Issues of partial credit in mathematical assessment by computer

The CALM Project for Computer Aided Learning in Mathematics has operated at Heriot‐Watt University since 1985. From the beginning CALM has featured assessment in its programs (Beevers, Cherry, Foster and McGuire, 1991), and enabled both students and teachers to view progress in formative assessment The computer can play a role in at least four types of assessment: diagnostic, self‐test, continuous and grading assessment. The TLTP project Mathwise employs the computer in three of these roles. In 1994 CALM reported on an educational experiment in which the computer was used for the first time to grade, in part, the learning of a large class of service mathematics students (Beevers, McGuire, Stirling and Wild ,1995), using the Mathwise assessment template. At that time the main issues identified were those of ‘partial credit’ and communication between the student and the computer. These educational points were addressed in the next phase of the CALM Project in which the commercial testing program Interactive PastPapers was developed. The main aim of this paper is to describe how Interactive Past Papers has been able to incorporate some approaches to partial credit which has helped to alleviate student worries on these issues. Background information on other features in Interactive Past Papers is also included to provide context for the discussion

Neurocognitive therapeutics: from concept to application in the treatment of negative attention bias

There is growing interest in the use of neuroimaging for the direct treatment of mental illness. Here, we present a new framework for such treatment, neurocognitive therapeutics. What distinguishes neurocognitive therapeutics from prior approaches is the use of precise brain-decoding techniques within a real-time feedback system, in order to adapt treatment online and tailor feedback to individuals’ needs. We report an initial feasibility study that uses this framework to alter negative attention bias in a small number of patients experiencing significant mood symptoms. The results are consistent with the promise of neurocognitive therapeutics to improve mood symptoms and alter brain networks mediating attentional control. Future work should focus on optimizing the approach, validating its effectiveness, and expanding the scope of targeted disorders

Improving Prediction of Real-Time Loneliness and Companionship Type Using Geosocial Features of Personal Smartphone Data

Loneliness is a widely affecting mental health symptom and can be mediated by and co-vary with patterns of social exposure. Using momentary survey and smartphone sensing data collected from 129 Android-using college student participants over three weeks, we (1) investigate and uncover the relations between momentary loneliness experience and companionship type and (2) propose and validate novel geosocial features of smartphone-based Bluetooth and GPS data for predicting loneliness and companionship type in real time. We base our features on intuitions characterizing the quantity and spatiotemporal predictability of an individual's Bluetooth encounters and GPS location clusters to capture personal significance of social exposure scenarios conditional on their temporal distribution and geographic patterns. We examine our features' statistical correlation with momentary loneliness through regression analyses and evaluate their predictive power using a sliding window prediction procedure. Our features achieved significant performance improvement compared to baseline for predicting both momentary loneliness and companionship type, with the effect stronger for the loneliness prediction task. As such we recommend incorporation and further evaluation of our geosocial features proposed in this study in future mental health sensing and context-aware computing applications.This work was supported by Whole Communities—Whole Health, a research grand challenge at the University of Texas at AustinOffice of the VP for Researc

A pilot randomized controlled trial for a videoconference-delivered mindfulness-based group intervention in a nonclinical setting

Technology is increasingly being integrated into the provision of therapy and mental health interventions. While the evidence base for technology-led delivery of mindfulness-based interventions is growing, one approach to understanding the effects of technology-delivered elements includes so-named blended programs that continue to include aspects of traditional face-to-face interaction. This arrangement offers unique practical advantages, and also enables researchers to isolate variables that may be underlying the effects of technology-delivered interventions. The present study reports on a pilot videoconference-delivered mindfulness-based group intervention offered to university students and staff members with wait-list controls. Apart from the first session of the six-week course, the main facilitator guided evening classes remotely via online videoconferencing, with follow-up exercises via email. Participants Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation were taught a variety of mindfulness-based exercises such as meditation, breathing exercises, mindful tasting, as well as the concepts underpinning such practice. Participants completed pre- and post-intervention questionnaires on depression, anxiety, repetitive negative thinking, dysfunctional attitudes, positive and negative affect, self-compassion, compassion for others, and mindfulness. For participants who attended at least five of the six sessions, scores on all outcome measures improved significantly post intervention and remained stable at three-week follow up. The videoconference-delivered mindfulness-based group intervention appears to provide a viable alternative format to standard mindfulness programs where the facilitator and participants need to live in close physical proximity with each other

Local and regional components of aerosol in a heavily trafficked street canyon in central London derived from PMF and cluster analysis of single-particle ATOFMS spectra.

