The Effect of a Yearlong One-To-One Laptop Computer Classroom Program on the 4th-Grade Achievement and Technology Outcomes of Digital Divide Learners

A yearlong one-to-one computer laptop classroom instruction intervention program used to prepare 4th-grade students for participation in computer learning activities was evaluated. Students used computers to complete daily reading, writing, and Internet search assignments. Students were divided into two groups according to past computer access; Digital Divide Learners ( n = 10) who did not have computers and Internet access at home, and Digital Native Learners (n = 15) who did have computers and Internet access at home. Reading, writing, total technology skills domain scores, and keyboarding speed and accuracy outcomes were evaluated. Results indicate reading vocabulary, reading comprehension, and writing pretest-posttest test score gain for both groups. However, the null hypothesis was rejected only for the Digital Native Learners reading vocabulary pretest-posttest comparison. The null hypothesis was not rejected for any of the reading and writing posttest-posttest comparisons. The null hypothesis was rejected for all pretest-posttest computer learning scores for both groups. Only the keyboarding accuracy posttest-posttest comparison was found to be statistically significantly different in the direction of greater accuracy scores for the Digital Native Learners. Computer competence for all students must begin in our classrooms

The effect of a yearlong one -to -one laptop computer classroom program on the 4th-grade achievement and technology outcomes of Digital Divide Learners

A yearlong one-to-one computer laptop classroom instruction intervention program used to prepare 4th-grade students for participation in computer learning activities was evaluated. Students used computers to complete daily reading, writing, and Internet search assignments. Students were divided into two groups according to past computer access; Digital Divide Learners (n = 10) who did not have computers and Internet access at home, and Digital Native Learners (n = 15) who did have computers and Internet access at home. Reading, writing, total technology skills domain scores, and keyboarding speed and accuracy outcomes were evaluated. Results indicate reading vocabulary, reading comprehension, and writing pretest-posttest test score gain for both groups. However, the null hypothesis was rejected only for the Digital Native Learners reading vocabulary pretest-posttest comparison. The null hypothesis was not rejected for any of the reading and writing posttest-posttest comparisons. The null hypothesis was rejected for all pretest-posttest computer learning scores for both groups. Only the keyboarding accuracy posttest-posttest comparison was found to be statistically significantly different in the direction of greater accuracy scores for the Digital Native Learners. Computer competence for all students must begin in our classrooms

Process evaluation of Derbyshire Intensive Alternatives to Custody Pilot

The aim of this study was to critically assess the implementation and development of the Intensive Alternatives to Custody (IAC) pilot in Derbyshire. The Ministry of Justice (MoJ) Penal Policy paper (May 2007) outlined the government’s intention to develop higher intensity community orders as an alternative to short-term custody. The IAC Order was subsequently developed and piloted, first in Derbyshire and then in six other areas.* The pilots were centrally funded until March 2011

On the meaning of uncertainty for ethical AI: philosophy and practice

Whether and how data scientists, statisticians and modellers should be accountable for the AI systems they develop remains a controversial and highly debated topic, especially given the complexity of AI systems and the difficulties in comparing and synthesising competing claims arising from their deployment for data analysis. This paper proposes to address this issue by decreasing the opacity and heightening the accountability of decision making using AI systems, through the explicit acknowledgement of the statistical foundations that underpin their development and the ways in which these dictate how their results should be interpreted and acted upon by users. In turn, this enhances (1) the responsiveness of the models to feedback, (2) the quality and meaning of uncertainty on their outputs and (3) their transparency to evaluation. To exemplify this approach, we extend Posterior Belief Assessment to offer a route to belief ownership from complex and competing AI structures. We argue that this is a significant way to bring ethical considerations into mathematical reasoning, and to implement ethical AI in statistical practice. We demonstrate these ideas within the context of competing models used to advise the UK government on the spread of the Omicron variant of COVID-19 during December 2021.Comment: 26 pages, 2 figure

Microcanonical entropy inflection points: Key to systematic understanding of transitions in finite systems

We introduce a systematic classification method for the analogs of phase transitions in finite systems. This completely general analysis, which is applicable to any physical system and extends towards the thermodynamic limit, is based on the microcanonical entropy and its energetic derivative, the inverse caloric temperature. Inflection points of this quantity signal cooperative activity and thus serve as distinct indicators of transitions. We demonstrate the power of this method through application to the long-standing problem of liquid-solid transitions in elastic, flexible homopolymers.Comment: 4 pages, 3 figure

GPU-accelerated 3D visualisation and analysis of migratory behaviour of long lived birds

With the amount of data we collect increasing, due to the efficacy of tagging technology improving, the methods we previously applied have begun to take longer and longer to process. As we move forward, it is important that the methods we develop also evolve with the data we collect. Maritime visualisation has already begun to leverage the power of parallel processing to accelerate visualisation. However, some of these techniques require the use of distributed computing, that while useful for datasets that contain billions of points, is harder to implement due to hardware requirements. Here we show that movement ecology can also significantly benefit from the use of parallel processing, while using GPGPU acceleration to enable the use of a single workstation. With only minor adjustments, algorithms can be implemented in parallel, enabling for computation to be completed in real time. We show this by first implementing a GPGPU accelerated visualisation of global environmental datasets. Through the use of OpenGL and CUDA, it is possible to visualise a dataset containing over 25 million datapoints per timestamp and swap between timestamps in 5ms, allowing for environmental context to be considered when visualising trajectories in real time. These can then be used alongside different GPU accelerated visualisation methods, such as aggregate flow diagrams, to explore large datasets in real time. We also continue to apply GPGPU acceleration to the analysis of migratory data through the use of parallel primitives. With these parallel primitives we show that GPGPU acceleration can allow researchers to accelerate their workflow without the need to completely understand the complexities of GPU programming, allowing for orders of magnitude faster computation times when compared to sequential CPU methods

Autonomous Airliners Anytime Soon?

This research seeks to extend the body of knowledge on factors influential in the decision to fly on an autonomous airliner as a passenger. Only a handful of studies have probed this direct question in the last 16 years, but the data is showing a growing public acceptance of this type of travel. Pivotal in this consideration is the basic element of trust – trust in automated airliners and trust in the airline and Air Traffic Control systems which are responsible for autonomous airliners. Human trust has many forms and manifestations, but in the end, it is a dichotomous or binary choice; either a human does or does not trust. Longitudinally comparing the previous autonomous airliner research samples was technically impure because the respondent pools were dissimilar in age demographics, vocational backgrounds, and nationality. Nevertheless, a current, United States-focused sampling was taken to compare with the 16-year historical data available and explore trends in this emerging discussion