Key Skills: making connections between HE and the workplace

This paper draws on a recent research project into high-level key skills links between HE and employment. The project has worked with groups in several universities and companies to explore how the developmental model embedded in the QCA key skills national standards can be used to support learning and assessment of higher level (QCA levels 4 and 5) key skills. Employers increasingly value skills such as teamworking, communicating effectively with partners and customers, and being able to adapt to new situations and develop new capabilities. Within organisations individuals may be expected to move from project to project and job to job. They may be expected to identify their own particular training needs, work within the company business goals and develop their own individual skills portfolio to satisfy professional recognition requirements. HE currently appears to offer relatively little support or training to develop the key skills needed in such environments. As part of the project students in HE have used a framework of planning, monitoring progress, presenting outcomes and reviewing progress to develop their skills. The model encourages learners to recognise and articulate their own capabilities more clearly, and offers an assessment structure for profiling achievement. It is this 'meta-skills' approach that is used to bridge the gap between HE and employment by encouraging learners to be actively aware of the context in which they are currently situated, and to make connections with experience, skills and knowledge they have gained elsewhere. The paper presents some preliminary findings and comments from the project

Learning outcomes and their assessment: putting Open University pedagogical practice under the microscope

The Open University (OU) is the United Kingdom's only university devoted to distance learning. It is also the UK's largest university with over 200,000 students overall. Around 150,000 students are studying undergraduate level courses. Over the last decade major policy changes have impacted on UK higher education. Following the recommendations of the National Committee of Inquiry into Higher Education (Dearing Report, 1997) and the establishment of the Quality Assurance Agency, all UK universities have been required to define learning outcomes for their programmes and link learning outcomes to teaching and assessment. This major pedagogic shift led the OU to establish the Learning Outcomes and their Assessment (LOTA) project to re-examine the ways its courses are planned, designed, delivered and assessed, and to initiate necessary institution-wide changes. Explicitly linking outcomes, assessment and teaching, actively using assessment for learning, and supporting academic staff development are key elements in enhancing student learning

Doctor of Philosophy

dissertationFor magnetic resonance-guided focused ultrasound (MRgFUS) treatments to be broadly accepted, progress must be made in treatment planning, monitoring, and control. A key component to this goal is accurate modeling of the bioheat transfer equation (BHTE). This dissertation develops new methods for identifying the significant parameters of the BHTE: the ultrasonic specific absorption rate (SAR), the tissue thermal diffusivity, and perfusion-related energy losses. SAR is determined by fitting an analytical solution one-dimensional radial Gaussian heating) to MRgFUS temperature data in simulations and a tissue-mimicking phantom. This new method is compared with linear and exponential methods for different fitting times, beam sizes, perfusion, and thermal diffusivity values. The analytical method is consistently most reliable and is accurate to within 10% for all cases, except high perfusion. An extension to the analytical solution improves SAR estimates for high perfusion cases. MRgFUS sampling characteristics (spatial averaging, temporal sampling, and noise) for SAR and thermal diffusivity estimation are parametrically evaluated against several focused ultrasound beam sizes. For single point heatings, a maximum voxel size of 1x1x3 mm is recommended for temperature and estimate errors to remain less than 10%. Two MRgFUS thermal diffusivity estimation methods are evaluated against a standard technique in ex vivo porcine and in vivo rabbit back muscle. Both methods accurately estimate thermal diffusivity using cooling data (overall ex vivo error < 6%, in vivo < 12%). Including heating data in the Gaussian SAR method further reduces errors (ex vivo error < 2%, in vivo < 3%). The Gaussian SAR method has better precision than the Gaussian temperature method. Two methods for quantifying perfusion-related energy losses using MRgFUS cooling temperatures are developed (experimental + modeled data vs. experimental data). The methods are verified via simulations and experiments in ex vivo perfused porcine kidney at different flow rates. The difference techniques employed make these methods susceptible to noise errors, but this feasibility study demonstrates promise for their use in future work. In conclusion, these methods can be used to validate biothermal models, and associated improvements in thermal modeling have the potential to increase the efficacy and safety of MRgFUS therapies

Convolutional Drift Networks for Video Classification

Analyzing spatio-temporal data like video is a challenging task that requires processing visual and temporal information effectively. Convolutional Neural Networks have shown promise as baseline fixed feature extractors through transfer learning, a technique that helps minimize the training cost on visual information. Temporal information is often handled using hand-crafted features or Recurrent Neural Networks, but this can be overly specific or prohibitively complex. Building a fully trainable system that can efficiently analyze spatio-temporal data without hand-crafted features or complex training is an open challenge. We present a new neural network architecture to address this challenge, the Convolutional Drift Network (CDN). Our CDN architecture combines the visual feature extraction power of deep Convolutional Neural Networks with the intrinsically efficient temporal processing provided by Reservoir Computing. In this introductory paper on the CDN, we provide a very simple baseline implementation tested on two egocentric (first-person) video activity datasets.We achieve video-level activity classification results on-par with state-of-the art methods. Notably, performance on this complex spatio-temporal task was produced by only training a single feed-forward layer in the CDN.Comment: Published in IEEE Rebooting Computin

