Harnessing Technology: new modes of technology-enhanced learning: opportunities and challenges

A report commissioned by Becta to explore the potential impact on education, staff and learners of new modes of technology enhanced learning, envisaged as becoming available in subsequent years. A generative framework, developed by the researchers is described, which was used as an analytical tool to relate the possibilities of the technology described to learning and teaching activities. This report is part of the curriculum and pedagogy strand of Becta's programme of managed research in support of the development of Harnessing Technology: Next Generation Learning 2008-14. A system-wide strategy for technology in education and skills. Between April 2008 and March 2009, the project carried out research, in three iterative phases, into the future of learning with technology. The research has drawn from, and aims to inform, all UK education sectors

Virulence of malaria is associated with differential expression of Plasmodium falciparum var gene subgroups in a case-control study

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) is a major pathogenicity factor in falciparum malaria that mediates cytoadherence. PfEMP1 is encoded by approximately 60 var genes per haploid genome. Most var genes are grouped into 3 subgroups: A, B, and C. Evidence is emerging that the specific expression of these subgroups has clinical significance. Using field samples from children from Papua New Guinea with severe, mild, and asymptomatic malaria, we compared proportions of transcripts of var groups, as determined by quantitative polymerase chain reaction. We found a significantly higher proportion of var group B transcripts in children with clinical malaria (mild and severe), whereas a large proportion of var group C transcripts was found in asymptomatic children. These data from naturally infected children clearly show that major differences exist in var gene expression between parasites causing clinical disease and those causing asymptomatic infections. Furthermore, parasites forming rosettes showed a significant up-regulation of var group A transcripts

The Tiered Radio Extragalactic Continuum Simulation (T-RECS)

We present the Tiered Radio Extragalactic Continuum Simulation (T-RECS): a new simulation of the radio sky in continuum, over the 150 MHz-20 GHz range. T-RECS models two main populations of radio galaxies: Active Galactic Nuclei (AGNs) and Star-Forming Galaxies (SFGs), and corresponding sub-populations. Our model also includes polarized emission over the full frequency range, which has been characterised statistically for each population using the available information. We model the clustering properties in terms of probability distributions of hosting halo masses, and use lightcones extracted from a high-resolution cosmological simulation to determine the positions of haloes. This limits the sky area for the simulations including clustering to a 25deg2 field of view. We compare luminosity functions, number counts in total intensity and polarization, and clustering properties of our outputs to up-to-date compilations of data and find a very good agreement. We deliver a set of simulated catalogues, as well as the code to produce them, which can be used for simulating observations and predicting results from deep radio surveys with existing and forthcoming radio facilities, such as the Square Kilometre Array (SKA).Comment: 20 pages, 11 figures, accepted by MNRA

Evaluating the Task Generalization of Temporal Convolutional Networks for Surgical Gesture and Motion Recognition using Kinematic Data

Fine-grained activity recognition enables explainable analysis of procedures for skill assessment, autonomy, and error detection in robot-assisted surgery. However, existing recognition models suffer from the limited availability of annotated datasets with both kinematic and video data and an inability to generalize to unseen subjects and tasks. Kinematic data from the surgical robot is particularly critical for safety monitoring and autonomy, as it is unaffected by common camera issues such as occlusions and lens contamination. We leverage an aggregated dataset of six dry-lab surgical tasks from a total of 28 subjects to train activity recognition models at the gesture and motion primitive (MP) levels and for separate robotic arms using only kinematic data. The models are evaluated using the LOUO (Leave-One-User-Out) and our proposed LOTO (Leave-One-Task-Out) cross validation methods to assess their ability to generalize to unseen users and tasks respectively. Gesture recognition models achieve higher accuracies and edit scores than MP recognition models. But, using MPs enables the training of models that can generalize better to unseen tasks. Also, higher MP recognition accuracy can be achieved by training separate models for the left and right robot arms. For task-generalization, MP recognition models perform best if trained on similar tasks and/or tasks from the same dataset.Comment: 8 pages, 4 figures, 6 tables. To be published in IEEE Robotics and Automation Letters (RA-L

Incorporating genetic selection into individual‐based models of malaria and other infectious diseases

Introduction Control strategies for human infections are often investigated using individual‐based models (IBMs) to quantify their impact in terms of mortality, morbidity and impact on transmission. Genetic selection can be incorporated into the IBMs to track the spread of mutations whose origin and spread are driven by the intervention and which subsequently undermine the control strategy; typical examples are mutations which encode drug resistance or diagnosis‐ or vaccine‐escape phenotypes. Methods and results We simulated the spread of malaria drug resistance using the IBM OpenMalaria to investigate how the finite sizes of IBMs require strategies to optimally incorporate genetic selection. We make four recommendations. Firstly, calculate and report the selection coefficients, s, of the advantageous allele as the key genetic parameter. Secondly, use these values of “s” to calculate the wait time until a mutation successfully establishes itself in the pathogen population. Thirdly, identify the inherent limits of the IBM to robustly estimate small selection coefficients. Fourthly, optimize computational efficacy: when “s” is small, fewer replicates of larger IBMs may be more efficient than a larger number of replicates of smaller size. Discussion The OpenMalaria IBM of malaria was an exemplar and the same principles apply to IBMs of other diseases

Tools for Collecting Speech Corpora via Mechanical-Turk

To rapidly port speech applications to new languages one of the most difficult tasks is the initial collection of sufficient speech corpora. State-of-the-art automatic speech recognition systems are typical trained on hundreds of hours of speech data. While pre-existing corpora do exist for major languages, a sufficient amount of quality speech data is not available for most world languages. While previous works have focused on the collection of translations and the transcription of audio via Mechanical-Turk mechanisms, in this paper we introduce two tools which enable the collection of speech data remotely. We then compare the quality of audio collected from paid part-time staff and unsupervised volunteers, and determine that basic user training is critical to obtain usable data

COMPASS: A Formal Framework and Aggregate Dataset for Generalized Surgical Procedure Modeling

Purpose: We propose a formal framework for the modeling and segmentation of minimally-invasive surgical tasks using a unified set of motion primitives (MPs) to enable more objective labeling and the aggregation of different datasets. Methods: We model dry-lab surgical tasks as finite state machines, representing how the execution of MPs as the basic surgical actions results in the change of surgical context, which characterizes the physical interactions among tools and objects in the surgical environment. We develop methods for labeling surgical context based on video data and for automatic translation of context to MP labels. We then use our framework to create the COntext and Motion Primitive Aggregate Surgical Set (COMPASS), including six dry-lab surgical tasks from three publicly-available datasets (JIGSAWS, DESK, and ROSMA), with kinematic and video data and context and MP labels. Results: Our context labeling method achieves near-perfect agreement between consensus labels from crowd-sourcing and expert surgeons. Segmentation of tasks to MPs results in the creation of the COMPASS dataset that nearly triples the amount of data for modeling and analysis and enables the generation of separate transcripts for the left and right tools. Conclusion: The proposed framework results in high quality labeling of surgical data based on context and fine-grained MPs. Modeling surgical tasks with MPs enables the aggregation of different datasets and the separate analysis of left and right hands for bimanual coordination assessment. Our formal framework and aggregate dataset can support the development of explainable and multi-granularity models for improved surgical process analysis, skill assessment, error detection, and autonomy.Comment: 22 pages, 6 figures, 12 table