Can trainees' perceptions of the delivery of the foundation subjects and R.E. in primary teacher education be enhanced by using a Three-Lens model of delivery?

This paper reports on the findings of a small-scale research project, carried out by a team of teacher educators working in the Primary Foundation Subjects at a university in the North West of England. The team worked together to develop the Three-Lens Model, a teaching model that aimed both to provide a unification of subject delivery across the module whilst splitting subject delivery into three clear areas: subject-“ness” (subject identity), how to teach (pedagogy) and ideas of what to teach (toolbox). The paper presents a discussion of the perceived effectiveness of the Three-Lens Model. It gathers data from trainees across a variety of cohorts and programmes within the School of Education who were exposed to the model of delivery for one year and specifically questions how successful it was in providing a cohesive delivery structure and in raising the trainees’ perceptions of the pedagogical element of their training. The findings suggest that this was a largely successful delivery model which could potentially benefit other training providers

Robust Singular Smoothers For Tracking Using Low-Fidelity Data

Tracking underwater autonomous platforms is often difficult because of noisy, biased, and discretized input data. Classic filters and smoothers based on standard assumptions of Gaussian white noise break down when presented with any of these challenges. Robust models (such as the Huber loss) and constraints (e.g. maximum velocity) are used to attenuate these issues. Here, we consider robust smoothing with singular covariance, which covers bias and correlated noise, as well as many specific model types, such as those used in navigation. In particular, we show how to combine singular covariance models with robust losses and state-space constraints in a unified framework that can handle very low-fidelity data. A noisy, biased, and discretized navigation dataset from a submerged, low-cost inertial measurement unit (IMU) package, with ultra short baseline (USBL) data for ground truth, provides an opportunity to stress-test the proposed framework with promising results. We show how robust modeling elements improve our ability to analyze the data, and present batch processing results for 10 minutes of data with three different frequencies of available USBL position fixes (gaps of 30 seconds, 1 minute, and 2 minutes). The results suggest that the framework can be extended to real-time tracking using robust windowed estimation.Comment: 9 pages, 9 figures, to be included in Robotics: Science and Systems 201

Facilitating a Collaborative Approach to E-Learning for Program Planners, Instructor Training and Technology Leadership

Significant differences in expertise and attitudes towards e-learning technology are not uncommon between those who design the program and maintain the technology (program planners and technicians) and those who use the resources and technology (instructors and trainers). This wide disconnect between the two groups will often result in frustration and a poor quality product. Models of e-learning training and collaboration will be illustrated in three settings: higher education, secondary education, and the corporate level

Digitizing and Disclosing Personal Data: The Proliferation of State Criminal Records on the Internet

Digitization and the release of public records on the Internet have expanded the reach and uses of criminal record data in the United States. This study analyzes the types and volume of personally identifiable data released on the Internet via two hundred public governmental websites for law enforcement, criminal courts, corrections, and criminal record repositories in each state. We find that public disclosures often include information valuable to the personal data economy, including the full name, birthdate, home address, and physical characteristics of arrestees, detainees, and defendants. Using administrative data, we also estimate the volume of data disclosed online. Our findings highlight the mass dissemination of pre-conviction data: every year, over ten million arrests, 4.5 million mug shots, and 14.7 million criminal court proceedings are digitally released at no cost. Post-conviction, approximately 6.5 million current and former prisoners and 12.5 million people with a felony conviction have a record on the Internet. While justified through public records laws, such broad disclosures reveal an imbalance between the “transparency” of data releases that facilitate monitoring of state action and those that facilitate monitoring individual people. The results show how the criminal legal system increasingly distributes Internet privacy violations and community surveillance as part of contemporary punishment