Application of Pyroprinting for Source Tracking of E. coli in Pennington Creek

The goal of this project is to determine the source of fecal contamination in Pennington Creek. Dangerously high numbers of E. coli were recorded in the creek by the Morro Bay National Estuary Program (MBNEP) over the last 10 years. This is of particular concern because the creek runs directly through an outdoor school for K-12 education. One potential source of the contamination is the cattle in fields on either side of the creek for several miles, though they are supposedly kept out by fences. Another possibility is local wildlife—specifically wild turkeys, which roam around in flocks of over 100 and commonly roost directly over the creek. The Cal Poly Pyroprinting Project, housed in the Chevron Center for Applications in Biotechnology, is building a comprehensive database of genetic fingerprints (pyroprints) of E. coli from local wildlife and domestic animals for use in bacterial source tracking. Strain-specific pyroprints of E. coli isolates from the water body of interest are matched against wildlife pyroprints in the database. The two are matched up in a way similar to the matching of fingerprints, hence the term “pyroprints”. We will collect E. coli from two locations along Pennington Creek (labeled CPN and PEN) over a 16-month period, with additional bimonthly monitoring of overall E. coli counts. The two sites, along with an extended sampling period, will provide a good idea of the spatiotemporal fluctuations in concentration and sources of E. coli in the creek. The MBNEP has offered to help with the project by providing half of the supplies and reagents. The results of this project will be ultimately used by Cal Poly and the MBNEP to guide management decisions regarding Pennington Creek

Localized Fast Radio Bursts Are Consistent with Magnetar Progenitors Formed in Core-collapse Supernovae

With the localization of fast radio bursts (FRBs) to galaxies similar to the Milky Way and the detection of a bright radio burst from SGR J1935+2154 with energy comparable to extragalactic radio bursts, a magnetar origin for FRBs is evident. By studying the environments of FRBs, evidence for magnetar formation mechanisms not observed in the Milky Way may become apparent. In this Letter, we use a sample of FRB host galaxies and a complete sample of core-collapse supernova (CCSN) hosts to determine whether FRB progenitors are consistent with a population of magnetars born in CCSNe. We also compare the FRB hosts to the hosts of hydrogen-poor superluminous supernovae (SLSNe-I) and long gamma-ray bursts (LGRBs) to determine whether the population of FRB hosts is compatible with a population of transients that may be connected to millisecond magnetars. After using a novel approach to scale the stellar masses and star formation rates of each host galaxy to be statistically representative of z = 0 galaxies, we find that the CCSN hosts and FRBs are consistent with arising from the same distribution. Furthermore, the FRB host distribution is inconsistent with the distribution of SLSNe-I and LGRB hosts. With the current sample of FRB host galaxies, our analysis shows that FRBs are consistent with a population of magnetars born through the collapse of giant, highly magnetic stars

Localized FRBs are Consistent with Magnetar Progenitors Formed in Core-Collapse Supernovae

With the localization of fast radio bursts (FRBs) to galaxies similar to the Milky Way and the detection of a bright radio burst from SGR J1935+2154 with energy comparable to extragalactic radio bursts, a magnetar origin for FRBs is evident. By studying the environments of FRBs, evidence for magnetar formation mechanisms not observed in the Milky Way may become apparent. In this paper, we use a sample of FRB host galaxies and a complete sample of core-collapse supernova (CCSN) hosts to determine whether FRB progenitors are consistent with a population of magnetars born in CCSNe. We also compare the FRB hosts to the hosts of hydrogen-poor superluminous supernovae (SLSNe-I) and long gamma-ray bursts (LGRBs) to determine whether the population of FRB hosts is compatible with a population of transients that may be connected to millisecond magnetars. After using a novel approach to scale the stellar masses and star-formation rates of each host galaxy to be statistically representative of z=0 galaxies, we find that the CCSN hosts and FRBs are consistent with arising from the same distribution. Furthermore, the FRB host distribution is inconsistent with the distribution of SLSNe-I and LGRB hosts. With the current sample of FRB host galaxies, our analysis shows that FRBs are consistent with a population of magnetars born through the collapse of giant, highly magnetic stars