presents a tradeoff between accuracy and usability. This led to an increase in accuracy compared to models built on static data. The error rate was reduced between 0.05% and 5.3% for landscape-based methods and between 5.3% and 11.9% for portrait-based methods

Data, Data Everywhere, and Still Too Hard to Link: Insights from User Interactions with Diabetes Apps

For those with chronic conditions, such as Type 1 diabetes, smartphone apps offer the promise of an affordable, convenient, and personalized disease management tool. How- ever, despite significant academic research and commercial development in this area, diabetes apps still show low adoption rates and underwhelming clinical outcomes. Through user-interaction sessions with 16 people with Type 1 diabetes, we provide evidence that commonly used interfaces for diabetes self-management apps, while providing certain benefits, can fail to explicitly address the cognitive and emotional requirements of users. From analysis of these sessions with eight such user interface designs, we report on user requirements, as well as interface benefits, limitations, and then discuss the implications of these findings. Finally, with the goal of improving these apps, we identify 3 questions for designers, and review for each in turn: current shortcomings, relevant approaches, exposed challenges, and potential solutions

Gambling Alone? A Study of Solitary and Social Gambling in America

In his acclaimed 2000 book Bowling Alone, Robert Putnam documents a disturbing social trend of the broadest kind. Putnam cites a wide variety of data that indicate that over the past fifty years, Americans have become increasingly socially disengaged. In developing this theme, Putnam specifically cites the increase in casino gambling (and especially machine gambling) as evidence in support of his argument. Building on the empirical and theoretical work of Putnam, this exploratory article examines the subphenomenon of gambling alone by exploring sample survey data on solitary and social gambling behavior among adults who reside in Las Vegas, Nevada. Specifically, to further understand these phenomena, a number of demographic, attitudinal, and behavioral variables are examined for their explanatory power in predicting solitary vs. social gambling behavior

Regulation, governance and informality: an empirical analysis of selected countries

The Informal Economy provides employment to more than 60 per cent of the labour population in the developing world despite being a site unfettered by regulations and social norms of fairness governing pay and work conditions. In assessing the factors behind an informal agent’s decision to formalize, it is asserted that rigidity in regulatory mechanism is the primary cause that impedes the process of formalization. However whether flexible regulations can encourage formalization by making gains of formalization more accessible and certain remains a question. In this paper we argue that flexible regulations does not necessarily manifest into the incentives that are essential for formalization. Reducing rigidities in regulation has a significant pay off only in the ambit of good governance. More specifically we hypothesise that degree of intensity of regulation will hardly matter in containing informality; rather what matters is the quality of governance and capability of the institutions to put the regulations into effect. Using secondary data for 46 countries over the period between 1980 and 2008, we empirically investigate into the linkages between governance, regulation and informal employment by developing static and dynamic panel data models and establish that in curbing informality what turns out to be crucial is the interaction between quality of governance and regulation

Mobile travel services: A three-country study into the impact of local circumstances

In this paper we explore the difference in acceptance patterns of mobile services that are related to travelling in three countries: Finland, The Netherlands and New Zealand. The objective of this paper is to understand differences in the use of Mobile Travel Services in three countries that differ with regard to national travel patterns. This paper also contributes to the discussion of the relevance of the Technology Acceptance Model for mobile applications by focusing on the importance of context characteristics, such as the degree of mobility of the user, the social situation people are in, and their need for social interaction. Based on surveys in the three countries as executed in 2009, we use structural equation modelling to find differences in patterns. The paper concludes that context factors have an impact on the relation between the core concepts as used in TAM and DOI approach, and that t here is a clear need for closer research in the moderating effect of physical (e.g. mobile and fixed context) and social context, as well as the need for social interaction. Moreover it is clear that country specific characteristics play a role in the acceptance of mobile travel services. As we pointed out in many of our research projects before the acceptance and use of mobile services requires deep understanding from individual, context and technology related characteristics and their mutual interactions