Positive matrix factorization (PMF) has been applied to single particle ATOFMS spectra collected on a six lane heavily trafficked road in central London (Marylebone Road), which well represents an urban street canyon. PMF analysis successfully extracted 11 factors from mass spectra of about 700,000 particles as a complement to information on particle types (from K-means cluster analysis). The factors were associated with specific sources and represent the contribution of different traffic related components (i.e., lubricating oils, fresh elemental carbon, organonitrogen and aromatic compounds), secondary aerosol locally produced (i.e., nitrate, oxidized organic aerosol and oxidized organonitrogen compounds), urban background together with regional transport (aged elemental carbon and ammonium) and fresh sea spray. An important result from this study is the evidence that rapid chemical processes occur in the street canyon with production of secondary particles from road traffic emissions. These locally generated particles, together with aging processes, dramatically affected aerosol composition producing internally mixed particles. These processes may become important with stagnant air conditions and in countries where gasoline vehicles are predominant and need to be considered when quantifying the impact of traffic emissions.This is the author accepted manuscript. The final version is available via ACS at http://pubs.acs.org/doi/abs/10.1021/es506249z

Spatially resolved flux measurements of NO_X from London suggest significantly higher emissions than predicted by inventories

To date, direct validation of city-wide emissions inventories for air pollutants has been difficult or impossible. However, recent technological innovations now allow direct measurement of pollutant fluxes from cities, for comparison with emissions inventories, which are themselves commonly used for prediction of current and future air quality and to help guide abatement strategies. Fluxes of NOx were measured using the eddy-covariance technique from an aircraft flying at low altitude over London. The highest fluxes were observed over central London, with lower fluxes measured in suburban areas. A footprint model was used to estimate the spatial area from which the measured emissions occurred. This allowed comparison of the flux measurements to the UK's National Atmospheric Emissions Inventory (NAEI) for NOx, with scaling factors used to account for the actual time of day, day of week and month of year of the measurement. The comparison suggests significant underestimation of NOx emissions in London by the NAEI, mainly due to its under-representation of real world road traffic emissions. A comparison was also carried out with an enhanced version of the inventory using real world driving emission factors and road measurement data taken from the London Atmospheric Emissions Inventory (LAEI). The measurement to inventory agreement was substantially improved using the enhanced version, showing the importance of fully accounting for road traffic, which is the dominant NOx emission source in London. In central London there was still an underestimation by the inventory of 30-40% compared with flux measurements, suggesting significant improvements are still required in the NOx emissions inventory.</p

Measurement of NOx fluxes from a tall tower in central London, UK and comparison with emissions inventories

Direct measurements of NOx concentration and flux were made from a tall tower in central London, UK as part of the Clean Air for London (ClearfLo) project. Fast time resolution (10 Hz) NO and NO2 concentrations were measured and combined with fast vertical wind measurements to provide top-down flux estimates using the eddy covariance technique. Measured NOx fluxes were usually positive and ranged from close to zero at night to 2000 - 8000 ng m(-2) s(-1) during the day. Peak fluxes were usually observed in the morning, coincident with the maximum traffic flow. Measurements of the NOx flux have been scaled and compared to the UK National Atmospheric Emissions Inventory (NAEI) estimate of NOx emission for the measurement footprint. The measurements are on average 80 % higher than the NAEI emission inventory for all of London. Observations made in westerly airflow (from parts of London where traffic is a smaller fraction of the NOx source) showed a better agreement on average with the inventory. The observations suggest that the emissions inventory is poorest at estimating NOx when traffic is the dominant source, in this case from an Easterly direction from the BT tower. Agreement between the measurements and the London Atmospheric Emissions Inventory (LAEI) are better, due to the more explicit treatment of traffic flow by this more detailed inventory. The flux observations support previous tailpipe observations of higher NOx emitted from the London vehicle diesel fleet than is represented in the NAEI or predicted for several EURO emission control technologies. Higher than anticipated vehicle NOx is likely responsible for the significant discrepancies that exist in London between observed NOx and its projections.</p

Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms

Cognitive training is a promising intervention for psychological distress; however, its effectiveness has yielded inconsistent outcomes across studies. This research is a pre-registered individual-level meta-analysis to identify factors contributing to cognitive training efficacy for anxiety and depression symptoms. Machine learning methods, alongside traditional statistical approaches, were employed to analyze 22 datasets with 1544 participants who underwent working memory training, attention bias modification, interpretation bias modification, or inhibitory control training. Baseline depression and anxiety symptoms were found to be the most influential factor, with individuals with more severe symptoms showing the greatest improvement. The number of training sessions was also important, with more sessions yielding greater benefits. Cognitive trainings were associated with higher predicted improvement than control conditions, with attention and interpretation bias modification showing the most promise. Despite the limitations of heterogeneous datasets, this investigation highlights the value of large-scale comprehensive analyses in guiding the development of personalized training interventions

Seasonal variation of cerebrovascular diseases

The seasonal variation in all admissions of all types of cerebro-vascular disease within the West Midlands Region was examined between the years 1973–1980. There was a fluctuation for both sexes with a peak in winter, between the months of October and April; a trough was observed in late summer, in July and August. Multivariate analysis of the meteorological factors showed an association between hours of sunshine and intracerebral haemorrhage. The meteorological variables were strongly correlated with each other making the selection of the most predictable variable to stroke difficult.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41646/1/701_2005_Article_BF01400492.pd